NormalMatt/nl2sql

1{"prompt":"Give the product ID and name of the product with the highest prices among the quantity ranges from 400 to 500.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the product ID and name of the product with the highest prices among the quantity ranges from 400 to 500.\n        \n        ### Context:\n        #### Query Context:\n        highest prices refers to MAX(Price); quantity ranges from 400 to 500 refers to Quantity BETWEEN 400 AND 500;\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ProductID, T1.Name FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T2.quantity BETWEEN 400 AND 500 ORDER BY T1.Price DESC LIMIT 1"}],"db_id":"sales"}2{"prompt":"Give me the name and menu price of dishes that were free.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give me the name and menu price of dishes that were free.\n        \n        ### Context:\n        #### Query Context:\n        dishes that were free refers to lowest_price = 0;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.name, T1.price FROM MenuItem AS T1 INNER JOIN Dish AS T2 ON T2.id = T1.dish_id WHERE T2.lowest_price = 0"}],"db_id":"menu"}3{"prompt":"List the names of the first andthe second teams that played a match with the point of the winning margin lower than the 30% of the average winning margin of the matches held in 2011.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the names of the first andthe second teams that played a match with the point of the winning margin lower than the 30% of the average winning margin of the matches held in 2011.\n        \n        ### Context:\n        #### Query Context:\n        name of the team refers to Team_Name; in 2011 refers to Match_Date LIKE '2011%'; the point of winning margin lower than 30% of the average winning margin refers to Win_Margin < multiply(avg(Win_Margin), 0.3) where Match_Date LIKE '2011%'\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT CASE WHEN T1.Win_Margin < ( SELECT AVG(Win_Margin) * 0.3 FROM Match WHERE Match_Date LIKE '2011%' ) THEN T2.Team_Name END, CASE WHEN T1.Win_Margin < ( SELECT AVG(Win_Margin) * 0.3 FROM Match WHERE Match_Date LIKE '2011%' ) THEN T3.Team_Name END FROM Match AS T1 INNER JOIN Team AS T2 ON T2.Team_Id = T1.Team_1 INNER JOIN Team AS T3 ON T3.Team_Id = T1.Team_2 WHERE T1.Match_Date LIKE '2011%'"}],"db_id":"soccer_2016"}4{"prompt":"What is the task of method number 2?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the task of method number 2?\n        \n        ### Context:\n        #### Query Context:\n        task of the method refers to the second part of the Name after the \".\"; method number refers to Method_100k.Id; Method_100k.Id = 2;\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUBSTR(SUBSTR(Name, INSTR(Name, '.') + 1), 1, INSTR(SUBSTR(Name, INSTR(Name, '.') + 1), '.') - 1) task FROM Method WHERE Id = 2"}],"db_id":"codebase_comments"}5{"prompt":"Which is the majority of the ethnic group in country with great than 10,000,000 population","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which is the majority of the ethnic group in country with great than 10,000,000 population\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name FROM country AS T1 INNER JOIN ethnicGroup AS T2 ON T1.Code = T2.Country WHERE T1.Population > 10000000 GROUP BY T2.Name, T2.Percentage ORDER BY T2.Percentage DESC LIMIT 2"}],"db_id":"mondial_geo"}6{"prompt":"What is the percentage of Primetime Emmy nominated episodes with a rating over 7 to all the episodes that have a rating over 7?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of Primetime Emmy nominated episodes with a rating over 7 to all the episodes that have a rating over 7?\n        \n        ### Context:\n        #### Query Context:\n        \"Primetime Emmy' is the award_category;\u00a0 rating over 7 refers to rating > 7; nominated refers to result = 'Nominee'; percentage = Divide(Count(episode_id(award_category = 'Primetime Emmy')), Count (episode_id)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.award_category = 'Primetime Emmy' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM Award AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T2.rating > 7 AND T1.result = 'Nominee';"}],"db_id":"simpson_episodes"}7{"prompt":"How many clients who live in Kansas City provided a 1-star review?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many clients who live in Kansas City provided a 1-star review?\n        \n        ### Context:\n        #### Query Context:\n        1-star review refers stars = 1;\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.Stars) FROM reviews AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.city = 'Kansas City' AND T1.Stars = 1"}],"db_id":"retail_complains"}8{"prompt":"In the Venus and Adonis, what is the description of the last scene listed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In the Venus and Adonis, what is the description of the last scene listed?\n        \n        ### Context:\n        #### Query Context:\n        Venus and Adonis refers to Title = 'Venus and Adonis'; last scene refers to max(Scene)\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Description FROM works AS T1 RIGHT JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Venus and Adonis' ORDER BY T2.Scene DESC LIMIT 1"}],"db_id":"shakespeare"}9{"prompt":"Among undergraduate students, list the name of the course with the highest student satisfaction.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among undergraduate students, list the name of the course with the highest student satisfaction.\n        \n        ### Context:\n        #### Query Context:\n        Undergraduate students refers to type = 'UG'; satisfaction refers to sat; highest satisfaction refers to MAX(sat);\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.name FROM student AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T1.type = 'UG' ORDER BY T2.sat DESC LIMIT 1"}],"db_id":"cs_semester"}10{"prompt":"Calculate the service of GDP for Brazil.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the service of GDP for Brazil.\n        \n        ### Context:\n        #### Query Context:\n        The service of GDP can be computed by service * GDP\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Service * T2.GDP FROM country AS T1 INNER JOIN economy AS T2 ON T1.Code = T2.Country WHERE T1.Name = 'Brazil'"}],"db_id":"mondial_geo"}11{"prompt":"How many suppliers for the part \"hot spring dodger dim light\" are in Vietnam?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many suppliers for the part \"hot spring dodger dim light\" are in Vietnam?\n        \n        ### Context:\n        #### Query Context:\n        part \"hot spring dodger dim light\" refers to p_name = 'hot spring dodger dim light'; Vietnam refers to n_name = 'VIETNAM'\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T3.s_name) FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey INNER JOIN supplier AS T3 ON T2.ps_suppkey = T3.s_suppkey INNER JOIN nation AS T4 ON T3.s_nationkey = T4.n_nationkey WHERE T1.p_name = 'hot spring dodger dim light' AND T4.n_name = 'VIETNAM'"}],"db_id":"retails"}12{"prompt":"Among the films that the customer RUTH MARTINEZ has rented, what is the title of the one with the highest replacement cost?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the films that the customer RUTH MARTINEZ has rented, what is the title of the one with the highest replacement cost?\n        \n        ### Context:\n        #### Query Context:\n        highest replacement cost refers to Max(replacement_cost)\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T4.title FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id INNER JOIN inventory AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN film AS T4 ON T3.film_id = T4.film_id WHERE T1.first_name = 'RUTH' AND T1.last_name = 'MARTINEZ' ORDER BY T4.replacement_cost DESC LIMIT 1"}],"db_id":"movie_3"}13{"prompt":"Which country have conducted population census from 2010 until 2012 and have completed vital registration?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country have conducted population census from 2010 until 2012 and have completed vital registration?\n        \n        ### Context:\n        #### Query Context:\n        from 2010 until 2012 refers to LatestPopulationCensus between '2010' and '2012'; have completed vital registration refers to VitalRegistrationComplete = 'Yes'; CountryName = ShortName + LongName\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT ShortName, LongName FROM Country WHERE LatestPopulationCensus >= 2010 AND LatestPopulationCensus < 2013 AND VitalRegistrationComplete = 'Yes'"}],"db_id":"world_development_indicators"}14{"prompt":"What are the ratings of the film featuring behind the scenes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the ratings of the film featuring behind the scenes?\n        \n        ### Context:\n        #### Query Context:\n        film featuring behind the scenes refers to special_features = 'Behind the Scenes'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT rating FROM film WHERE special_features LIKE '%Behind the Scenes%'"}],"db_id":"movie_3"}15{"prompt":"What is the average donation amount to a project created by a teacher working in a school in Brooklyn?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average donation amount to a project created by a teacher working in a school in Brooklyn?\n        \n        ### Context:\n        #### Query Context:\n        school in Brooklyn refers to school_city = 'Brooklyn'; Average = AVG(donation_total);\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.donation_total) \/ COUNT(donationid) FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.school_city = 'Brooklyn'"}],"db_id":"donor"}16{"prompt":"How many papers were published by the \"Virtual Reality, IEEE Annual International Symposium\" conference in 2012?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many papers were published by the \"Virtual Reality, IEEE Annual International Symposium\" conference in 2012?\n        \n        ### Context:\n        #### Query Context:\n        'Virtual Reality, IEEE Annual International Symposium' is the FullName of conference; in 2012 refers to Year = 2012;\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.Id) FROM Conference AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.ConferenceId WHERE T1.FullName = 'Virtual Reality, IEEE Annual International Symposium' AND T2.Year = 2012"}],"db_id":"authors"}17{"prompt":"Please list the departments that are part of the Executive General and Administration group.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the departments that are part of the Executive General and Administration group.\n        \n        ### Context:\n        #### Query Context:\n        Department refers to Name where GroupName = 'Executive General and Administration'\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 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[26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 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[50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 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'integer', 'integer', 'integer', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'datetime', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Name FROM Department WHERE GroupName = 'Executive General and Administration'"}],"db_id":"works_cycles"}18{"prompt":"Which team has the most victories as the home team in matches of the Bundesliga division?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which team has the most victories as the home team in matches of the Bundesliga division?\n        \n        ### Context:\n        #### Query Context:\n        Bundesliga is the name of division; the most victories as the home team refers to MAX(COUNT(FTR = 'H'));\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.HomeTeam FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.name = 'Bundesliga' AND T1.FTR = 'H' GROUP BY T1.HomeTeam ORDER BY COUNT(T1.FTR) DESC LIMIT 1"}],"db_id":"european_football_1"}19{"prompt":"Which journal was the paper \"Education, democracy and growth\" published on? Give the full name of the journal.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which journal was the paper \"Education, democracy and growth\" published on? Give the full name of the journal.\n        \n        ### Context:\n        #### Query Context:\n        'Education, democracy and growth' is the title of a paper\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.FullName FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T2.Title = 'Education, democracy and growth'"}],"db_id":"authors"}20{"prompt":"Please list the IDs of all the shipments made by a retailer customer.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the IDs of all the shipments made by a retailer customer.\n        \n        ### Context:\n        #### Query Context:\n        \"retailer\" is the cust_type;\u00a0 IDs of shipments refers to ship_id\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ship_id FROM customer AS T1 INNER JOIN shipment AS T2 ON T1.cust_id = T2.cust_id WHERE T1.cust_type = 'retailer'"}],"db_id":"shipping"}21{"prompt":"Among businesses with \"Wi-Fi\" attribute, which businesses id are located at SC State?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among businesses with \"Wi-Fi\" attribute, which businesses id are located at SC State?\n        \n        ### Context:\n        #### Query Context:\n        \"Wi-Fi\" attribute refers to attribute_name = 'Wi-Fi' AND attribute_value = 'true'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.business_id FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T1.attribute_name = 'Wi-Fi' AND T2.attribute_value = 'true' AND T3.state = 'SC'"}],"db_id":"public_review_platform"}22{"prompt":"How many chapters are there in \"Midsummer Night's Dream\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many chapters are there in \"Midsummer Night's Dream\"?\n        \n        ### Context:\n        #### Query Context:\n        in \"Midsummer Night's Dream\" refers to Title = 'Midsummer Night's Dream'\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.id) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Midsummer Night''s Dream'"}],"db_id":"shakespeare"}23{"prompt":"What are the x and y coordinates of all the images with a prediction relationship class id of 98?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the x and y coordinates of all the images with a prediction relationship class id of 98?\n        \n        ### Context:\n        #### Query Context:\n        prediction relationship class id of 98 refers to PRED_CLASS_ID = 98\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.X, T2.Y FROM IMG_REL AS T1 INNER JOIN IMG_OBJ AS T2 ON T1.IMG_ID = T2.IMG_ID WHERE T1.PRED_CLASS_ID = 98"}],"db_id":"image_and_language"}24{"prompt":"How many users belong to the same behavior category as comics?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many users belong to the same behavior category as comics?\n        \n        ### Context:\n        #### Query Context:\n        behavior category refers to category; category = 'comics';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.app_id) FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id WHERE T1.category = 'comics'"}],"db_id":"talkingdata"}25{"prompt":"The ward represented by which alderman had more incidents in January, 2018, Pat Dowell or Sophia King?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        The ward represented by which alderman had more incidents in January, 2018, Pat Dowell or Sophia King?\n        \n        ### Context:\n        #### Query Context:\n        January, 2018 refers to Substr(date, 1, 1) = '1' AND Substr(date, 5, 4) = '2018'; had more incidents refers to Max(Count(ward_no))\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.alderman_first_name, T1.alderman_last_name, COUNT(T1.ward_no) AS num FROM Ward AS T1 INNER JOIN Crime AS T2 ON T1.ward_no = T2.ward_no WHERE (SUBSTR(T2.date, 1, 1) = '1' AND SUBSTR(T2.date, 5, 4) = '2018' AND T1.alderman_first_name = 'Pat' AND T1.alderman_last_name = 'Dowell') OR (T1.alderman_first_name = 'Sophia' AND T1.alderman_last_name = 'King') GROUP BY T1.ward_no"}],"db_id":"chicago_crime"}26{"prompt":"Give the criteria name where Harvard University scored 100.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the criteria name where Harvard University scored 100.\n        \n        ### Context:\n        #### Query Context:\n        Harvard University refers to university_name = 'Harvard University'; scored 100 refers to score = 100\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.criteria_name FROM university AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.university_id INNER JOIN ranking_criteria AS T3 ON T3.id = T2.ranking_criteria_id WHERE T1.university_name = 'Harvard University' AND T2.score = 100"}],"db_id":"university"}27{"prompt":"What is the id of the repository with the highest number of solution path?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the id of the repository with the highest number of solution path?\n        \n        ### Context:\n        #### Query Context:\n        highest number of solution path refers to max(count(Path)); id of the repository refers to RepoId\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT RepoId FROM solution GROUP BY RepoId ORDER BY COUNT(Path) DESC LIMIT 1"}],"db_id":"codebase_comments"}28{"prompt":"Please list the titles of the papers published in the journal \"Concepts in Magnetic Resonance Part A\" in 2008.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the titles of the papers published in the journal \"Concepts in Magnetic Resonance Part A\" in 2008.\n        \n        ### Context:\n        #### Query Context:\n        journal \"Concepts in Magnetic Resonance Part A\" refers to FullName = 'Concepts in Magnetic Resonance Part A'; in 2018 refers to Year = 2018\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Title FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T1.FullName = 'Concepts in Magnetic Resonance Part A' AND T2.Year = 2008"}],"db_id":"authors"}29{"prompt":"Among the books ordered by Lucas Wyldbore, how many of them are over 300 pages?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the books ordered by Lucas Wyldbore, how many of them are over 300 pages?\n        \n        ### Context:\n        #### Query Context:\n        books have over 300 pages refers to num_pages > 300\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id INNER JOIN cust_order AS T3 ON T3.order_id = T2.order_id INNER JOIN customer AS T4 ON T4.customer_id = T3.customer_id WHERE T4.first_name = 'Lucas' AND T4.last_name = 'Wyldbore' AND T1.num_pages > 300"}],"db_id":"books"}30{"prompt":"Please list the depth of the lakes that are located in the Province of Albania.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the depth of the lakes that are located in the Province of Albania.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Depth FROM located AS T1 INNER JOIN lake AS T2 ON T1.Lake = T2.Name WHERE T1.Province = 'Albania'"}],"db_id":"mondial_geo"}31{"prompt":"How many votes did the episode titled \"Cherished\" get in total?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many votes did the episode titled \"Cherished\" get in total?\n        \n        ### Context:\n        #### Query Context:\n        titled \"Cherished\" refers to title = 'Cherished'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.votes) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Cherished'"}],"db_id":"law_episode"}32{"prompt":"Give me the full birthdate, email and phone number of the youngest client in Indianapolis .","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give me the full birthdate, email and phone number of the youngest client in Indianapolis .\n        \n        ### Context:\n        #### Query Context:\n        full birthdate = year, month, day; youngest refers to max(year, month, day); in Indianapolis refers to city = 'Indianapolis'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.year, T1.month, T1.day, T1.email, T1.phone FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.city = 'Indianapolis' ORDER BY T1.year DESC, T1.month DESC, T1.day DESC LIMIT 1"}],"db_id":"retail_complains"}33{"prompt":"How many games did player Id \"vanbijo01\" win in the 1990 season?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many games did player Id \"vanbijo01\" win in the 1990 season?\n        \n        ### Context:\n        #### Query Context:\n        the number of wins refers to W\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], 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'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], 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'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT W FROM Goalies WHERE playerID = 'vanbijo01' AND year = 1990"}],"db_id":"hockey"}34{"prompt":"What is the geographic ID and total income per year when the average income is above 3300 dollar.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the geographic ID and total income per year when the average income is above 3300 dollar.\n        \n        ### Context:\n        #### Query Context:\n        total income per year refers to MULTIPLY(12, INHABITANTS_K, INCOME_K) where INCOME_K > 3300; geographic ID refers to GEOID;\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT GEOID, INHABITANTS_K * INCOME_K * 12 FROM Demog WHERE INCOME_K > 3300"}],"db_id":"software_company"}35{"prompt":"List the person IDs of the second film editors in Movie No. 12.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the person IDs of the second film editors in Movie No. 12.\n        \n        ### Context:\n        #### Query Context:\n        second film editors refers to job = 'Second Film Editor'; Movie No. 12 refers to movie_id = 12\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT person_id FROM movie_crew WHERE movie_id = 12 AND job = 'Second Film Editor'"}],"db_id":"movies_4"}36{"prompt":"List the locations ids, compartments and containers for the Lock Ring","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the locations ids, compartments and containers for the Lock Ring\n        \n        ### Context:\n        #### Query Context:\n        compartment refers to Shelf; container refers to Bin; Lock Ring is a name of product\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, 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'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.Venue_Name = 'M Chinnaswamy Stadium' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.Venue_Name = 'Maharashtra Cricket Association Stadium' THEN 1 ELSE 0 END) FROM `Match` AS T1 INNER JOIN Venue AS T2 ON T1.Venue_Id = T2.Venue_Id"}],"db_id":"soccer_2016"}38{"prompt":"How many Minipumps have been sold?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Minipumps have been sold?\n        \n        ### Context:\n        #### Query Context:\n        Minipump is name of a product\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 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[26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 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'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'date', 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'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'datetime', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(OrderQty) FROM SalesOrderDetail WHERE ProductID IN ( SELECT ProductID FROM Product WHERE Name = 'Minipump' )"}],"db_id":"works_cycles"}39{"prompt":"What are the total quantities of the items ordered by customer 101660 on 10\/5\/1995?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the total quantities of the items ordered by customer 101660 on 10\/5\/1995?\n        \n        ### Context:\n        #### Query Context:\n        total quantity refers to sum(l_quantity); customer 101660 refers to o_custkey = 101660; on 10\/5\/1995 refers to o_orderdate = '1995-10-05'\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.l_quantity) FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T1.o_orderdate = '1995-10-05' AND T1.o_custkey = 101660"}],"db_id":"retails"}40{"prompt":"How many businesses operating in the \"Accessories\" category have received a \"wonderful experience\" review from users?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many businesses operating in the \"Accessories\" category have received a \"wonderful experience\" review from users?\n        \n        ### Context:\n        #### Query Context:\n        Accessories category refers to category_name = 'Accessories'; wonderful experience review refers to stars > 3;\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.business_id) FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T3.stars > 3 AND T1.category_name LIKE 'Accessories'"}],"db_id":"public_review_platform"}41{"prompt":"Give the match's venue and winning team for the match ID 392194.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the match's venue and winning team for the match ID 392194.\n        \n        ### Context:\n        #### Query Context:\n        venue refers to Venue_Name; winning team refers to match_winner\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Venue_Name, T3.Team_Name FROM Venue AS T1 INNER JOIN Match AS T2 ON T1.venue_id = T2.venue_id INNER JOIN Team AS T3 ON T2.match_winner = T3.Team_Id WHERE T2.Match_Id = 392194"}],"db_id":"soccer_2016"}42{"prompt":"How many movie lists with over 100 movies had user 85981819 created when he or she was a paying subscriber?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many movie lists with over 100 movies had user 85981819 created when he or she was a paying subscriber?\n        \n        ### Context:\n        #### Query Context:\n        the user was a paying subscriber when he created the list refers to user_has_payment_method = 1;\u00a0 movie lists with over 100 refers to list_movie_number >100;\u00a0 user 85981819 refers to user_id = 85981819;\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM lists AS T1 INNER JOIN lists_users AS T2 ON T1.list_id = T2.list_id AND T1.user_id = T2.user_id WHERE T1.user_id = 85981819 AND T1.list_movie_number > 100 AND T2.user_has_payment_method = 1"}],"db_id":"movie_platform"}43{"prompt":"How did Kyran Muller submit his complaint?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How did Kyran Muller submit his complaint?\n        \n        ### Context:\n        #### Query Context:\n        how it was submitted refers to \"Submitted via\";\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.`Submitted via` FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.first = 'Kyran' AND T1.last = 'Muller'"}],"db_id":"retail_complains"}44{"prompt":"List down all of the product names that were placed by sales person with id 10.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down all of the product names that were placed by sales person with id 10.\n        \n        ### Context:\n        #### Query Context:\n        id refers to SalesPersonID; SalesPersonID = 10\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.Name FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T2.SalesPersonID = 10"}],"db_id":"sales"}45{"prompt":"Provide a list of titles together with its publisher name for all publishers located in the USA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide a list of titles together with its publisher name for all publishers located in the USA.\n        \n        ### Context:\n        #### Query Context:\n        publisher name refers to pub_name;\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title, T2.pub_name FROM titles AS T1 INNER JOIN publishers AS T2 ON T1.pub_id = T2.pub_id WHERE T2.country = 'USA'"}],"db_id":"book_publishing_company"}46{"prompt":"What is the profit for part no.98768 in order no.1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the profit for part no.98768 in order no.1?\n        \n        ### Context:\n        #### Query Context:\n        part no.98768 refers to l_partkey = 98768; order no.1 refers to l_orderkey = 1; profit = subtract(multiply(l_extendedprice, subtract(1, l_discount)), multiply(ps_supplycost, l_quantity))\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.l_extendedprice * (1 - T1.l_discount) - T2.ps_supplycost * T1.l_quantity FROM lineitem AS T1 INNER JOIN partsupp AS T2 ON T1.l_suppkey = T2.ps_suppkey WHERE T1.l_orderkey = 1 AND T1.l_partkey = 98768"}],"db_id":"retails"}47{"prompt":"Between Deering and Near West districts, which district reported the most number of crime incidents that happened in a library?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Between Deering and Near West districts, which district reported the most number of crime incidents that happened in a library?\n        \n        ### Context:\n        #### Query Context:\n        \"Deering\" and \"Near West\" are both district_name; 'LIBRARY' is the location_description; district with the most number of crime Max(Count(district_no))\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.district_name FROM District AS T1 INNER JOIN Crime AS T2 ON T1.district_no = T2.district_no WHERE T1.district_name IN ('Deering', 'Near West') AND T2.location_description = 'LIBRARY' GROUP BY T1.district_name ORDER BY COUNT(T2.district_no) DESC LIMIT 1"}],"db_id":"chicago_crime"}48{"prompt":"Among the active male employees, how many of them are paid with the highest frequency?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the active male employees, how many of them are paid with the highest frequency?\n        \n        ### Context:\n        #### Query Context:\n        active status of employees refers to CurrentFlag = 1; Male refers to Gender = 'M'; highest frequency refers to PayFrequency = 2;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 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are a powerful text-to-SQL model. 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You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students are enlisted in the Peace Corps organization are enrolled in UCSD school?\n        \n        ### Context:\n        #### Query Context:\n        enlisted in the Peace Corps refers to organ = 'peace_corps'; enrolled in UCSD school refers to school = 'ucsd';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.name) FROM enlist AS T1 INNER JOIN enrolled AS T2 ON T1.name = T2.name WHERE T1.organ = 'peace_corps' AND T2.school = 'ucsd'"}],"db_id":"student_loan"}50{"prompt":"Calculate the percentage of the weight of goods being transported by Zachery Hicks to California in year 2016.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the percentage of the weight of goods being transported by Zachery Hicks to California in year 2016.\n        \n        ### Context:\n        #### Query Context:\n        \"California\" is the state; in 2016 refers to CAST (ship_date AS DATE) = 2016; percentage = Divide (Sum(weight where first_name = 'Zachery' AND last_name = 'Hicks'), Sum(weight)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.first_name = 'Zachery' AND T2.last_name = 'Hicks' THEN T1.weight ELSE 0 END) AS REAL) * 100 \/ SUM(T1.weight) FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id WHERE STRFTIME('%Y', T1.ship_date) = '2016'"}],"db_id":"shipping"}51{"prompt":"Please list the official full names of all the current legislators who do not have an account on C-SPAN's video website.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the official full names of all the current legislators who do not have an account on C-SPAN's video website.\n        \n        ### Context:\n        #### Query Context:\n        legislators who do not have an account refers to cspan_id IS NULL OR cspan_id = ''\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT official_full_name FROM current WHERE cspan_id IS NULL OR cspan_id = ''"}],"db_id":"legislator"}52{"prompt":"List the full name of all current female senators.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the full name of all current female senators.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to official_full_name; female refers to gender_bio = 'F'; senators refers to type = 'sen'\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.first_name, T2.last_name FROM `current-terms` AS T1 INNER JOIN current AS T2 ON T2.bioguide_id = T1.bioguide WHERE T1.type = 'sen' AND T2.gender_bio = 'F' GROUP BY T2.ballotpedia_id"}],"db_id":"legislator"}53{"prompt":"What is the height and weight for coaches who have won awards in 1930?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the height and weight for coaches who have won awards in 1930?\n        \n        ### Context:\n        #### Query Context:\n        year = 1930;\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 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T1.height, T1.weight FROM Master AS T1 INNER JOIN AwardsCoaches AS T2 ON T1.coachID = T2.coachID WHERE T2.year = '1930'"}],"db_id":"hockey"}54{"prompt":"What job is person number 322 currently holding?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What job is person number 322 currently holding?\n        \n        ### Context:\n        #### Query Context:\n        person number 322 refers to PersonID = 18; job is the name of contacttype\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 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89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM ContactType AS T1 INNER JOIN BusinessEntityContact AS T2 ON T1.ContactTypeID = T2.ContactTypeID WHERE T2.BusinessEntityID = 332"}],"db_id":"works_cycles"}55{"prompt":"What is the average number of papers published in the World Computer Congress each year?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average number of papers published in the World Computer Congress each year?\n        \n        ### Context:\n        #### Query Context:\n        published in the World Computer Congress refers to FullName = 'World Computer Congress'; average refers to DIVIDE(COUNT(FullName = 'World Computer Congress'), COUNT(Id))\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.FullName = 'International Congress Series' THEN 1 ELSE 0 END) AS REAL) \/ COUNT(T1.Id) AS Div1, T1.Year FROM Paper AS T1 INNER JOIN Journal AS T2 ON T1.JournalId = T2.Id GROUP BY T1.YEAR HAVING Div1 != 0"}],"db_id":"authors"}56{"prompt":"How many users answered the question No.20?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many users answered the question No.20?\n        \n        ### Context:\n        #### Query Context:\n        question No.20 refers to QuestionID = 20\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(UserID) - MIN(UserID) + 1 FROM Answer WHERE QuestionID = 20"}],"db_id":"mental_health_survey"}57{"prompt":"Provide a list of all organisations with headquarters in London?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide a list of all organisations with headquarters in London?\n        \n        ### Context:\n        #### Query Context:\n        London is a city\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Name FROM organization WHERE City = 'London'"}],"db_id":"mondial_geo"}58{"prompt":"What are the products that are supplied by Aux joyeux ecclsiastiques?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the products that are supplied by Aux joyeux ecclsiastiques?\n        \n        ### Context:\n        #### Query Context:\n        Aux joyeux ecclsiastiques is the name of supply company; products refer to ProductName;\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ProductName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T2.CompanyName = 'Aux joyeux ecclsiastiques'"}],"db_id":"retail_world"}59{"prompt":"How many unemployed students have never been absent?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many unemployed students have never been absent?\n        \n        ### Context:\n        #### Query Context:\n        never been absent refers to month = 0;\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.name) FROM longest_absense_from_school AS T1 INNER JOIN unemployed AS T2 ON T2.name = T1.name WHERE T1.month = 0"}],"db_id":"student_loan"}60{"prompt":"Calculate the average number of complaints received from New Bedford each year which are closed with explanation.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average number of complaints received from New Bedford each year which are closed with explanation.\n        \n        ### Context:\n        #### Query Context:\n        average = AVG(\"Complaint ID\"); New Bedford refers to city = 'New Bedford'; closed with explanation refers to Company response to consumer = 'Closed with explanation';\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT STRFTIME('%Y', T3.`Date received`) , CAST(SUM(CASE WHEN T3.`Company response to consumer` = 'Closed with explanation' THEN 1 ELSE 0 END) AS REAL) \/ COUNT(T3.`Complaint ID`) AS average FROM callcenterlogs AS T1 INNER JOIN client AS T2 ON T1.`rand client` = T2.client_id INNER JOIN events AS T3 ON T1.`Complaint ID` = T3.`Complaint ID` WHERE T2.city = 'New Bedford' GROUP BY strftime('%Y', T3.`Date received`)"}],"db_id":"retail_complains"}61{"prompt":"How many books did A.R. Braunmuller write?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many books did A.R. Braunmuller write?\n        \n        ### Context:\n        #### Query Context:\n        \"A.R. Braunmuller\" is the author_name\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM author AS T1 INNER JOIN book_author AS T2 ON T1.author_id = T2.author_id WHERE T1.author_name = 'A.R. Braunmuller'"}],"db_id":"books"}62{"prompt":"Calculate the ratio in percentage between the average number of female and male students who joined Fire Department organization.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the ratio in percentage between the average number of female and male students who joined Fire Department organization.\n        \n        ### Context:\n        #### Query Context:\n        ratio = CONCAT(MULTIPLY(DIVIDE(COUNT(enlist.name WHERE organ = 'fire_department' which is NOT in male.name), COUNT(enlist.name WHERE organ = 'fire_department),'%'))) AS FEMALE; ratio = CONCAT(MULTIPLY(DIVIDE(COUNT(enlist.name WHERE organ = 'fire_department' which is IN male.name), COUNT(enlist.name WHERE organ = 'fire_department), 100))) AS MALE; female students refers to enlist.name who are NOT in male.name; male students refers to enlist.name who are IN male.name; organization refers to organ; organ = 'fire_department';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T2.name IS NULL, 1, 0)) AS REAL) * 100 \/ COUNT(T1.name), CAST(SUM(IIF(T2.name IS NOT NULL, 1, 0)) AS REAL) * 100 \/ COUNT(T1.name) FROM enlist AS T1 LEFT JOIN male AS T2 ON T2.name = T1.name WHERE T1.organ = 'fire_department'"}],"db_id":"student_loan"}63{"prompt":"What is the full name of the customer who gave a 5-star rating and commented \"The quintessential dessert root beer. No ice cream required\" on his review?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of the customer who gave a 5-star rating and commented \"The quintessential dessert root beer. No ice cream required\" on his review?\n        \n        ### Context:\n        #### Query Context:\n        full name = First, Last; 5-star rating refers to StarRating = 5; commented \"The quintessential dessert root beer. No ice cream required\" refers to Review = 'The quintessential dessert root beer. No ice cream required.';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.First, T1.Last FROM customers AS T1 INNER JOIN rootbeerreview AS T2 ON T1.CustomerID = T2.CustomerID WHERE T2.StarRating = 5 AND T2.Review = 'The quintessential dessert root beer. No ice cream required.'"}],"db_id":"beer_factory"}64{"prompt":"What is the full name of the team that the 'NBA Coach of the Year' 1992 winner coached?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of the team that the 'NBA Coach of the Year' 1992 winner coached?\n        \n        ### Context:\n        #### Query Context:\n        \"NBA Coach of the Year\" is the award; in 1992 refers to year = 1992;\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT name FROM teams AS T1 INNER JOIN coaches AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year INNER JOIN awards_coaches AS T3 ON T2.coachID = T3.coachID AND T2.year = T3.year WHERE T3.year = 1992 AND award = 'NBA Coach of the Year'"}],"db_id":"professional_basketball"}65{"prompt":"How many professors are more popular than Zhou Zhihua?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many professors are more popular than Zhou Zhihua?\n        \n        ### Context:\n        #### Query Context:\n        higher popularity means the professor is more popular;\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(prof_id) FROM prof WHERE popularity > ( SELECT popularity FROM prof WHERE first_name = 'Zhihua' AND last_name = 'Zhou' )"}],"db_id":"cs_semester"}66{"prompt":"Give customer No.106936's region name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give customer No.106936's region name.\n        \n        ### Context:\n        #### Query Context:\n        \"Customer#000000055\" is the name of the customer which refers to c_name; region name refers to r_name;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.r_name FROM nation AS T1 INNER JOIN customer AS T2 ON T1.n_nationkey = T2.c_nationkey INNER JOIN region AS T3 ON T1.n_regionkey = T3.r_regionkey WHERE T2.c_custkey = 106936"}],"db_id":"retails"}67{"prompt":"How many Chinese restaurants are there on 1st st, Livermore?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Chinese restaurants are there on 1st st, Livermore?\n        \n        ### Context:\n        #### Query Context:\n        Chinese restaurant refers to food_type = 'chinese'; 1st st refers to street_name = '1st st'; Livermore refers to city = 'livermore'\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.id_restaurant) FROM generalinfo AS T1 INNER JOIN location AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T1.city = 'livermore' AND T1.food_type = 'chinese' AND T2.street_name = '1st st'"}],"db_id":"restaurant"}68{"prompt":"Among papers that were published in 2005, provide the author name of paper with key words of \"LOAD; IDE; SNP; haplotype; asso- ciation studies\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among papers that were published in 2005, provide the author name of paper with key words of \"LOAD; IDE; SNP; haplotype; asso- ciation studies\".\n        \n        ### Context:\n        #### Query Context:\n        in 2005 refers to Year = '2005'; key words of \"LOAD; IDE; SNP; haplotype; asso- ciation studies\" refers to Keyword = 'LOAD; IDE; SNP; haplotype; asso- ciation studies'\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId WHERE T1.Year = 2005 AND T1.Keyword = 'KEY WORDS: LOAD IDE SNP haplotype asso- ciation studies'"}],"db_id":"authors"}69{"prompt":"Among the cars produced in year 1973, how many of the cars have horsepower less than 100?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the cars produced in year 1973, how many of the cars have horsepower less than 100?\n        \n        ### Context:\n        #### Query Context:\n        produced in year 1973 refers to model_year = 1973; have horsepower less than 100 refers to horsepower < 100\n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM data AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID WHERE T2.model_year = 1973 AND T1.horsepower < 100"}],"db_id":"cars"}70{"prompt":"Please list the first name of the players who are good at both left hands and right hands for goalie and have gotten in the Hall of Fame.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the first name of the players who are good at both left hands and right hands for goalie and have gotten in the Hall of Fame.\n        \n        ### Context:\n        #### Query Context:\n        good at both left hands and right hands for goalie refers to shootCatch IS NULL\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], 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[273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.firstName, T1.lastName FROM Master AS T1 INNER JOIN HOF AS T2 ON T1.hofID = T2.hofID WHERE T1.shootCatch IS NULL"}],"db_id":"hockey"}71{"prompt":"State the nick name of player ID 'aubinje01'. 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'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], [12, 'league ID'], [12, 'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], [13, 'year'], [13, 'stint'], [13, 'team ID'], [13, 'shots'], [13, 'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.nameNick, T3.year, T3.name FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID INNER JOIN Teams AS T3 ON T2.tmID = T3.tmID WHERE T1.playerID = 'aubinje01'"}],"db_id":"hockey"}72{"prompt":"List out all award titles nominated for episode 20.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List out all award titles nominated for episode 20.\n        \n        ### Context:\n        #### Query Context:\n        award title refers to title; nominated refers to result = 'Winner' or result = 'Nominee'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.award FROM Episode AS T1 INNER JOIN Award AS T2 ON T1.episode_id = T2.episode_id WHERE T1.episode = 20 AND T2.result IN ('Winner', 'Nominee')"}],"db_id":"law_episode"}73{"prompt":"List by ID all sales teams that have sold products at a 10% discount in store.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List by ID all sales teams that have sold products at a 10% discount in store.\n        \n        ### Context:\n        #### Query Context:\n        ID refers to _SalesTeamID; 10% discount refers to Discount Applied = 0.1; 'In-Store' is the Sales Channel\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T FROM ( SELECT CASE  WHEN `Discount Applied` = '0.1' AND `Sales Channel` = 'In-Store' THEN _SalesTeamID ELSE NULL END AS T FROM `Sales Orders` ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}74{"prompt":"List all the inactive coins and state the last date of its transaction?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the inactive coins and state the last date of its transaction?\n        \n        ### Context:\n        #### Query Context:\n        the last date refers to max(date); inactive coins refers to status = 'inactive'\n        \n        #### Database Schema:\n        {'db_id': 'coinmarketcap', 'table_names_original': ['coins', 'historical'], 'table_names': ['Coins', 'Historical'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform_id'], [0, 'date_added'], [0, 'date_launched'], [1, 'date'], [1, 'coin_id'], [1, 'cmc_rank'], [1, 'market_cap'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent_change_1h'], [1, 'percent_change_24h'], [1, 'percent_change_7d'], [1, 'circulating_supply'], [1, 'total_supply'], [1, 'max_supply'], [1, 'num_market_pairs']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform id'], [0, 'date added'], [0, 'date lanched'], [1, 'date'], [1, 'coin_id'], [1, 'coinmarketcap rank'], [1, 'Market capitalization'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent change 1h'], [1, 'percent change 24h'], [1, 'percent change 7d'], [1, 'circulating supply'], [1, 'total supply'], [1, 'max supply'], [1, 'number market pairs']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer'], 'primary_keys': [1], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.NAME, MAX(T2.DATE) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.ID = T2.coin_id WHERE T1.status = 'inactive' ORDER BY T2.DATE DESC LIMIT 1"}],"db_id":"coinmarketcap"}75{"prompt":"What is the name and length of rivers located at 'Orleans' city?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name and length of rivers located at 'Orleans' city?\n        \n        ### Context:\n        #### Query Context:\n        Orleans is a city in north-central France\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.Name, T3.Length FROM city AS T1 INNER JOIN located AS T2 ON T1.Name = T2.City INNER JOIN river AS T3 ON T3.Name = T2.River WHERE T1.Name = 'Orleans'"}],"db_id":"mondial_geo"}76{"prompt":"How many furniture products were ordered at central superstore?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many furniture products were ordered at central superstore?\n        \n        ### Context:\n        #### Query Context:\n        furniture products refers to Category = 'Furniture'\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM central_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T2.Category = 'Furniture'"}],"db_id":"superstore"}77{"prompt":"Indicate the courtesy title of the 3 employees who have the lowest salary.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Indicate the courtesy title of the 3 employees who have the lowest salary.\n        \n        ### Context:\n        #### Query Context:\n        courtesy title refers to TitleOfCourtesy; the lowest salary refers to MIN(Salary);\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT TitleOfCourtesy FROM Employees ORDER BY Salary LIMIT 3"}],"db_id":"retail_world"}78{"prompt":"How many people gave the most enjoyed episode a 10-star rating?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many people gave the most enjoyed episode a 10-star rating?\n        \n        ### Context:\n        #### Query Context:\n        the most enjoyed refers max(rating); 10-star refers to stars = 10\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.episode_id) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T2.stars = 10"}],"db_id":"law_episode"}79{"prompt":"Tell the language of the movie \"C'era una volta il West\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tell the language of the movie \"C'era una volta il West\".\n        \n        ### Context:\n        #### Query Context:\n        language refers to language_name; movie \"C'era una volta il West\" refers to title = 'C''era una volta il West'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.language_name FROM movie AS T1 INNER JOIN movie_languages AS T2 ON T1.movie_id = T2.movie_id INNER JOIN language AS T3 ON T2.language_id = T3.language_id WHERE T1.title LIKE 'C%era una volta il West'"}],"db_id":"movies_4"}80{"prompt":"Name the teams played in a match which resulted in a tie in 2015.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the teams played in a match which resulted in a tie in 2015.\n        \n        ### Context:\n        #### Query Context:\n        resulted in a tie refers to Win_Type = 'Tie'; in 2015 refers to SUBSTR(Match_Date, 1, 4) = 2015\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Team_Name FROM Team AS T1 INNER JOIN Match AS T2 ON T1.Team_Id = T2.Team_1 OR T1.Team_Id = T2.Team_2 INNER JOIN Win_By AS T3 ON T2.Win_Type = T3.Win_Id WHERE SUBSTR(T2.Match_Date, 1, 4) = '2015' AND T3.Win_Type = 'Tie' LIMIT 1"}],"db_id":"soccer_2016"}81{"prompt":"How many disabled students are male?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many disabled students are male?\n        \n        ### Context:\n        #### Query Context:\n        male students are mentioned in male.name;\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.name) FROM male AS T1 INNER JOIN disabled AS T2 ON T1.name = T2.name"}],"db_id":"student_loan"}82{"prompt":"How many authors have written paper \"145 GROWTH HORMONE RECEPTORS AND THE ONSET OF HYPERINSULINEMIA IN THE OBESE ZUCKER RAT: \"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many authors have written paper \"145 GROWTH HORMONE RECEPTORS AND THE ONSET OF HYPERINSULINEMIA IN THE OBESE ZUCKER RAT: \"?\n        \n        ### Context:\n        #### Query Context:\n        paper \"145 GROWTH HORMONE RECEPTORS AND THE ONSET OF HYPERINSULINEMIA IN THE OBESE ZUCKER RAT: \" refers to Title like'%145 GROWTH HORMONE RECEPTORS AND THE ONSET OF HYPERINSULINEMIA IN THE OBESE ZUCKER RAT:%'\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.Name) FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId WHERE T1.Title = '145 GROWTH HORMONE RECEPTORS AND THE ONSET OF HYPERINSULINEMIA IN THE OBESE ZUCKER RAT: '"}],"db_id":"authors"}83{"prompt":"Provide the weight of the shipment to U-haul Center Of N Syracuse on 2016\/9\/21.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the weight of the shipment to U-haul Center Of N Syracuse on 2016\/9\/21.\n        \n        ### Context:\n        #### Query Context:\n        \"U-haul Center Of N Syracuse\" is the cust_name; on 2016\/9\/21 refers to ship_date = '2016\/09\/21'\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.weight FROM shipment AS T1 INNER JOIN customer AS T2 ON T1.cust_id = T2.cust_id WHERE T2.cust_name = 'U-haul Center Of N Syracuse' AND T1.ship_date = '2016-09-21'"}],"db_id":"shipping"}84{"prompt":"How many documentary films are rated PG-13?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many documentary films are rated PG-13?\n        \n        ### Context:\n        #### Query Context:\n        documentary film refers to category.name = 'documentary'; rated PG-13 refers to rating = 'PG-13'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.film_id) FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T2.category_id = T3.category_id WHERE T3.name = 'Documentary' AND T1.rating = 'PG-13'"}],"db_id":"movie_3"}85{"prompt":"How much dollars was a Bitcoin worth on 2013\/4\/28 according to the coin market?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much dollars was a Bitcoin worth on 2013\/4\/28 according to the coin market?\n        \n        ### Context:\n        #### Query Context:\n        worth refers to price; on 2013\/4\/28 refers to date = '2013-04-28'\n        \n        #### Database Schema:\n        {'db_id': 'coinmarketcap', 'table_names_original': ['coins', 'historical'], 'table_names': ['Coins', 'Historical'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform_id'], [0, 'date_added'], [0, 'date_launched'], [1, 'date'], [1, 'coin_id'], [1, 'cmc_rank'], [1, 'market_cap'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent_change_1h'], [1, 'percent_change_24h'], [1, 'percent_change_7d'], [1, 'circulating_supply'], [1, 'total_supply'], [1, 'max_supply'], [1, 'num_market_pairs']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform id'], [0, 'date added'], [0, 'date lanched'], [1, 'date'], [1, 'coin_id'], [1, 'coinmarketcap rank'], [1, 'Market capitalization'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent change 1h'], [1, 'percent change 24h'], [1, 'percent change 7d'], [1, 'circulating supply'], [1, 'total supply'], [1, 'max supply'], [1, 'number market pairs']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer'], 'primary_keys': [1], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.market_cap FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T2.date = '2013-04-28' AND T1.name = 'Bitcoin'"}],"db_id":"coinmarketcap"}86{"prompt":"Find the difference between the average royalty of titles published by US and non US publishers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find the difference between the average royalty of titles published by US and non US publishers?\n        \n        ### Context:\n        #### Query Context:\n        US publisher refers publisher in the US where country = 'USA'; non-US publishers refers publisher not in the US where country! = 'USA'; difference = SUBTRACT(AVG(royalty) where country = 'USA', AVG(royalty) where country! = 'USA'))\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT (CAST(SUM(CASE WHEN T2.country = 'USA' THEN T1.royalty ELSE 0 END) AS REAL) \/ SUM(CASE WHEN T2.country = 'USA' THEN 1 ELSE 0 END)) - (CAST(SUM(CASE WHEN T2.country != 'USA' THEN T1.royalty ELSE 0 END) AS REAL) \/ SUM(CASE WHEN T2.country != 'USA' THEN 1 ELSE 0 END)) FROM titles AS T1 INNER JOIN publishers AS T2 ON T1.pub_id = T2.pub_id INNER JOIN roysched AS T3 ON T1.title_id = T3.title_id"}],"db_id":"book_publishing_company"}87{"prompt":"Identify the percent of long reviews among all 5-star reviews given to businesses by the Yelp users.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the percent of long reviews among all 5-star reviews given to businesses by the Yelp users.\n        \n        ### Context:\n        #### Query Context:\n        percentage = divide(count(business_id where review_length = 'Long' and review_stars = 5), count(business_id)) * 100%; long reviews refers to review_length = 'Long'; 5-star review refers to review_stars = 5\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN review_length = 'Long' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(review_length) FROM Reviews WHERE review_stars = 5"}],"db_id":"public_review_platform"}88{"prompt":"What is the venue name of Bandladore?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the venue name of Bandladore?\n        \n        ### Context:\n        #### Query Context:\n        Bandladore refers to City_Name = 'Bangalore'\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Venue_Name FROM Venue AS T1 INNER JOIN City AS T2 ON T1.City_ID = T2.City_ID WHERE T2.City_Name = 'Bangalore'"}],"db_id":"soccer_2016"}89{"prompt":"How many shooter games are there?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many shooter games are there?\n        \n        ### Context:\n        #### Query Context:\n        shooter games refers to game_name WHERE genre_name = 'shooter';\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.id) FROM game AS T1 INNER JOIN genre AS T2 ON T1.genre_id = T2.id WHERE T2.genre_name = 'Shooter'"}],"db_id":"video_games"}90{"prompt":"Among the customers who have ordered the product \"Telescoping Adjustable Floor Lamp\", how many of them are consumers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the customers who have ordered the product \"Telescoping Adjustable Floor Lamp\", how many of them are consumers?\n        \n        ### Context:\n        #### Query Context:\n        \"Telescoping Adjustable Floor Lamp\" is a \"Product Name\"; consumers refers to Segment = 'Consumer';\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.`Customer Name`) FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T3.`Product Name` = 'Telescoping Adjustable Floor Lamp' AND T1.Segment = 'Consumer'"}],"db_id":"superstore"}91{"prompt":"List the ISBN of the books that cost 7.5 dollars.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the ISBN of the books that cost 7.5 dollars.\n        \n        ### Context:\n        #### Query Context:\n        ISBN refers to isbn13; books cost 7.5 dollars refers to price = 7.5\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.isbn13 FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id WHERE T2.price = 7.5"}],"db_id":"books"}92{"prompt":"What is the delivery time and the clerk of order number 6?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the delivery time and the clerk of order number 6?\n        \n        ### Context:\n        #### Query Context:\n        delivery time = subtract(l_receiptdate, l_commitdate); clerk refers to o_clerk; order number 6 refers to o_orderkey = 6\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT JULIANDAY(T2.l_receiptdate) - JULIANDAY(T2.l_commitdate), T1.o_clerk FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T1.o_orderkey = 6"}],"db_id":"retails"}93{"prompt":"How many countries are there in the No.2 region?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many countries are there in the No.2 region?\n        \n        ### Context:\n        #### Query Context:\n        No.2 region refers to n_regionkey = 2;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(n_nationkey) FROM nation WHERE n_regionkey = 2"}],"db_id":"retails"}94{"prompt":"In 2006, what is the overall number of october defeats for the team with the most October defeats? Indicate the team ID.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In 2006, what is the overall number of october defeats for the team with the most October defeats? Indicate the team ID.\n        \n        ### Context:\n        #### Query Context:\n        team ID refers to tmID; 'defeats' and 'loses' are synonyms; most October defeats refers to max(OctL)\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], 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[16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], 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'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 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'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 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'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT OctL, tmID FROM TeamSplits WHERE year = '2006' ORDER BY OctL DESC LIMIT 1"}],"db_id":"hockey"}95{"prompt":"List down the customer IDs and names that start with alphabet \"W\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the customer IDs and names that start with alphabet \"W\".\n        \n        ### Context:\n        #### Query Context:\n        names that start with alphabet \"W\" refer to Customer Names LIKE 'W%';\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT CustomerID, `Customer Names` FROM Customers WHERE `Customer Names` LIKE 'W%' ORDER BY `Customer Names` DESC"}],"db_id":"regional_sales"}96{"prompt":"List down the area code and country of the city named Savoy.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the area code and country of the city named Savoy.\n        \n        ### Context:\n        #### Query Context:\n        Savoy is the city;\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.area_code, T2.county FROM area_code AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code INNER JOIN zip_data AS T3 ON T1.zip_code = T3.zip_code WHERE T3.city = 'Savoy'"}],"db_id":"address"}97{"prompt":"Who is the Lutheran representative that served in the state of Ohio for 14 years before becoming a senator?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the Lutheran representative that served in the state of Ohio for 14 years before becoming a senator?\n        \n        ### Context:\n        #### Query Context:\n        Lutheran refers to religion_bio = 'Lutheran'; representative refers to type = 'rep'; served for 14 years refers to SUBTRACT(SUM(CAST(strftime('%Y', end)), CAST(strftime('%Y', start)))) = 14;\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN SUM(CAST(strftime('%Y', T2.end) AS int) - CAST(strftime('%Y', T2.start) AS int)) = 14 THEN official_full_name END FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.religion_bio = 'Lutheran' AND T2.state = 'OH' AND T2.type = 'rep'"}],"db_id":"legislator"}98{"prompt":"What is the owner's name of the of the business that violates 103156 on June 12, 2014?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the owner's name of the of the business that violates 103156 on June 12, 2014?\n        \n        ### Context:\n        #### Query Context:\n        business that violates 103156 on June 12, 2014 refers to business_id where violation_type_id = 103156 and date = '2014-06-12';\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.owner_name FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.violation_type_id = 103156 AND T1.`date` = '2014-06-12'"}],"db_id":"food_inspection"}99{"prompt":"Please list the departure airports of the flights on 2018\/8\/1 that were delayed.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the departure airports of the flights on 2018\/8\/1 that were delayed.\n        \n        ### Context:\n        #### Query Context:\n        departure airports refers ORIGIN; on 2018\/8\/1 refers to FL_DATE = '2018\/8\/1'; delayed refers to DEP_DELAY > 0;\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE = '2018\/8\/1' AND T2.DEP_DELAY > 0 GROUP BY T1.Description"}],"db_id":"airline"}100{"prompt":"Which different movies from France got the least ratings?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which different movies from France got the least ratings?\n        \n        ### Context:\n        #### Query Context:\n        France is a country\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.movieid FROM u2base AS T1 INNER JOIN movies AS T2 ON T1.movieid = T2.movieid WHERE T2.country = 'France' AND T1.rating = 1"}],"db_id":"movielens"}101{"prompt":"How many words have repetitions greater than 2000 and lower than 5000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many words have repetitions greater than 2000 and lower than 5000?\n        \n        ### Context:\n        #### Query Context:\n        repetitions greater than 2000 and lower than 5000 refer to occurrences between 2000 and 5000;\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(wid) FROM langs_words WHERE occurrences BETWEEN '2000' AND '5000'"}],"db_id":"language_corpus"}102{"prompt":"List the name of all New Zealand umpires.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the name of all New Zealand umpires.\n        \n        ### Context:\n        #### Query Context:\n        New Zealand umpires refers to Country_Name = 'New Zealand'; name of umpires refers to Umpire_Name\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Umpire_Name FROM Umpire AS T1 INNER JOIN Country AS T2 ON T1.Umpire_Country = T2.Country_Id WHERE T2.Country_Name = 'New Zealand'"}],"db_id":"soccer_2016"}103{"prompt":"What is the age of the oldest active user that participated in the event held on 5\/6\/2016 at coordinates 121, 31?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the age of the oldest active user that participated in the event held on 5\/6\/2016 at coordinates 121, 31?\n        \n        ### Context:\n        #### Query Context:\n        oldest user refers to MAX(age); active user refers to is_active = 1; on 5\/6\/2016 refers to timestamp LIKE '2016-05-06%'; coordinates 121, 31 refers to longitude = 121 AND latitude = 31;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.age FROM app_events AS T1 INNER JOIN events_relevant AS T2 ON T1.event_id = T2.event_id INNER JOIN gender_age AS T3 ON T2.device_id = T3.device_id WHERE T1.is_active = 1 AND T2.longitude = 121 AND T2.latitude = 31 AND SUBSTR(T2.timestamp, 1, 10) = '2016-05-06' ORDER BY T3.age DESC LIMIT 1"}],"db_id":"talkingdata"}104{"prompt":"What is the price of the car ID 15?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the price of the car ID 15?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.price FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID WHERE T1.ID = 15"}],"db_id":"cars"}105{"prompt":"Count the number of postal points in the district represented by Kirkpatrick Ann.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Count the number of postal points in the district represented by Kirkpatrick Ann.\n        \n        ### Context:\n        #### Query Context:\n        postal points refer to zip_code;\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.zip_code) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.first_name = 'Kirkpatrick' AND T1.last_name = 'Ann'"}],"db_id":"address"}106{"prompt":"What is the highest grossing movie without a song?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the highest grossing movie without a song?\n        \n        ### Context:\n        #### Query Context:\n        the highest grossing movie without song refers to movie_title where MAX(total_gross) and song = 'null';\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.movie_title FROM movies_total_gross AS T1 INNER JOIN characters AS T2 ON T2.movie_title = T1.movie_title WHERE T2.song IS NULL ORDER BY CAST(REPLACE(trim(T1.total_gross, '$'), ',', '') AS REAL) DESC LIMIT 1"}],"db_id":"disney"}107{"prompt":"What are the top three universities with the most international students?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the top three universities with the most international students?\n        \n        ### Context:\n        #### Query Context:\n        most international students refers to MAX(SUM(DIVIDE(MULTIPLE(pct_international_students, num_students), 100))); name of university refers to university_name;\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.university_name FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id GROUP BY T2.university_name ORDER BY SUM(T1.num_students * T1.pct_international_students \/ 100) DESC LIMIT 3"}],"db_id":"university"}108{"prompt":"In how many matches in the Seria A division did both teams have equal goals?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In how many matches in the Seria A division did both teams have equal goals?\n        \n        ### Context:\n        #### Query Context:\n        Seria A is a name of division; equal goals refers to FTR = 'D', where D stands for draft;\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.FTR) FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.name = 'Seria A' AND T1.FTR = 'D'"}],"db_id":"european_football_1"}109{"prompt":"What is the thumbnail photo file for the product with the id \"979\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the thumbnail photo file for the product with the id \"979\"?\n        \n        ### Context:\n        #### Query Context:\n        thumbnail photo file refers to ThumbnailPhotoFileName;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 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'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ThumbnailPhotoFileName FROM ProductProductPhoto AS T1 INNER JOIN ProductPhoto AS T2 ON T1.ProductPhotoID = T2.ProductPhotoID WHERE T1.ProductID = 979"}],"db_id":"works_cycles"}110{"prompt":"What are the top 10 products with the highest net profit?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the top 10 products with the highest net profit?\n        \n        ### Context:\n        #### Query Context:\n        products refers to Product Name; highest net profit = Max(Subtract(Unit Price, Unit Cost))\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Product Name` FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID GROUP BY T1._ProductID ORDER BY SUM(REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '')) DESC LIMIT 10"}],"db_id":"regional_sales"}111{"prompt":"What is the greatest length of the border between 2 independent countries?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the greatest length of the border between 2 independent countries?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(T3.Length) FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country INNER JOIN borders AS T3 ON T3.Country1 = T2.Country WHERE T2.Independence IS NOT NULL"}],"db_id":"mondial_geo"}112{"prompt":"Give the gender of the user who made the highest klout tweet on Wednesdays.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the gender of the user who made the highest klout tweet on Wednesdays.\n        \n        ### Context:\n        #### Query Context:\n        highest klout refers to Max(Klout); 'Wednesday' is the Weekday\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Gender FROM twitter AS T1 INNER JOIN user AS T2 ON T1.UserID = T2.UserID WHERE T1.Weekday = 'Wednesday' ORDER BY T1.Klout DESC LIMIT 1"}],"db_id":"social_media"}113{"prompt":"Give the number of \"game-Fishing\" apps.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the number of \"game-Fishing\" apps.\n        \n        ### Context:\n        #### Query Context:\n        game-Fishing refers to category = 'game-Fishing';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.app_id) FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id WHERE T1.category = 'game-Fishing'"}],"db_id":"talkingdata"}114{"prompt":"How many people who are below 30 and participated in the summer season?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many people who are below 30 and participated in the summer season?\n        \n        ### Context:\n        #### Query Context:\n        people who are below 30 refer to person_id where age < 30; the summer season refers to season = 'Summer';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.person_id) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.season = 'Summer' AND T2.age < 30"}],"db_id":"olympics"}115{"prompt":"For all the movies which were produced by Cruel and Unusual Films, which one has the most popularity?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For all the movies which were produced by Cruel and Unusual Films, which one has the most popularity?\n        \n        ### Context:\n        #### Query Context:\n        produced by Cruel and Unusual Films refers to company_name = 'Cruel and Unusual Films'; most popularity refers to max(popularity)\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.title FROM production_company AS T1 INNER JOIN movie_company AS T2 ON T1.company_id = T2.company_id INNER JOIN movie AS T3 ON T2.movie_id = T3.movie_id WHERE T1.company_name = 'Cruel and Unusual Films' ORDER BY T3.popularity DESC LIMIT 1"}],"db_id":"movies_4"}116{"prompt":"What is the credited cast for the episode \"In the Name of the Grandfather\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the credited cast for the episode \"In the Name of the Grandfather\"?\n        \n        ### Context:\n        #### Query Context:\n        credited cast refers to category = 'Cast' and\u00a0 credited = 'true'; episode \"In the Name of the Grandfather\" refers to title = 'In the Name of the Grandfather'\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.person FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'In the Name of the Grandfather' AND T2.category = 'Cast' AND T2.credited = 'true';"}],"db_id":"simpson_episodes"}117{"prompt":"Who has participated in the most Olympic Games in the database?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who has participated in the most Olympic Games in the database?\n        \n        ### Context:\n        #### Query Context:\n        Who refers to full_name; participated in the most Olympic Games refers to MAX(COUNT(games_id));\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.full_name FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id GROUP BY T2.person_id ORDER BY COUNT(T2.person_id) DESC LIMIT 1"}],"db_id":"olympics"}118{"prompt":"List the name of the customer with the most number of order quantity from 2018 to 2020.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the name of the customer with the most number of order quantity from 2018 to 2020.\n        \n        ### Context:\n        #### Query Context:\n        name of customer refers to Customer Names; from 2018 to 2020 refers to OrderDate between '1\/1\/2018' and '31\/12\/2020'; most number of order quantity refers to Order Quantity = 8\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.`Customer Names` FROM Customers AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._CustomerID = T1.CustomerID WHERE T2.OrderDate LIKE '%\/%\/18' OR T2.OrderDate LIKE '%\/%\/19' OR T2.OrderDate LIKE '%\/%\/20' ORDER BY T2.`Order Quantity` DESC LIMIT 1"}],"db_id":"regional_sales"}119{"prompt":"Name the patients who had an allergy to soy.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the patients who had an allergy to soy.\n        \n        ### Context:\n        #### Query Context:\n        allergy to soy refers to allergies where DESCRIPTION = 'Allergy to soya';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first, T1.last FROM patients AS T1 INNER JOIN allergies AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'Allergy to soya'"}],"db_id":"synthea"}120{"prompt":"What are the movie popularity of the movies released in 2021 that were directed by Steven Spielberg? List the names of the movies and their corresponding popularity.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the movie popularity of the movies released in 2021 that were directed by Steven Spielberg? List the names of the movies and their corresponding popularity.\n        \n        ### Context:\n        #### Query Context:\n        movie released in 2021 refers to movie_release_year = 2021; popularity refers to movie_popularity;\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT movie_title, movie_popularity FROM movies WHERE movie_release_year = 2021 AND director_name = 'Steven Spielberg'"}],"db_id":"movie_platform"}121{"prompt":"Calculate the percentage of the winners who are higher than 1.75 meters.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the percentage of the winners who are higher than 1.75 meters.\n        \n        ### Context:\n        #### Query Context:\n        winners refers to result = 'Winner'; higher than 1.75 meters refers to height_meters > 1.75; percentage = divide(sum(result = 'Winner' and height_meters > 1.75), count(result = 'Winner' )) * 100%\n\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.height_meters > 1.75 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM Person AS T1 INNER JOIN Award AS T2 ON T1.name = T2.person WHERE T2.result = 'Winner';"}],"db_id":"simpson_episodes"}122{"prompt":"Among the customers with a marital status of married-civ-spouse, list the number of inhabitants and age of those who are machine-op-inspct.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the customers with a marital status of married-civ-spouse, list the number of inhabitants and age of those who are machine-op-inspct.\n        \n        ### Context:\n        #### Query Context:\n        OCCUPATION = 'Machine-op-inspct'; number of inhabitants refers to INHABITANTS_K;\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.INHABITANTS_K FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.OCCUPATION = 'Farming-fishing' AND T1.SEX = 'Male' AND T1.age >= 20 AND T1.age <= 30"}],"db_id":"software_company"}123{"prompt":"How many inspections were done under the display of inspection report summary category?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many inspections were done under the display of inspection report summary category?\n        \n        ### Context:\n        #### Query Context:\n        under the display of inspection report summary category refers to category = 'Display of Inspection Report Summary'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.inspection_id) FROM inspection_point AS T1 INNER JOIN violation AS T2 ON T1.point_id = T2.point_id WHERE T1.category = 'Display of Inspection Report Summary'"}],"db_id":"food_inspection_2"}124{"prompt":"What is the location coordinates of the bike station from which the bike for the trip that last the longest was borrowed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the location coordinates of the bike station from which the bike for the trip that last the longest was borrowed?\n        \n        ### Context:\n        #### Query Context:\n        location coordinates refers to (lat, long); bike that was borrowed the longest refers to MAX(duration);\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.lat, T2.long FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T1.duration = ( SELECT MAX(T1.duration) FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name )"}],"db_id":"bike_share_1"}125{"prompt":"Please list different IDs of movies that are the newest and have gotten the lowest rating.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list different IDs of movies that are the newest and have gotten the lowest rating.\n        \n        ### Context:\n        #### Query Context:\n        Year contains relative value, higer year value refers to newer date; Year = 4 refers to newest date, Year = 1 refer to oldest date; Lowest rating = 1\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.movieid FROM u2base AS T1 INNER JOIN movies AS T2 ON T1.movieid = T2.movieid WHERE T2.year = 4 AND T1.rating = 1"}],"db_id":"movielens"}126{"prompt":"Indicate the full name of all customers whose last name begins with the letter K.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Indicate the full name of all customers whose last name begins with the letter K.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name; last name begin with the letter 'K' refers to last_name LIKE 'K%'\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT first_name, last_name FROM customer WHERE last_name LIKE 'K%'"}],"db_id":"books"}127{"prompt":"Indicate which is the word that is repeated the most times.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Indicate which is the word that is repeated the most times.\n        \n        ### Context:\n        #### Query Context:\n        repeated the most times refer to MAX(occurrences);\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT word FROM words WHERE occurrences = ( SELECT MAX(occurrences) FROM words )"}],"db_id":"language_corpus"}128{"prompt":"What is the average age of the sales agents in the company by 12\/31\/2009?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average age of the sales agents in the company by 12\/31\/2009?\n        \n        ### Context:\n        #### Query Context:\n        average age as of 12\/31\/2009 = AVG(SUBTRACT(2009, year(BirthDate));\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email 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'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 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'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(2009 - STRFTIME('%Y', T2.BirthDate)) FROM Person AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.PersonType = 'SP'"}],"db_id":"works_cycles"}129{"prompt":"Where is the coordinate (41.66236555, -87.63470194) located? Give the name of the district.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Where is the coordinate (41.66236555, -87.63470194) located? Give the name of the district.\n        \n        ### Context:\n        #### Query Context:\n        coordinate (41.66236555, -87.63470194) refers to latitude = '41.66236555' AND longitude = '-87.63470194'; name of the district refers to district_name\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.district_name FROM Crime AS T1 INNER JOIN District AS T2 ON T1.district_no = T2.district_no WHERE T1.longitude = '-87.63470194' AND T1.latitude = '41.66236555'"}],"db_id":"chicago_crime"}130{"prompt":"What is the IATA code of the Anita Bay Airport in Anita Bay, Alaska?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the IATA code of the Anita Bay Airport in Anita Bay, Alaska?\n        \n        ### Context:\n        #### Query Context:\n        IATA code refers to Code; Anita Bay Airport in Anita Bay, Alaska refers to Description = 'Anita Bay, AK: Anita Bay Airport';\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Code FROM Airports WHERE Description = 'Anita Bay, AK: Anita Bay Airport'"}],"db_id":"airline"}131{"prompt":"List by its ID number all restaurants on 11th Street in Oakland.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List by its ID number all restaurants on 11th Street in Oakland.\n        \n        ### Context:\n        #### Query Context:\n        11th Street refers to street_name = '11th street'; Oakland refers to city = 'oakland'; ID number of restaurant refers to id_restaurant\n\n\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT id_restaurant FROM location WHERE city = 'oakland' AND street_name = '11th street'"}],"db_id":"restaurant"}132{"prompt":"What is the average absence period of a disabled student?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average absence period of a disabled student?\n        \n        ### Context:\n        #### Query Context:\n        average refers to DIVIDE(SUM(month), COUNT(name))\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.month) FROM longest_absense_from_school AS T1 INNER JOIN disabled AS T2 ON T1.name = T2.name"}],"db_id":"student_loan"}133{"prompt":"How severe was case JB296775? Give the index code for severity.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How severe was case JB296775? Give the index code for severity.\n        \n        ### Context:\n        #### Query Context:\n        index code refers to iucr_no; case JB296775 refers to case_number = 'JB296775'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.iucr_no FROM Crime AS T1 INNER JOIN IUCR AS T2 ON T1.iucr_no = T2.iucr_no WHERE T1.case_number = 'JB296775'"}],"db_id":"chicago_crime"}134{"prompt":"What is the average score for \"Chairman Bao\" in all its unscheduled routine inspections?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average score for \"Chairman Bao\" in all its unscheduled routine inspections?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(SUM(score where type = 'Routine - Unscheduled' and name = 'Chairman Bao'), COUNT(type = 'Routine - Unscheduled' where name = 'Chairman Bao'));\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.name = 'Chairman Bao' THEN T1.score ELSE 0 END) AS REAL) \/ COUNT(CASE WHEN T1.type = 'Routine - Unscheduled' THEN T1.score ELSE 0 END) FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id"}],"db_id":"food_inspection"}135{"prompt":"Which sales team name has the least orders in 2019?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which sales team name has the least orders in 2019?\n        \n        ### Context:\n        #### Query Context:\n        sale team names refer to Sales Team; the least orders in 2019 refer to MIN(COUNT(OrderNumber where SUBSTR(OrderDate, -2) = '19'));\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Sales Team` FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T1.OrderDate LIKE '%\/%\/19' GROUP BY T2.`Sales Team` ORDER BY COUNT(T1.OrderNumber) ASC LIMIT 1"}],"db_id":"regional_sales"}136{"prompt":"Calculate the average number of female students who are disabled and who joined Foreign Legion organization.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average number of female students who are disabled and who joined Foreign Legion organization.\n        \n        ### Context:\n        #### Query Context:\n        average = DIVIDE(COUNT(disabled.name who are not in male.name WHERE organ = 'foreign_legion'), COUNT(disabled.name)); female students who are disabled refers to disabled.name who are NOT in male.name; organization refers to organ; organ = 'Foreign Legion';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T3.name IS NULL, 1, 0)) AS REAL) \/ COUNT(T1.name) FROM disabled AS T1 INNER JOIN enlist AS T2 ON T1.name = T2.name LEFT JOIN male AS T3 ON T2.name = T3.name WHERE T2.organ = 'foreign_legion'"}],"db_id":"student_loan"}137{"prompt":"What is the total sick leave hours of employees who do not wish to receive any e-mail promotion?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the total sick leave hours of employees who do not wish to receive any e-mail promotion?\n        \n        ### Context:\n        #### Query Context:\n        Employees who do not wish to receive any e-mail promotions are marked as EmailPromotion = 0\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please give all the list prices of the product LL Fork.\n        \n        ### Context:\n        #### Query Context:\n        Hex Nut 5 is name of a product\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 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date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 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'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ListPrice FROM Product AS T1 INNER JOIN ProductListPriceHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Name = 'LL Fork'"}],"db_id":"works_cycles"}139{"prompt":"Among HTC Butterfly phone users, list any five devices' IDs used by females.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among HTC Butterfly phone users, list any five devices' IDs used by females.\n        \n        ### Context:\n        #### Query Context:\n        HTC Butterfly refers to phone_brand = 'HTC' AND device_model = 'Butterfly'; females refers to gender = 'F';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.device_id FROM phone_brand_device_model2 AS T1 INNER JOIN gender_age AS T2 ON T2.device_id = T1.device_id WHERE T1.device_model = 'Butterfly' AND T2.gender = 'F' AND T1.phone_brand = 'HTC' LIMIT 5"}],"db_id":"talkingdata"}140{"prompt":"How many films have a rental duration of over 6 days?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many films have a rental duration of over 6 days?\n        \n        ### Context:\n        #### Query Context:\n        rental duration of over 6 days refers to rental_duration > 6\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(film_id) FROM film WHERE rental_duration > 6"}],"db_id":"movie_3"}141{"prompt":"List the movies in the Somali language.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the movies in the Somali language.\n        \n        ### Context:\n        #### Query Context:\n        List the movies refers to title; Somali language refers to language_name = 'Somali'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM movie AS T1 INNER JOIN movie_languages AS T2 ON T1.movie_id = T2.movie_id INNER JOIN language AS T3 ON T2.language_id = T3.language_id WHERE T3.language_name = 'Somali'"}],"db_id":"movies_4"}142{"prompt":"How many books are in English?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many books are in English?\n        \n        ### Context:\n        #### Query Context:\n        books in English refers to language_name = 'English'\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM book AS T1 INNER JOIN book_language AS T2 ON T1.language_id = T2.language_id WHERE T2.language_name = 'English'"}],"db_id":"books"}143{"prompt":"How many deserts are there in a country where over 90% of people speaks Armenian?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many deserts are there in a country where over 90% of people speaks Armenian?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.Desert) FROM country AS T1 INNER JOIN geo_desert AS T2 ON T1.Code = T2.Country INNER JOIN language AS T3 ON T1.Code = T2.Country WHERE T3.Name = 'Armenian' AND T3.Percentage > 90"}],"db_id":"mondial_geo"}144{"prompt":"How many cities does congressman Pierluisi Pedro represent?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cities does congressman Pierluisi Pedro represent?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.city) FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code INNER JOIN congress AS T3 ON T2.district = T3.cognress_rep_id WHERE T3.first_name = 'Pierluisi' AND T3.last_name = 'Pedro'"}],"db_id":"address"}145{"prompt":"List the name of all products that Cindy Stewart ordered in the east superstore.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the name of all products that Cindy Stewart ordered in the east superstore.\n        \n        ### Context:\n        #### Query Context:\n        name of all products refers to Product Name; Cindy Stewart is the Customer Name;\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.`Product Name` FROM south_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T1.`Product ID` WHERE T2.`Customer Name` = 'Cindy Stewart'"}],"db_id":"superstore"}146{"prompt":"What is the average number of items shipped each day in April of 1994?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average number of items shipped each day in April of 1994?\n        \n        ### Context:\n        #### Query Context:\n        AVG(l_linenumber) where l_shipdate between '1994-01-01' and '1994-01-30';\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(l_linenumber) FROM lineitem WHERE l_shipdate BETWEEN '1994-01-01' AND '1994-01-30'"}],"db_id":"retails"}147{"prompt":"Give the number of \"drive-thru\" businesses in \"Scottsdale\" with business ID number less than \"1000\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the number of \"drive-thru\" businesses in \"Scottsdale\" with business ID number less than \"1000\".\n        \n        ### Context:\n        #### Query Context:\n        drive-thru refers to attribute_name = 'Drive-Thru' AND attribute_value = 'true'; Scottsdale refers to city = 'Scottsdale'; business_id < 1000;\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.business_id FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T3.business_id < 1000 AND T3.city LIKE 'Scottsdale' AND T1.attribute_name LIKE 'Drive-Thru' AND T2.attribute_value LIKE 'TRUE'"}],"db_id":"public_review_platform"}148{"prompt":"How many students are enlisted in the Navy organization?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students are enlisted in the Navy organization?\n        \n        ### Context:\n        #### Query Context:\n        enlisted in the navy organization refers to organ = 'navy';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(name) FROM enlist WHERE organ = 'navy'"}],"db_id":"student_loan"}149{"prompt":"What percentage of customers from the African region is in the household segment?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of customers from the African region is in the household segment?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(c_custkey where c_mktsegment = 'HOUSEHOLD' and r_name = 'AFRICA'), COUNT(c_custkey where r_name = 'AFRICA')) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T2.r_name = 'AFRICA', 1, 0)) AS REAL) * 100 \/ COUNT(T1.n_nationkey) FROM nation AS T1 INNER JOIN region AS T2 ON T1.n_regionkey = T2.r_regionkey INNER JOIN customer AS T3 ON T1.n_nationkey = T3.c_nationkey WHERE T3.c_mktsegment = 'HOUSEHOLD'"}],"db_id":"retails"}150{"prompt":"Please list the phone numbers of all the suppliers in Germany.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the phone numbers of all the suppliers in Germany.\n        \n        ### Context:\n        #### Query Context:\n        phone number refers to s_phone; Germany refers to n_name = 'Germany'\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.s_phone FROM supplier AS T1 INNER JOIN nation AS T2 ON T1.s_nationkey = T2.n_nationkey WHERE T2.n_name = 'Germany'"}],"db_id":"retails"}151{"prompt":"Among the customers who come from the place with 25746 inhabitants, how many of them are male?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the customers who come from the place with 25746 inhabitants, how many of them are male?\n        \n        ### Context:\n        #### Query Context:\n        place with 44114 inhabitants refers to GEOID where INHABITANTS_K = 44.114; SEX = 'Male';\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.ID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T2.INHABITANTS_K = 25.746 AND T1.SEX = 'Male'"}],"db_id":"software_company"}152{"prompt":"What year in the program do the students with more than 2 advisors are in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What year in the program do the students with more than 2 advisors are in?\n        \n        ### Context:\n        #### Query Context:\n        students refers to student = 1; more than 2 advisors refers to count(p_id_dummy) > 2\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.yearsInProgram FROM advisedBy AS T1 INNER JOIN person AS T2 ON T1.p_id = T2.p_id WHERE T2.student = 1 GROUP BY T2.p_id HAVING COUNT(T2.p_id) > 2"}],"db_id":"computer_student"}153{"prompt":"What types of domestic crimes have occurred the most in the North Lawndale community?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What types of domestic crimes have occurred the most in the North Lawndale community?\n        \n        ### Context:\n        #### Query Context:\n        \"North Lawndale' is the community_area_name; occur the most domestic crime refers to Max(Count(domestic = 'TRUE'))\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.domestic FROM Community_Area AS T1 INNER JOIN Crime AS T2 ON T2.community_area_no = T1.community_area_no WHERE T1.community_area_name = 'North Lawndale' AND T2.domestic = 'TRUE' GROUP BY T2.domestic ORDER BY COUNT(T2.domestic) DESC LIMIT 1"}],"db_id":"chicago_crime"}154{"prompt":"Calculate the percentage salary of employees who handled orders shipped in 1996.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the percentage salary of employees who handled orders shipped in 1996.\n        \n        ### Context:\n        #### Query Context:\n        shipped in 1996 refers to ShippedDate = 1996; percentage salary = divide(sum(Salary when ShippedDate = 1996), sum(Salary)) as percentage\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN STRFTIME('%Y', T2.ShippedDate) = '1996' THEN T1.Salary ELSE 0 END) AS REAL) * 100 \/ SUM(T1.Salary) FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID"}],"db_id":"retail_world"}155{"prompt":"Who is the owner of email address \"JEREMY.HURTADO@sakilacustomer.org\"? Give the full name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the owner of email address \"JEREMY.HURTADO@sakilacustomer.org\"? Give the full name.\n        \n        ### Context:\n        #### Query Context:\n        \"JEREMY.HURTADO@sakilacustomer.org\" is the email; owner refers to customer; full name refers to first_name, last_name\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT first_name, last_name FROM customer WHERE email = 'JEREMY.HURTADO@sakilacustomer.org'"}],"db_id":"movie_3"}156{"prompt":"What is the average height of all the goalies born in the 70s who's a left shooting\/catching dominant and to which team do the tallest player\/s play for most recently?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average height of all the goalies born in the 70s who's a left shooting\/catching dominant and to which team do the tallest player\/s play for most recently?\n        \n        ### Context:\n        #### Query Context:\n        goalies refers to pos = 'G'; born in the 70s refers to birthYear between '1970' and '1979'; left shooting\/catching dominant refers to shootCatch = 'L'; tallest refers to max(height); average height = avg(height(pos = 'G' & birthYear between '1970' and '1979' & shootCatch = 'L')); most recently refers to max(Teams.year)\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], [12, 'league ID'], [12, 'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], [13, 'year'], [13, 'stint'], [13, 'team ID'], [13, 'shots'], [13, 'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T2.height) AS REAL) \/ COUNT(*) FROM AwardsPlayers AS T1 INNER JOIN Master AS T2 ON T1.playerID = T2.playerID WHERE T2.height IS NOT NULL AND (T2.pos = 'LW' OR T2.pos = 'L\/C')"}],"db_id":"hockey"}157{"prompt":"Please list the IDs of the events happened on all the vivo devices.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the IDs of the events happened on all the vivo devices.\n        \n        ### Context:\n        #### Query Context:\n        IDs of the events refers to event_id; vivo devices refers to phone_brand = 'vivo';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.event_id FROM phone_brand_device_model2 AS T1 INNER JOIN events AS T2 ON T2.device_id = T1.device_id WHERE T1.phone_brand = 'vivo'"}],"db_id":"talkingdata"}158{"prompt":"What is the most common allergy among patients?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the most common allergy among patients?\n        \n        ### Context:\n        #### Query Context:\n        the most common allergy refers to MAX(COUNT(DESCRIPTION)) from allergies;\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DESCRIPTION FROM allergies GROUP BY DESCRIPTION ORDER BY COUNT(DESCRIPTION) DESC LIMIT 1"}],"db_id":"synthea"}159{"prompt":"List down the name of venues in season 2.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the name of venues in season 2.\n        \n        ### Context:\n        #### Query Context:\n        name of venues refers to Venue_Name; season 2 refers to Season_Id = 2\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Venue_Name FROM Match AS T1 INNER JOIN Venue AS T2 ON T2.Venue_Id = T1.Venue_Id WHERE T1.Season_Id = 2 GROUP BY T2.Venue_Name"}],"db_id":"soccer_2016"}160{"prompt":"Show the No.3 character name in the credit list of the movie \"G.I. Joe: The Rise of Cobra\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Show the No.3 character name in the credit list of the movie \"G.I. Joe: The Rise of Cobra\".\n        \n        ### Context:\n        #### Query Context:\n        No.3 character refers to creditOrder = '3'; movie \"G.I. Joe: The Rise of Cobra\" refers to Title = 'G.I. Joe: The Rise of Cobra'\n        \n        #### Database Schema:\n        {'db_id': 'movie', 'table_names_original': ['actor', 'movie', 'characters'], 'table_names': ['actor', 'movie', 'characters'], 'column_names_original': [[-1, '*'], [0, 'ActorID'], [0, 'Name'], [0, 'Date of Birth'], [0, 'Birth City'], [0, 'Birth Country'], [0, 'Height (Inches)'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'MovieID'], [1, 'Title'], [1, 'MPAA Rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'Rating Count'], [1, 'Summary'], [2, 'MovieID'], [2, 'ActorID'], [2, 'Character Name'], [2, 'creditOrder'], [2, 'pay'], [2, 'screentime']], 'column_names': [[-1, '*'], [0, 'actor id'], [0, 'Name'], [0, 'date of birth'], [0, 'birth city'], [0, 'birth country'], [0, 'height inches'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'movie id'], [1, 'Title'], [1, 'motion picture association of america rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'rating count'], [1, 'Summary'], [2, 'movie id'], [2, 'actor id'], [2, 'character name'], [2, 'credit order'], [2, 'pay'], [2, 'screentime']], 'column_types': ['text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text'], 'primary_keys': [1, 11, [22, 23]], 'foreign_keys': [[22, 11], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Character Name` FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID WHERE T1.Title = 'G.I. Joe: The Rise of Cobra' AND T2.creditOrder = '3'"}],"db_id":"movie"}161{"prompt":"In drug abuse crimes, what percentage is related to cannabis?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In drug abuse crimes, what percentage is related to cannabis?\n        \n        ### Context:\n        #### Query Context:\n        drug abuse crime refer to title = 'Drug Abuse'; percentage = Divide (Count (secondary_description LIKE '%CANNABIS%'), Count (secondary_description)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T1.secondary_description LIKE '%CANNABIS%' THEN T1.secondary_description END) AS REAL) * 100 \/ COUNT(T1.secondary_description) FROM IUCR AS T1 INNER JOIN Crime AS T2 ON T2.iucr_no = T1.iucr_no INNER JOIN FBI_Code AS T3 ON T3.fbi_code_no = T2.fbi_code_no WHERE T3.title = 'Drug Abuse'"}],"db_id":"chicago_crime"}162{"prompt":"How many trucks were manufactured in year 2009?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many trucks were manufactured in year 2009?\n        \n        ### Context:\n        #### Query Context:\n        manufactured in year 2009 refers to model_year = 2009\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(truck_id) FROM truck WHERE model_year = 2009"}],"db_id":"shipping"}163{"prompt":"What is the percentage of players who were born in Denmark and weight above 154 lbs?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of players who were born in Denmark and weight above 154 lbs?\n        \n        ### Context:\n        #### Query Context:\n        percentage = MULTIPLY(DIVIDE(SUM(weight_in_lbs > 154 and nation = 'Denmark'), COUNT(ELITEID)), 100); players refers to PlayerName; born in Denmark refers to nation = 'Denmark'; weight above 154 lbs refers to weight_in_lbs > 154;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T1.nation = 'Denmark' AND T2.weight_in_lbs > 154 THEN T1.ELITEID ELSE NULL END) AS REAL) * 100 \/ COUNT(T1.ELITEID) FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id"}],"db_id":"ice_hockey_draft"}164{"prompt":"Please list the stores that ordered the book \"Life Without Fear\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the stores that ordered the book \"Life Without Fear\".\n        \n        ### Context:\n        #### Query Context:\n        store name refers to stor_name\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.stor_name FROM sales AS T1 INNER JOIN stores AS T2 ON T1.stor_id = T2.stor_id INNER JOIN titles AS T3 ON T1.title_id = T3.title_id WHERE T3.title = 'Life Without Fear'"}],"db_id":"book_publishing_company"}165{"prompt":"Which country have the least number of users being surveyed? Indicate the name of the country. If there are multiple countries having the same number of users, indicate all of their names.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country have the least number of users being surveyed? Indicate the name of the country. If there are multiple countries having the same number of users, indicate all of their names.\n        \n        ### Context:\n        #### Query Context:\n        Country with least number of users being surveyed refers to MIN(COUNT(AnswerText)) where questionid = 3\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.AnswerText FROM Answer AS T1 INNER JOIN Question AS T2 ON T1.QuestionID = T2.questionid WHERE T2.questionid = 3 GROUP BY T1.AnswerText ORDER BY COUNT(T1.UserID) DESC LIMIT 1"}],"db_id":"mental_health_survey"}166{"prompt":"List restaurant ids located in Danville city.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List restaurant ids located in Danville city.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT id_restaurant FROM location WHERE city = 'Danville'"}],"db_id":"restaurant"}167{"prompt":"What is the third least common genre?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the third least common genre?\n        \n        ### Context:\n        #### Query Context:\n        least common genre refers to min(count(genre_name))\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.genre_name FROM movie_genres AS T1 INNER JOIN genre AS T2 ON T1.genre_id = T2.genre_id GROUP BY T2.genre_id ORDER BY COUNT(T1.movie_id) LIMIT 2, 1"}],"db_id":"movies_4"}168{"prompt":"Find the ID of advisor of student ID 80 and state the level of courses taught by him\/her.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find the ID of advisor of student ID 80 and state the level of courses taught by him\/her.\n        \n        ### Context:\n        #### Query Context:\n        ID of advisor refers to p_id_dummy; student ID 80 refers to advisedBy.p_id = 80; level of courses refers to courseLevel\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.p_id_dummy, T2.courseLevel FROM advisedBy AS T1 INNER JOIN course AS T2 ON T1.p_id = T2.course_id INNER JOIN taughtBy AS T3 ON T2.course_id = T3.course_id WHERE T1.p_id = 80"}],"db_id":"computer_student"}169{"prompt":"How many arrests were made in 2018 in an animal hospital under FBI code 08B?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many arrests were made in 2018 in an animal hospital under FBI code 08B?\n        \n        ### Context:\n        #### Query Context:\n        arrest refers to arrest = 'TRUE'; in 2018 refers to date LIKE '%2018%'; in an animal hospital refers to location_description = 'ANIMAL HOSPITAL'; FBI code 08B refers to fbi_code_no = '08B'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN arrest = 'TRUE' THEN 1 ELSE 0 END) FROM Crime WHERE date LIKE '%2018%' AND location_description = 'ANIMAL HOSPITAL' AND fbi_code_no = '08B'"}],"db_id":"chicago_crime"}170{"prompt":"In which year did the record ID 19 with game publisher ID 6657 released?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which year did the record ID 19 with game publisher ID 6657 released?\n        \n        ### Context:\n        #### Query Context:\n        which year refers to release_year; record ID 19 refers to game platform.id; id = 19\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.release_year FROM game_platform AS T WHERE T.game_publisher_id = 6657 AND T.id = 19"}],"db_id":"video_games"}171{"prompt":"List down the first name of patients who encountered normal pregnancy.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the first name of patients who encountered normal pregnancy.\n        \n        ### Context:\n        #### Query Context:\n        encountered normal pregnancy refers to encounters where REASONDESCRIPTION = 'Normal pregnancy';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.first FROM patients AS T1 INNER JOIN encounters AS T2 ON T1.patient = T2.PATIENT WHERE T2.REASONDESCRIPTION = 'Normal pregnancy'"}],"db_id":"synthea"}172{"prompt":"What is the average rating of Apps falling under the racing genre and what is the percentage ratio of positive sentiment reviews?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average rating of Apps falling under the racing genre and what is the percentage ratio of positive sentiment reviews?\n        \n        ### Context:\n        #### Query Context:\n        average rating = AVG(Rating); percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*)), 100));\n        \n        #### Database Schema:\n        {'db_id': 'app_store', 'table_names_original': ['playstore', 'user_reviews'], 'table_names': ['googleplaystore', 'googleplaystore_user_reviews'], 'column_names_original': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment_Polarity'], [1, 'Sentiment_Subjectivity']], 'column_names': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment Polarity'], [1, 'Sentiment Subjectivity']], 'column_types': ['text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [], 'foreign_keys': [[11, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.Rating), CAST(COUNT(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE NULL END) AS REAL) * 100 \/ COUNT(T2.Sentiment) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres = 'Racing'"}],"db_id":"app_store"}173{"prompt":"What is the average median income for all City type of stores?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average median income for all City type of stores?\n        \n        ### Context:\n        #### Query Context:\n        AVG(Median Income) where Type = 'City';\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(`Median Income`) FROM `Store Locations` WHERE Type = 'City'"}],"db_id":"regional_sales"}174{"prompt":"How many in debt customers in the household market segment are from Russia?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many in debt customers in the household market segment are from Russia?\n        \n        ### Context:\n        #### Query Context:\n        in debt customers refer to c_custkey where c_acctbal < 0; c_mktsegment = 'HOUSEHOLD'; Russian is the name of the nation which refers to n_name = 'RUSSIA';\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.c_custkey) FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T1.c_acctbal < 0 AND T1.c_mktsegment = 'HOUSEHOLD' AND T2.n_name = 'RUSSIA'"}],"db_id":"retails"}175{"prompt":"Write down the top ten menus with the highest dish count.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Write down the top ten menus with the highest dish count.\n        \n        ### Context:\n        #### Query Context:\n        highest dish count refers to MAX(dish_count);\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT name FROM Menu GROUP BY name ORDER BY dish_count DESC LIMIT 10"}],"db_id":"menu"}176{"prompt":"Which country is the oldest player from?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country is the oldest player from?\n        \n        ### Context:\n        #### Query Context:\n        country refers to Country_Name; the oldest refers to min(DOB)\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Country_Name FROM Country AS T1 INNER JOIN Player AS T2 ON T2.Country_Name = T1.Country_Id WHERE T2.Country_Name IS NOT NULL ORDER BY T2.DOB LIMIT 1"}],"db_id":"soccer_2016"}177{"prompt":"Which county in northern California has the highest number of cities?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which county in northern California has the highest number of cities?\n        \n        ### Context:\n        #### Query Context:\n        northern California refers to region = 'northern california'; the highest number of cities refers to max(count(city))\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT county FROM geographic WHERE region = 'northern california' GROUP BY county ORDER BY COUNT(city) DESC LIMIT 1"}],"db_id":"restaurant"}178{"prompt":"Among the black students in 2011, list the institution site and name of those who has 20 t0 30 degree-seeking students in the cohort.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the black students in 2011, list the institution site and name of those who has 20 t0 30 degree-seeking students in the cohort.\n        \n        ### Context:\n        #### Query Context:\n        black refers to race = 'B'; in 2011 refers to year = '2011'; institution name refers to chronname; 20 to 30 degree-seeking students in the cohort refers to grad_cohort BWEEN 20 AND 30;\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.site, T1.chronname FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T2.year = 2011 AND T2.race = 'B' AND T2.grad_cohort BETWEEN 20 AND 30"}],"db_id":"college_completion"}179{"prompt":"Find the names of papers which are published in the year 1996.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find the names of papers which are published in the year 1996.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Title FROM Paper WHERE year = 1996"}],"db_id":"authors"}180{"prompt":"Who is the stunt coordinator in episode 3?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the stunt coordinator in episode 3?\n        \n        ### Context:\n        #### Query Context:\n        who refers to name; stunt coordinator refers to role = 'stunt coordinator'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.name FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T3.person_id = T2.person_id WHERE T1.episode = 3 AND T2.role = 'stunt coordinator'"}],"db_id":"law_episode"}181{"prompt":"What is the ship ID of shipments shipped to the city with the largest area?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the ship ID of shipments shipped to the city with the largest area?\n        \n        ### Context:\n        #### Query Context:\n        city with largest area refers to Max(area)\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ship_id FROM shipment AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.city_id ORDER BY T2.area DESC LIMIT 1"}],"db_id":"shipping"}182{"prompt":"List the student's first and last name that got a C in the course named \"Applied Deep Learning\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the student's first and last name that got a C in the course named \"Applied Deep Learning\".\n        \n        ### Context:\n        #### Query Context:\n        student's first name refers to f_name; student's last name refers to l_name; got a C refers to grade = 'C';\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.f_name, T1.l_name FROM student AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T3.name = 'Applied Deep Learning ' AND T2.grade = 'C'"}],"db_id":"cs_semester"}183{"prompt":"How heavy is Matthias Trattnig in kilograms?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How heavy is Matthias Trattnig in kilograms?\n        \n        ### Context:\n        #### Query Context:\n        how heavy in kilograms refers to weight_in_kg;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.weight_in_kg FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id WHERE T1.PlayerName = 'Pavel Patera'"}],"db_id":"ice_hockey_draft"}184{"prompt":"Identify the cost difference between Priority and Express shipping methods.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the cost difference between Priority and Express shipping methods.\n        \n        ### Context:\n        #### Query Context:\n        \"Priority\" and \"Express\" are both method_name; cost difference = Subtract (Sum(cost where method_name = 'Express'), Sum(cost where method_name 'Priority'))\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN method_name = 'Priority' THEN cost ELSE 0 END) - SUM(CASE WHEN method_name = 'Express' THEN cost ELSE 0 END) FROM shipping_method"}],"db_id":"books"}185{"prompt":"Please write down the footnote descriptions of Albania in 1981.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please write down the footnote descriptions of Albania in 1981.\n        \n        ### Context:\n        #### Query Context:\n        Albania is the name of country where Country = 'Albania'\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.Description FROM FootNotes AS T1 INNER JOIN Country AS T2 ON T1.Countrycode = T2.CountryCode WHERE T1.Year = 'YR1981' AND T2.ShortName = 'Albania'"}],"db_id":"world_development_indicators"}186{"prompt":"How many incidents of domestic violence occurred in an abandoned building in 2018?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many incidents of domestic violence occurred in an abandoned building in 2018?\n        \n        ### Context:\n        #### Query Context:\n        domestic violence refers to domestic = 'TRUE'; in an abandoned building refers to location_description = 'ABANDONED BUILDING'; in 2018 refers to date LIKE '%2018%'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN location_description = 'ABANDONED BUILDING' THEN 1 ELSE 0 END) FROM Crime WHERE date LIKE '%2018%' AND domestic = 'TRUE'"}],"db_id":"chicago_crime"}187{"prompt":"How many teams have the same total number of postseason wins and postseason loses?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many teams have the same total number of postseason wins and postseason loses?\n        \n        ### Context:\n        #### Query Context:\n        same total number of postseason wins and postseason loses refers to PostW = PostL\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], 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[15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 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### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT COUNT(tmID) FROM Goalies WHERE PostW = PostL"}],"db_id":"hockey"}188{"prompt":"How many students who have never been absent from school?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students who have never been absent from school?\n        \n        ### Context:\n        #### Query Context:\n        have never been absent refers to `month` = 0;\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(name) FROM longest_absense_from_school WHERE month = 0"}],"db_id":"student_loan"}189{"prompt":"Which country has the city of 114339 in population? Give the full name of the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country has the city of 114339 in population? 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'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM country AS T1 INNER JOIN city AS T2 ON T1.Code = T2.Country WHERE T2.Population = 114339"}],"db_id":"mondial_geo"}190{"prompt":"List out the total pages of Wikipedia in Catalan language.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List out the total pages of Wikipedia in Catalan language.\n        \n        ### Context:\n        #### Query Context:\n        This is not;\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT pages FROM langs"}],"db_id":"language_corpus"}191{"prompt":"Please list the donation messages of the donations for the projects created by a teacher working in a public magnet school in Brooklyn.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the donation messages of the donations for the projects created by a teacher working in a public magnet school in Brooklyn.\n        \n        ### Context:\n        #### Query Context:\n        school in Brooklyn refers to school_city = 'Brooklyn'; public magnet refers to school_magnet = 't';\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.donation_message FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.school_city = 'Brooklyn' AND T1.school_magnet = 't'"}],"db_id":"donor"}192{"prompt":"Among the books purchased by less than 1 dollar, what is the difference between the number of books with less than 500 pages and books with greater than 500 pages?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the books purchased by less than 1 dollar, what is the difference between the number of books with less than 500 pages and books with greater than 500 pages?\n        \n        ### Context:\n        #### Query Context:\n        book purchased by less than 1 dollar refers to price < 1; books with less than 500 pages refers to num_pages < 500; greater than 500 pages refers to num_pages > 500; Difference = Subtract (Count(book_id where num_pages < 500), Count(book_id where num_pages > 500))\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.num_pages < 500 THEN 1 ELSE 0 END) - SUM(CASE WHEN T1.num_pages > 500 THEN 1 ELSE 0 END) AS dif FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id WHERE T2.price < 1"}],"db_id":"books"}193{"prompt":"Among the\" Mumbai Indians\" team that played in 2009, how many percent of the matches did they win?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the\" Mumbai Indians\" team that played in 2009, how many percent of the matches did they win?\n        \n        ### Context:\n        #### Query Context:\n        played in 2009 Match_Date like '2009%'; Mumbai Indians\" team refers to Team_Name = 'Mumbai Indians'; percent of the matches did they win refers to DIVIDE(COUNT(Match_Winner = Team_Id), COUNT(Match_Id))\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.Match_Winner = T2.Team_Id THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.Match_Id) FROM `Match` AS T1 INNER JOIN Team AS T2 ON T1.Team_1 = T2.Team_Id OR T1.Team_2 = T2.Team_Id WHERE T2.Team_Name = 'Mumbai Indians' AND T1.Match_Date LIKE '2009%'"}],"db_id":"soccer_2016"}194{"prompt":"List the included categories in the event ID of 155.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the included categories in the event ID of 155.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.category FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T2.label_id = T1.label_id INNER JOIN app_events AS T3 ON T3.app_id = T2.app_id WHERE T3.event_id = 155"}],"db_id":"talkingdata"}195{"prompt":"How many students that graduated from Lincoln College in 2011 belong to the cohort type of Bachelor's\/equivalent seeking cohort at 4-year institutions?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students that graduated from Lincoln College in 2011 belong to the cohort type of Bachelor's\/equivalent seeking cohort at 4-year institutions?\n        \n        ### Context:\n        #### Query Context:\n        Lincoln College refers to chronname = 'Lincoln College'; in 2011 refers to year = 2011; Bachelor's\/equivalent seeking cohort at 4-year institutions refers to cohort = '4y bach';\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.unitid) FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T1.unitid = T2.unitid WHERE T1.chronname = 'Lincoln College' AND T2.year = 2011 AND T2.cohort = '4y bach'"}],"db_id":"college_completion"}196{"prompt":"Which headquarter's truck has the highest shipments in year 2016?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which headquarter's truck has the highest shipments in year 2016?\n        \n        ### Context:\n        #### Query Context:\n        in 2016 refers to CAST(ship_date AS DATE) = 2016; make = 'Peterbilt' means headquarter is 'Texas (TX)', make = 'Mack' means headquarter is 'North Carolina (NC)', make = 'Kenworth' means headquarter is 'Washington (WA)'; highest shipment refers to MAX(COUNT(ship_id))\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN T2.make = 'Peterbilt' THEN 'Texas (TX)' WHEN T2.make = 'Mack' THEN 'North Carolina (NC)' WHEN T2.make = 'Kenworth' THEN 'Washington (WA)' END AS \"result\" FROM shipment AS T1 INNER JOIN truck AS T2 ON T1.truck_id = T2.truck_id WHERE CAST(T1.ship_date AS DATE) = 2016 GROUP BY T2.make ORDER BY COUNT(T1.ship_id) DESC LIMIT 1"}],"db_id":"shipping"}197{"prompt":"What is the highest price of dishes with menu item ID 1 to 5?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the highest price of dishes with menu item ID 1 to 5?\n        \n        ### Context:\n        #### Query Context:\n        highest price refers to MAX(price); menu item ID 1 to 5 refers to MenuItem.id BETWEEN 1 AND 5;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.price FROM Dish AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.dish_id WHERE T2.id BETWEEN 1 AND 5 ORDER BY T2.price DESC LIMIT 1"}],"db_id":"menu"}198{"prompt":"What is the name and category of the app with the highest amount of -1 sentiment polarity score?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name and category of the app with the highest amount of -1 sentiment polarity score?\n        \n        ### Context:\n        #### Query Context:\n        highest amount of -1 sentiment polarity score refers to MAX(Count(Sentiment_Polarity = 1.0))\n        \n        #### Database Schema:\n        {'db_id': 'app_store', 'table_names_original': ['playstore', 'user_reviews'], 'table_names': ['googleplaystore', 'googleplaystore_user_reviews'], 'column_names_original': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment_Polarity'], [1, 'Sentiment_Subjectivity']], 'column_names': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment Polarity'], [1, 'Sentiment Subjectivity']], 'column_types': ['text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [], 'foreign_keys': [[11, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.App, T1.Category FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity = '-1.0'"}],"db_id":"app_store"}199{"prompt":"Give the number of planes that took off from Los Angeles International airport on 2018\/8\/27.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the number of planes that took off from Los Angeles International airport on 2018\/8\/27.\n        \n        ### Context:\n        #### Query Context:\n        took off from refers to ORIGIN; Los Angeles International airport refers to Description = 'Los Angeles, CA: Los Angeles International'; on 2018\/8\/27 refers to FL_DATE = '2018\/8\/27';\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.FL_DATE = '2018\/8\/27' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Los Angeles, CA: Los Angeles International'"}],"db_id":"airline"}200{"prompt":"For all the employees that have left the Engineering Department, what is the average time of their stay?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For all the employees that have left the Engineering Department, what is the average time of their stay?\n        \n        ### Context:\n        #### Query Context:\n        employees who left a department refers to EndDate NOT null; average stay = AVG(SUBTRACT(year(EndDate)), (year(T1.StartDate)));\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], 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'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission 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T1.EndDate IS NOT NULL"}],"db_id":"works_cycles"}201{"prompt":"For country with area greater than 600000, what is agriculture percentage of GDP the country contributes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For country with area greater than 600000, what is agriculture percentage of GDP the country contributes?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 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'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Agriculture FROM country AS T1 INNER JOIN economy AS T2 ON T1.Code = T2.Country WHERE T1.Area > 600000 AND T2.Agriculture IS NOT NULL"}],"db_id":"mondial_geo"}202{"prompt":"What is the agricultural land area in sq. km of Italy in 1968?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the agricultural land area in sq. km of Italy in 1968?\n        \n        ### Context:\n        #### Query Context:\n        agricultural land area in sq. km refers value where indicatorname = 'Agricultural land (sq. km)'; Italy refers to countryname = 'Italy'; in 1968 refers to year = '1968'\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Value FROM Indicators WHERE IndicatorName = 'Agricultural land (sq. km)' AND Year = 1968 AND CountryName = 'Italy'"}],"db_id":"world_development_indicators"}203{"prompt":"Which winning team in the final round of the postseason series games against the LAL won more than 60 games in the NBA league during the year 1996?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which winning team in the final round of the postseason series games against the LAL won more than 60 games in the NBA league during the year 1996?\n        \n        ### Context:\n        #### Query Context:\n        final round of post season refers to round = 'CSF'; won against LAL refers to tmIDLoser = 'LAL'; in the NBA league refers to lgID = 'NBA'; won more than 60 games refers to won > 60\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.tmID FROM series_post AS T1 INNER JOIN teams AS T2 ON T1.tmIDWinner = T2.tmID WHERE T2.won > 60 AND T1.year = 1996 AND T1.round = 'CSF' AND T1.tmIDLoser = 'LAL'"}],"db_id":"professional_basketball"}204{"prompt":"How many donors in Los Angeles donated to school in another city?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many donors in Los Angeles donated to school in another city?\n        \n        ### Context:\n        #### Query Context:\n        in Los Angeles refers to donor_city = 'Los Angeles'; school in another city refers to school_city ! = 'Los Angeles'\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.schoolid) FROM donations AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T1.donor_city = 'Los Angeles' AND T2.school_city NOT LIKE 'Los Angeles'"}],"db_id":"donor"}205{"prompt":"Calculate the average number of cast members that appeared in the credit from the 185th to the 193rd episode.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average number of cast members that appeared in the credit from the 185th to the 193rd episode.\n        \n        ### Context:\n        #### Query Context:\n        appeared in the credit refers to credited = 'TRUE'; from the 185th to the 193rd episode refers to number_in_series between 185 and 193; cast refers to category = 'Cast'; average number = divide(count(episode_id), 9)\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(T1.episode_id) AS REAL) \/ (193 - 185 + 1) FROM Credit AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T1.category = 'Cast' AND T1.credited = 'true' AND T2.number_in_series BETWEEN 185 AND 193"}],"db_id":"law_episode"}206{"prompt":"How much more total box office gross did the Walt Disney Company have in revenue in 1998 than in 1997?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much more total box office gross did the Walt Disney Company have in revenue in 1998 than in 1997?\n        \n        ### Context:\n        #### Query Context:\n        SUBTRACT(SUM(Year = 1998), SUM(Year = 1997))\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN `Year` = 1998 THEN Total ELSE 0 END) - SUM(CASE WHEN `Year` = 1997 THEN Total ELSE 0 END) FROM revenue"}],"db_id":"disney"}207{"prompt":"Please list the top 3 cities with the most number of tweets posted in Canada.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the top 3 cities with the most number of tweets posted in Canada.\n        \n        ### Context:\n        #### Query Context:\n        \"Canada\" is the Country; city with most number of tweets refers to City where Max(Count(TweetID))\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.City FROM ( SELECT T2.City, COUNT(T1.TweetID) AS num FROM twitter AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID WHERE T2.Country = 'Canada' GROUP BY T2.City ) T ORDER BY T.num DESC LIMIT 3"}],"db_id":"social_media"}208{"prompt":"Which country has the most number of suppliers whose account is in debt?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country has the most number of suppliers whose account is in debt?\n        \n        ### Context:\n        #### Query Context:\n        country refers to n_name; the most number of suppliers whose account is in debt refers to MAX(SUM(s_acctbal < 0));\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.n_name FROM ( SELECT T2.n_name, SUM(T1.s_acctbal) AS num FROM supplier AS T1 INNER JOIN nation AS T2 ON T1.s_nationkey = T2.n_nationkey WHERE T1.s_acctbal < 0 GROUP BY T2.n_name ) AS T ORDER BY T.num LIMIT 1"}],"db_id":"retails"}209{"prompt":"How many students is disabled and unemployed at the same time?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students is disabled and unemployed at the same time?\n        \n        ### Context:\n        #### Query Context:\n        students who are disabled and unemployed at the same time refers to disabled.name = unemployed.name;\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.name) FROM disabled AS T1 INNER JOIN unemployed AS T2 ON T2.name = T1.name"}],"db_id":"student_loan"}210{"prompt":"How many foodborne illness investigations were done in 2014?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many foodborne illness investigations were done in 2014?\n        \n        ### Context:\n        #### Query Context:\n        foodborne illness investigations refer to inspections where type = 'Foodborne Illness Investigation'; investigations in 2014 refers to date between '2014-01-01' and '2014-12-31';\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM inspections WHERE STRFTIME('%Y', `date`) = '2014' AND type = 'Foodborne Illness Investigation'"}],"db_id":"food_inspection"}211{"prompt":"List out the full name and URL link of ICCI?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List out the full name and URL link of ICCI?\n        \n        ### Context:\n        #### Query Context:\n        'ICCI' is the ShortName of conference; URL refer to HomePage\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT FullName, HomePage FROM Conference WHERE ShortName = 'ICCI'"}],"db_id":"authors"}212{"prompt":"How many root beers of the Bulldog were purchased in August, 2014?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many root beers of the Bulldog were purchased in August, 2014?\n        \n        ### Context:\n        #### Query Context:\n        Bulldog refers to BrandName = 'Bulldog'; purchased in August, 2014 refers to SUBSTR(TransactionDate, 1, 4) = '2014' AND SUBSTR(TransactionDate, 6, 2) = '08';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.BrandID) FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID WHERE T2.TransactionDate LIKE '2014-08%' AND T3.BrandName = 'Bulldog'"}],"db_id":"beer_factory"}213{"prompt":"How many author published papers in the 'IEEE Computer' journal?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many author published papers in the 'IEEE Computer' journal?\n        \n        ### Context:\n        #### Query Context:\n        IEEE Computer refer to FullName; How many author published papers refer to COUNT(PaperAuthor.Name) where FullName = \u2019IEEE Computer\u2019\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.Name) FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId INNER JOIN Journal AS T3 ON T1.JournalId = T3.Id WHERE T3.FullName = 'IEEE Computer'"}],"db_id":"authors"}214{"prompt":"How many reviews does \"Planet Money\" have?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many reviews does \"Planet Money\" have?\n        \n        ### Context:\n        #### Query Context:\n        \"Planet Money\" is the title of podcast\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.podcast_id) FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.title = 'Planet Money'"}],"db_id":"music_platform_2"}215{"prompt":"In the year 2012, which conference had the most papers presented? Give the short name of the conference.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In the year 2012, which conference had the most papers presented? Give the short name of the conference.\n        \n        ### Context:\n        #### Query Context:\n        Papers refers to Paper.Id; short name of the conference refers to Conference.ShortName\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ShortName FROM Paper AS T1 INNER JOIN Conference AS T2 ON T1.ConferenceId = T2.Id WHERE T1.Year = '2012' GROUP BY T1.ConferenceId ORDER BY COUNT(T1.Id) DESC LIMIT 1"}],"db_id":"authors"}216{"prompt":"Please list any three order numbers that have been shipped using Speedy Express.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list any three order numbers that have been shipped using Speedy Express.\n        \n        ### Context:\n        #### Query Context:\n        Speedy Express is the name of the shipping company; three order numbers refer to OrderID LIMIT 3;\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.OrderID FROM Orders AS T1 INNER JOIN Shippers AS T2 ON T1.ShipVia = T2.ShipperID WHERE T2.CompanyName = 'Speedy Express' LIMIT 3"}],"db_id":"retail_world"}217{"prompt":"Among all films that the customer RUTH MARTINEZ has rented, what is the percentage of it being a Music film?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all films that the customer RUTH MARTINEZ has rented, what is the percentage of it being a Music film?\n        \n        ### Context:\n        #### Query Context:\n        music film refers to name = 'Music'; percentage = Divide (Count(film_id where name = 'Music'), Count(film_id)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T3.name = 'Music', 1, 0)) AS REAL) * 100 \/ COUNT(T1.film_id) FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T2.category_id = T3.category_id INNER JOIN inventory AS T4 ON T1.film_id = T4.film_id INNER JOIN customer AS T5 ON T4.store_id = T5.store_id INNER JOIN rental AS T6 ON T4.inventory_id = T6.inventory_id WHERE T5.first_name = 'RUTH' AND T5.last_name = 'MARTINEZ'"}],"db_id":"movie_3"}218{"prompt":"Please list the assumed name of all the facilities inspected by Joshua Rosa.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the assumed name of all the facilities inspected by Joshua Rosa.\n        \n        ### Context:\n        #### Query Context:\n        assumed name refers to dba_name\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.dba_name FROM employee AS T1 INNER JOIN inspection AS T2 ON T1.employee_id = T2.employee_id INNER JOIN establishment AS T3 ON T2.license_no = T3.license_no WHERE T1.first_name = 'Joshua' AND T1.last_name = 'Rosa'"}],"db_id":"food_inspection_2"}219{"prompt":"What is the percentage of the European countries among the given countries?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of the European countries among the given countries?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(n_name where r_name = 'EUROPE'), COUNT(n_name)) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T2.r_name = 'EUROPE', 1, 0)) AS REAL) * 100 \/ COUNT(T1.n_name) FROM nation AS T1 INNER JOIN region AS T2 ON T1.n_regionkey = T2.r_regionkey"}],"db_id":"retails"}220{"prompt":"To whom does the employee have to inform that is the sales representative of the French customer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        To whom does the employee have to inform that is the sales representative of the French customer?\n        \n        ### Context:\n        #### Query Context:\n        inform refers to reportsTo; 'reportsTO' is the leader of the 'employeeNumber'; France is a country; country = 'France';\n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.reportsTo FROM employees AS T1 INNER JOIN customers AS T2 ON T1.employeeNumber = T2.salesRepEmployeeNumber WHERE T2.country = 'France'"}],"db_id":"car_retails"}221{"prompt":"Which object in image 8 is the widest? State its object sample ID.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which object in image 8 is the widest? State its object sample ID.\n        \n        ### Context:\n        #### Query Context:\n        widest relates to the width of the bounding\nbox of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8;\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT OBJ_SAMPLE_ID FROM IMG_OBJ WHERE IMG_ID = 8 ORDER BY W DESC LIMIT 1"}],"db_id":"image_and_language"}222{"prompt":"How many businesses in Tempe are rated as 'Wonderful experience?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many businesses in Tempe are rated as 'Wonderful experience?\n        \n        ### Context:\n        #### Query Context:\n        in Tempe refers to city = 'Tempe'; rated as 'Wonderful experience refers to stars > 3\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM Business WHERE city = 'Phoenix' AND stars > 3"}],"db_id":"public_review_platform"}223{"prompt":"How many customers gave a product the best possible rating? Please list their names.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many customers gave a product the best possible rating? Please list their names.\n        \n        ### Context:\n        #### Query Context:\n        customers' name refers to ReviewerName; best possible ratings means the highest rating = 5\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        From 1960 to 1970, what is the total point of all-star players who are still alive?\n        \n        ### Context:\n        #### Query Context:\n        from 1960 to 1970 refers to season_id between 1960 and 1970; still alive refers to deathDate = '0000-00-00'\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 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'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.points) FROM players AS T1 INNER JOIN player_allstar AS T2 ON T1.playerID = T2.playerID WHERE T2.season_id BETWEEN 1960 AND 1970 AND T1.deathDate = '0000-00-00'"}],"db_id":"professional_basketball"}225{"prompt":"How many suppliers in Australia whose products were discontinued?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many suppliers in Australia whose products were discontinued?\n        \n        ### Context:\n        #### Query Context:\n        in Australia refers to Country = 'Australia'; discontinued refers to Discontinued = 1\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.Discontinued) FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T1.Discontinued = 1 AND T2.Country = 'Australia'"}],"db_id":"retail_world"}226{"prompt":"For all movies where users left a critic, find the movie name, user, rating and critics comments from the user.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For all movies where users left a critic, find the movie name, user, rating and critics comments from the user.\n        \n        ### Context:\n        #### Query Context:\n        movies where users left a critic refers to critic IS NOT NULL; critic comments refers to critic;\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.movie_title, T1.user_id, T1.rating_score, T1.critic FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T1.critic IS NOT NULL"}],"db_id":"movie_platform"}227{"prompt":"What is the percentage of the total products ordered were not rejected by Drill size?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of the total products ordered were not rejected by Drill size?\n        \n        ### Context:\n        #### Query Context:\n        rejected quantity refers to ScrappedQty; rejected by Drill size refers to Name in ('Drill size too small','Drill size too large'); percentage = DIVIDE(SUM(ScrappedQty) where Name in('Drill size too small','Drill size too large'), OrderQty)\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified 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'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.VacationHours > 20 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.BusinessEntityID) FROM EmployeePayHistory AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.CurrentFlag = 1 AND T2.SickLeaveHours > 10"}],"db_id":"works_cycles"}228{"prompt":"How many papers whose authors include Thomas Wiegand were published in 1995?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many papers whose authors include Thomas Wiegand were published in 1995?\n        \n        ### Context:\n        #### Query Context:\n        published in 1995 refers to Year = 1995; 'Thomas Wiegand' is the name of author\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.Title) FROM PaperAuthor AS T1 INNER JOIN Paper AS T2 ON T1.PaperId = T2.Id WHERE T1.Name = 'Thomas Wiegand' AND T2.Year = 1995"}],"db_id":"authors"}229{"prompt":"How many complaints on credit cards in the year 2016 were filed by male clients?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many complaints on credit cards in the year 2016 were filed by male clients?\n        \n        ### Context:\n        #### Query Context:\n        credit cards refers to Product = 'Credit card'; 2016 refers to year(Date received) = 2016; male refers to sex = 'Male';\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.sex) FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE strftime('%Y', T2.`Date received`) = '2016' AND T1.sex = 'Male' AND T2.Product = 'Credit card'"}],"db_id":"retail_complains"}230{"prompt":"What is the title of the movie that was made with the most money and resources?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of the movie that was made with the most money and resources?\n        \n        ### Context:\n        #### Query Context:\n        made with the most money and resources refers to max(budget)\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT title FROM movie ORDER BY budget DESC LIMIT 1"}],"db_id":"movies_4"}231{"prompt":"What are the names of the cities along the Euphrat River's course? Indicate the capital city of the nation where the Euphrat River flows.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the names of the cities along the Euphrat River's course? Indicate the capital city of the nation where the Euphrat River flows.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.City, T1.Capital FROM country AS T1 INNER JOIN located AS T2 ON T1.Code = T2.Country INNER JOIN river AS T3 ON T3.Name = T2.River WHERE T3.Name = 'Euphrat'"}],"db_id":"mondial_geo"}232{"prompt":"List down the advised student IDs and IDs of employing professor in faculty.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the advised student IDs and IDs of employing professor in faculty.\n        \n        ### Context:\n        #### Query Context:\n        advised student IDs refers to person.p_id; IDs of employing professor in faculty refers to p_id_dummy and hasPosition = 'Faculty_eme'\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.p_id, T2.p_id FROM advisedBy AS T1 INNER JOIN person AS T2 ON T1.p_id_dummy = T2.p_id WHERE hasPosition = 'Faculty_eme'"}],"db_id":"computer_student"}233{"prompt":"In which city was the 1992 Summer Olympic Games held?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which city was the 1992 Summer Olympic Games held?\n        \n        ### Context:\n        #### Query Context:\n        In which city refers to city_name; 1992 Summer Olympic Games refer to games_name = '1992 Summer';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.city_name FROM games_city AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.id INNER JOIN games AS T3 ON T1.games_id = T3.id WHERE T3.games_name = '1992 Summer'"}],"db_id":"olympics"}234{"prompt":"What is the title of the latest released movie produced by production company \"Universal Pictures\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of the latest released movie produced by production company \"Universal Pictures\"?\n        \n        ### Context:\n        #### Query Context:\n        \"Universal Pictures\" refers to company_name = 'Universal Pictures'; latest released refers to max(release_date)\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.title FROM production_company AS T1 INNER JOIN movie_company AS T2 ON T1.company_id = T2.company_id INNER JOIN movie AS T3 ON T2.movie_id = T3.movie_id WHERE T1.company_name = 'Universal Pictures' ORDER BY T3.release_date DESC LIMIT 1"}],"db_id":"movies_4"}235{"prompt":"Among the person nominated for the Comedy Series Award in 2009, how many of them were born in California?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the person nominated for the Comedy Series Award in 2009, how many of them were born in California?\n        \n        ### Context:\n        #### Query Context:\n        nominated refers to result = 'Nominee'; born in California refers to birth_place = 'California'; in 2009 refers to year = 2009\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM Person AS T1 INNER JOIN Award AS T2 ON T1.name = T2.person WHERE T2.year = 2009 AND T2.award = 'Comedy Series' AND T1.birth_region = 'California';"}],"db_id":"simpson_episodes"}236{"prompt":"What is the review length of user 60776 to business with business ID 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the review length of user 60776 to business with business ID 1?\n        \n        ### Context:\n        #### Query Context:\n        \"60776\" is the user_id\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT review_length FROM Reviews WHERE user_id = 60776 AND business_id = 1"}],"db_id":"public_review_platform"}237{"prompt":"Among the root beers sold in bottles, how many are sold at the location 38.559615, -121.42243?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the root beers sold in bottles, how many are sold at the location 38.559615, -121.42243?\n        \n        ### Context:\n        #### Query Context:\n        in bottles refers to ContainerType = 'Bottle';\u00a0 location 38.559615, -121.42243 refers to latitude = 38.559615 AND longitude = -121.42243;\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T4.BrandID) FROM `transaction` AS T1 INNER JOIN geolocation AS T2 ON T1.LocationID = T2.LocationID INNER JOIN location AS T3 ON T1.LocationID = T3.LocationID INNER JOIN rootbeer AS T4 ON T1.RootBeerID = T4.RootBeerID WHERE T2.Latitude = 38.559615 AND T2.Longitude = -121.42243 AND T4.ContainerType = 'Bottle'"}],"db_id":"beer_factory"}238{"prompt":"Of the students with high salaries, how many took the computer vision course?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Of the students with high salaries, how many took the computer vision course?\n        \n        ### Context:\n        #### Query Context:\n        high salaries refers to salary = 'High';\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.student_id) FROM RA AS T1 INNER JOIN registration AS T2 ON T2.student_id = T1.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T1.salary = 'high' AND T3.name = 'Computer Vision'"}],"db_id":"cs_semester"}239{"prompt":"Who is the youngest player who is still living. State the given name and date of birth.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the youngest player who is still living. State the given name and date of birth.\n        \n        ### Context:\n        #### Query Context:\n        still living refers to deathYear IS NULL; youngest refers to MAX(birthYear,birthMon,birthDay)\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], [12, 'league ID'], [12, 'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], [13, 'year'], [13, 'stint'], [13, 'team ID'], [13, 'shots'], [13, 'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT nameGiven , nameGiven , birthYear, birthMon, birthDay FROM Master WHERE deathYear IS NULL ORDER BY birthYear DESC, birthMon DESC, birthday DESC LIMIT 1"}],"db_id":"hockey"}240{"prompt":"How many publications were published by author named 'Howard F. Lipson'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many publications were published by author named 'Howard F. Lipson'?\n        \n        ### Context:\n        #### Query Context:\n        'Howard F. Lipson' is the name of author\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(PaperId) FROM PaperAuthor WHERE Name = 'Howard F. Lipson'"}],"db_id":"authors"}241{"prompt":"When is the highest price of Terracoin?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        When is the highest price of Terracoin?\n        \n        ### Context:\n        #### Query Context:\n        when refers to date; the highest price refers to max(price)\n        \n        #### Database Schema:\n        {'db_id': 'coinmarketcap', 'table_names_original': ['coins', 'historical'], 'table_names': ['Coins', 'Historical'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform_id'], [0, 'date_added'], [0, 'date_launched'], [1, 'date'], [1, 'coin_id'], [1, 'cmc_rank'], [1, 'market_cap'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent_change_1h'], [1, 'percent_change_24h'], [1, 'percent_change_7d'], [1, 'circulating_supply'], [1, 'total_supply'], [1, 'max_supply'], [1, 'num_market_pairs']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform id'], [0, 'date added'], [0, 'date lanched'], [1, 'date'], [1, 'coin_id'], [1, 'coinmarketcap rank'], [1, 'Market capitalization'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent change 1h'], [1, 'percent change 24h'], [1, 'percent change 7d'], [1, 'circulating supply'], [1, 'total supply'], [1, 'max supply'], [1, 'number market pairs']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer'], 'primary_keys': [1], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.date FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T1.name = 'Terracoin' ORDER BY T2.price DESC LIMIT 1"}],"db_id":"coinmarketcap"}242{"prompt":"List all titles with sales of quantity more than 20 and store located in the CA state.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all titles with sales of quantity more than 20 and store located in the CA state.\n        \n        ### Context:\n        #### Query Context:\n        qty is abbreviation for quantity; sales of quantity more than 20 refers to qty>20; store refers to stor_name\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title, T2.qty FROM titles AS T1 INNER JOIN sales AS T2 ON T1.title_id = T2.title_id INNER JOIN stores AS T3 ON T2.stor_id = T3.stor_id WHERE T2.qty > 20 AND T3.state = 'CA'"}],"db_id":"book_publishing_company"}243{"prompt":"Name all students enlisted in the foreign legion.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name all students enlisted in the foreign legion.\n        \n        ### Context:\n        #### Query Context:\n        in the foreign legion organ = 'foreign_legion';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT name FROM enlist WHERE organ = 'foreign_legion'"}],"db_id":"student_loan"}244{"prompt":"How many basic and medium undergraduate courses are there?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many basic and medium undergraduate courses are there?\n        \n        ### Context:\n        #### Query Context:\n        basic and medium undergraduate courses refers to courseLevel = 'Level_300' and courses refers to course.course_id\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM course WHERE courseLevel = 'Level_300'"}],"db_id":"computer_student"}245{"prompt":"How many times is the number of images containing \"broccoli\" than \"tomato\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many times is the number of images containing \"broccoli\" than \"tomato\"?\n        \n        ### Context:\n        #### Query Context:\n        images refers to IMG_ID; \"broccoli\" refers to OBJ_CLASS = 'broccoli'; \"tomato\" refers to OBJ_CLASS = 'tomato' ; How many times = divide(count(OBJ_SAMPLE_ID) when OBJ_CLASS = 'broccoli', count(OBJ_SAMPLE_ID) when OBJ_CLASS = 'tomato')\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T1.OBJ_CLASS = 'broccoli' THEN T2.OBJ_SAMPLE_ID ELSE NULL END) AS REAL) \/ COUNT(CASE WHEN T1.OBJ_CLASS = 'tomato' THEN T2.OBJ_SAMPLE_ID ELSE NULL END) times FROM OBJ_CLASSES AS T1 INNER JOIN IMG_OBJ AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID"}],"db_id":"image_and_language"}246{"prompt":"Compute the average percentage of female students.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Compute the average percentage of female students.\n        \n        ### Context:\n        #### Query Context:\n        average percentage of female students refers to avg(pct_female_students)\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(pct_female_students) FROM university_year"}],"db_id":"university"}247{"prompt":"How much taller is David Bornhammar than Pauli Levokari in centimeters?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much taller is David Bornhammar than Pauli Levokari in centimeters?\n        \n        ### Context:\n        #### Query Context:\n        how much taller = SUBTRACT(SUM(height_in_cm WHERE PlayerName = 'David Bornhammar'), SUM(height_in_cm WHERE PlayerName = 'Pauli Levokari')); height in centimeters refers to height_in_cm;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT ( SELECT T2.height_in_cm FROM PlayerInfo AS T1 INNER JOIN height_info AS T2 ON T1.height = T2.height_id WHERE T1.PlayerName = 'David Bornhammar' ) - ( SELECT T2.height_in_cm FROM PlayerInfo AS T1 INNER JOIN height_info AS T2 ON T1.height = T2.height_id WHERE T1.PlayerName = 'Pauli Levokari' )"}],"db_id":"ice_hockey_draft"}248{"prompt":"Provide the number of Yelp businesses in \"Gilbert\" which got a\" high\" review count.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the number of Yelp businesses in \"Gilbert\" which got a\" high\" review count.\n        \n        ### Context:\n        #### Query Context:\n        \"Gilbert\" is the name of city; high review count refers to review_count = 'High'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM Business WHERE review_count = 'High' AND city = 'Gilbert'"}],"db_id":"public_review_platform"}249{"prompt":"What percentage of nations have achieved independence since 1993 and practice parliamentary democracy? Please include any three parliament-based democracies that attained independence after 1993.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of nations have achieved independence since 1993 and practice parliamentary democracy? Please include any three parliament-based democracies that attained independence after 1993.\n        \n        ### Context:\n        #### Query Context:\n        Percentage of rivers with lengths greater than 3,000 miles = [(Total number of rivers with lengths greater than 3,000 miles) \/ (Total number of rivers)] * 100%\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(IIF(government = 'parliamentary democracy', 1, 0)) , CAST(SUM(IIF(government = 'parliamentary democracy', 1, 0)) AS REAL) * 100 \/ COUNT(*) FROM politics AS t1 WHERE STRFTIME('%Y', independence) >= '1993'"}],"db_id":"mondial_geo"}250{"prompt":"Name the country in which the topic is about Poverty: Shared Prosperity. Indicate the long name of the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the country in which the topic is about Poverty: Shared Prosperity. Indicate the long name of the country.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.LongName FROM Country AS T1 INNER JOIN footnotes AS T2 ON T1.CountryCode = T2.Countrycode INNER JOIN Series AS T3 ON T2.Seriescode = T3.SeriesCode WHERE T3.Topic = 'Poverty: Shared prosperity'"}],"db_id":"world_development_indicators"}251{"prompt":"In 2012, how many complaints about Credit card product came from clients in Omaha?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In 2012, how many complaints about Credit card product came from clients in Omaha?\n        \n        ### Context:\n        #### Query Context:\n        in 2012 refers to Date received LIKE'2012%'; in Omaha refers to city = 'Omaha'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.client_id) FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.city = 'Omaha' AND strftime('%Y', T2.`Date received`) = '2012' AND T2.Product = 'Credit card'"}],"db_id":"retail_complains"}252{"prompt":"Give the coordinates of the buyer of R & A Plant Genetics from Benchmark Education.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the coordinates of the buyer of R & A Plant Genetics from Benchmark Education.\n        \n        ### Context:\n        #### Query Context:\n        coordinates refer to (school_latitude, school_longitude); R & A Plant Genetics refer to item_name; Benchmark Education refer to vendor_name\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.school_latitude, T2.school_longitude FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T1.item_name = 'R & A Plant Genetics' AND T1.vendor_name = 'Benchmark Education'"}],"db_id":"donor"}253{"prompt":"How many ratings on the movie \"A Way of Life\" are made after the year 2011?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many ratings on the movie \"A Way of Life\" are made after the year 2011?\n        \n        ### Context:\n        #### Query Context:\n        A Way of Life' is movie_title; rating after the year 2011 refers to rating_timestamp_utc > '2011';\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.rating_id) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_title = 'A Way of Life' AND T1.rating_timestamp_utc >= '2012-01-01'"}],"db_id":"movie_platform"}254{"prompt":"List all goalies who played in the year 2005 season and shorter than 72 inches. List all the team names he play for.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all goalies who played in the year 2005 season and shorter than 72 inches. List all the team names he play for.\n        \n        ### Context:\n        #### Query Context:\n        shorter than 72 inches refers to height<72\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], [12, 'league ID'], [12, 'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], [13, 'year'], [13, 'stint'], [13, 'team ID'], [13, 'shots'], [13, 'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.firstName, T1.lastName, T3.name FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID INNER JOIN Teams AS T3 ON T2.tmID = T3.tmID WHERE T2.year = 2005 AND T1.height < 72"}],"db_id":"hockey"}255{"prompt":"What is the population of the district with the least population?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the population of the district with the least population?\n        \n        ### Context:\n        #### Query Context:\n        the least population refers = min(sum(population))\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(population) FROM Community_Area GROUP BY side ORDER BY SUM(population) LIMIT 1"}],"db_id":"chicago_crime"}256{"prompt":"List the drivers who shipped the shipments to the least populated city.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the drivers who shipped the shipments to the least populated city.\n        \n        ### Context:\n        #### Query Context:\n        least populated city refers to Min(population); name refers to first_name, last_name\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.first_name, T3.last_name FROM shipment AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.city_id INNER JOIN driver AS T3 ON T3.driver_id = T1.driver_id ORDER BY T2.population ASC LIMIT 1"}],"db_id":"shipping"}257{"prompt":"What is the brand of the truck that is used to ship by Zachery Hicks?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the brand of the truck that is used to ship by Zachery Hicks?\n        \n        ### Context:\n        #### Query Context:\n        brand of truck refers to make\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.make FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id INNER JOIN driver AS T3 ON T3.driver_id = T2.driver_id WHERE T3.first_name = 'Zachery' AND T3.last_name = 'Hicks'"}],"db_id":"shipping"}258{"prompt":"List all the films that Karl Berr starred in and rated as PG.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the films that Karl Berr starred in and rated as PG.\n        \n        ### Context:\n        #### Query Context:\n        film refers to title; rated as PG refers to rating = 'PG'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.title FROM actor AS T1 INNER JOIN film_actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T2.film_id = T3.film_id WHERE T1.first_name = 'KARL' AND T1.last_name = 'BERRY' AND T3.rating = 'PG'"}],"db_id":"movie_3"}259{"prompt":"How many players, who were drafted by Anaheim Ducks in 2008, have played for U.S. National U18 Team?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many players, who were drafted by Anaheim Ducks in 2008, have played for U.S. National U18 Team?\n        \n        ### Context:\n        #### Query Context:\n        drafted by Anaheim Ducks refers to overallby = 'Anaheim Ducks'; in 2008 refers to draftyear = 2008; played for U.S. National U18 Team refers to TEAM = 'U.S. National U18 Team';\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.ELITEID) FROM PlayerInfo AS T1 INNER JOIN SeasonStatus AS T2 ON T1.ELITEID = T2.ELITEID WHERE T1.overallby = 'Anaheim Ducks' AND T1.draftyear = 2008 AND T2.TEAM = 'U.S. National U18 Team'"}],"db_id":"ice_hockey_draft"}260{"prompt":"What is the capability on research of the student named Alvera McQuillin?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the capability on research of the student named Alvera McQuillin?\n        \n        ### Context:\n        #### Query Context:\n        capability on research refers to capability;\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.capability FROM student AS T1 INNER JOIN RA AS T2 ON T1.student_id = T2.student_id WHERE T1.f_name = 'Alvera' AND T1.l_name = 'McQuillin'"}],"db_id":"cs_semester"}261{"prompt":"How many active businesses from Casa Grande are registered in the database?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many active businesses from Casa Grande are registered in the database?\n        \n        ### Context:\n        #### Query Context:\n        active business refers to active = 'true'; Casa Grande refers to city = 'Casa Grande'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM Business WHERE active = 'true' AND city = 'Casa Grande'"}],"db_id":"public_review_platform"}262{"prompt":"What is the runtime of the movie starred by Jackie Chan with a rating greater than 7?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the runtime of the movie starred by Jackie Chan with a rating greater than 7?\n        \n        ### Context:\n        #### Query Context:\n        starred by Jackie Chan refers to Name = 'Jackie Chan'; rating greater than 7 refers to Rating > 7\n        \n        #### Database Schema:\n        {'db_id': 'movie', 'table_names_original': ['actor', 'movie', 'characters'], 'table_names': ['actor', 'movie', 'characters'], 'column_names_original': [[-1, '*'], [0, 'ActorID'], [0, 'Name'], [0, 'Date of Birth'], [0, 'Birth City'], [0, 'Birth Country'], [0, 'Height (Inches)'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'MovieID'], [1, 'Title'], [1, 'MPAA Rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'Rating Count'], [1, 'Summary'], [2, 'MovieID'], [2, 'ActorID'], [2, 'Character Name'], [2, 'creditOrder'], [2, 'pay'], [2, 'screentime']], 'column_names': [[-1, '*'], [0, 'actor id'], [0, 'Name'], [0, 'date of birth'], [0, 'birth city'], [0, 'birth country'], [0, 'height inches'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'movie id'], [1, 'Title'], [1, 'motion picture association of america rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'rating count'], [1, 'Summary'], [2, 'movie id'], [2, 'actor id'], [2, 'character name'], [2, 'credit order'], [2, 'pay'], [2, 'screentime']], 'column_types': ['text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text'], 'primary_keys': [1, 11, [22, 23]], 'foreign_keys': [[22, 11], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Runtime FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID INNER JOIN actor AS T3 ON T3.ActorID = T2.ActorID WHERE T3.Name = 'Jackie Chan' AND T1.Rating > 7"}],"db_id":"movie"}263{"prompt":"How many location IDs have actual resource hours of 2?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many location IDs have actual resource hours of 2?\n        \n        ### Context:\n        #### Query Context:\n        actual resource hours of 2 refers to ActualResourceHrs = 2\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity 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'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(LocationID) FROM WorkOrderRouting WHERE ActualResourceHrs = 2"}],"db_id":"works_cycles"}264{"prompt":"How many low-income countries under the lending category of the International Development Associations have a note on the series code SM.POP.TOTL?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many low-income countries under the lending category of the International Development Associations have a note on the series code SM.POP.TOTL?\n        \n        ### Context:\n        #### Query Context:\n        low-income countries are where the incomegroup = Low income\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.Countrycode) FROM Country AS T1 INNER JOIN CountryNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T1.LendingCategory = 'IDA' AND T2.Seriescode = 'SM.POP.TOTL' AND IncomeGroup = 'Low income'"}],"db_id":"world_development_indicators"}265{"prompt":"Please calculate the total payment amount of customers who come from the USA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please calculate the total payment amount of customers who come from the USA.\n        \n        ### Context:\n        #### Query Context:\n        USA is a country; total amount payment refers to SUM(amount);\n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T1.amount) FROM payments AS T1 INNER JOIN customers AS T2 ON T1.customerNumber = T2.customerNumber WHERE T2.country = 'USA'"}],"db_id":"car_retails"}266{"prompt":"What is the average amount of donations by people who donated in the project \"Recording Rockin' Readers\"","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average amount of donations by people who donated in the project \"Recording Rockin' Readers\"\n        \n        ### Context:\n        #### Query Context:\n        average amount of donations = divide(sum(donation_to_project), count(donor_acctid)); project \"Recording Rockin' Readers\" refers to title = 'Recording Rockin' Readers'\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T3.donation_to_project) FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid INNER JOIN donations AS T3 ON T2.projectid = T3.projectid WHERE T1.title = 'Recording Rockin'' Readers'"}],"db_id":"donor"}267{"prompt":"Tweet with ID tw-682723090279841798 was posted from which country?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tweet with ID tw-682723090279841798 was posted from which country?\n        \n        ### Context:\n        #### Query Context:\n        \"tw-682723090279841798\" is the TweetID\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Country FROM twitter AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID WHERE T1.TweetID = 'tw-682723090279841798'"}],"db_id":"social_media"}268{"prompt":"List the title of the episode with stars greater than the 70% of average stars of all episodes.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the title of the episode with stars greater than the 70% of average stars of all episodes.\n        \n        ### Context:\n        #### Query Context:\n        stars greater than the 70% of average stars refers to stars > multiply(avg(stars), 0.7)\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.title FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE T2.stars > 0.7 * ( SELECT AVG(stars) FROM Vote );"}],"db_id":"simpson_episodes"}269{"prompt":"What are the favorite project types of each of the top 10 donors?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the favorite project types of each of the top 10 donors?\n        \n        ### Context:\n        #### Query Context:\n        favorite project type refers to project_resource_type; top donors refers to max(donation_total)\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT project_resource_type FROM ( SELECT T1.donor_acctid, T3.project_resource_type FROM donations AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid INNER JOIN resources AS T3 ON T2.projectid = T3.projectid ORDER BY T1.donation_total DESC LIMIT 10 ) GROUP BY project_resource_type ORDER BY COUNT(project_resource_type) DESC LIMIT 1"}],"db_id":"donor"}270{"prompt":"Please list the website purchasing links of the vendors from whom the product Hex Nut 5 can be purchased.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the website purchasing links of the vendors from whom the product Hex Nut 5 can be purchased.\n        \n        ### Context:\n        #### Query Context:\n        website purchasing link refers to PurchasingWebServiceURL\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 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[60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], 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Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], 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'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.PurchasingWebServiceURL FROM ProductVendor AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID INNER JOIN Vendor AS T3 ON T1.BusinessEntityID = T3.BusinessEntityID WHERE T2.Name = 'Hex Nut 5'"}],"db_id":"works_cycles"}271{"prompt":"What is the journal's short and full names that feature papers on the \"Materials\" topic?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the journal's short and full names that feature papers on the \"Materials\" topic?\n        \n        ### Context:\n        #### Query Context:\n        papers on the \"Materials\" topic refers to Keyword = 'Materials'\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ShortName, T2.FullName FROM Paper AS T1 INNER JOIN Journal AS T2 ON T1.JournalId = T2.Id WHERE T1.Keyword LIKE '%Materials%'"}],"db_id":"authors"}272{"prompt":"Count the total quantity for sales from id 1 to 10.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Count the total quantity for sales from id 1 to 10.\n        \n        ### Context:\n        #### Query Context:\n        sales from id 1 to 10 refers to SalesID BETWEEN 1 AND 10;\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(Quantity) FROM Sales WHERE SalesID BETWEEN 1 AND 10"}],"db_id":"sales"}273{"prompt":"On image no. 5, name the attributes that are composed of multiple objects.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        On image no. 5, name the attributes that are composed of multiple objects.\n        \n        ### Context:\n        #### Query Context:\n        image no. 5 refers to IMG_ID = 5; name the attributes refers to ATT_CLASS; multiple objects refers to count(ATT_CLASS) > = 2\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ATT_CLASS FROM IMG_OBJ_ATT AS T1 INNER JOIN ATT_CLASSES AS T2 ON T1.ATT_CLASS_ID = T2.ATT_CLASS_ID WHERE T1.IMG_ID = 5 GROUP BY T2.ATT_CLASS HAVING COUNT(T2.ATT_CLASS) > 2"}],"db_id":"image_and_language"}274{"prompt":"How many organizations are established after 1999\/1\/1 in a country whose GDP is under 500000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many organizations are established after 1999\/1\/1 in a country whose GDP is under 500000?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Country, COUNT(T1.Country) FROM economy AS T1 INNER JOIN organization AS T2 ON T1.Country = T2.Country WHERE T1.GDP < 500000 AND STRFTIME('%Y', T2.Established) < '1999' GROUP BY T1.Country"}],"db_id":"mondial_geo"}275{"prompt":"Identify the players who weigh 120 kg.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the players who weigh 120 kg.\n        \n        ### Context:\n        #### Query Context:\n        players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.PlayerName FROM weight_info AS T1 INNER JOIN PlayerInfo AS T2 ON T1.weight_id = T2.weight WHERE T1.weight_in_kg = 120"}],"db_id":"ice_hockey_draft"}276{"prompt":"What is the highest vendor's selling price for Hex Nut 5?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the highest vendor's selling price for Hex Nut 5?\n        \n        ### Context:\n        #### Query Context:\n        vendor's selling price refers to StandardPrice\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 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'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation 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'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], 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'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.StandardPrice FROM ProductVendor AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID WHERE T2.Name = 'Hex Nut 5' ORDER BY T1.StandardPrice DESC LIMIT 1"}],"db_id":"works_cycles"}277{"prompt":"Provide the ID of professors who are teaching high-level or harder undergraduate course.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the ID of professors who are teaching high-level or harder undergraduate course.\n        \n        ### Context:\n        #### Query Context:\n        ID of professors refers to taughtBy.p_id; high-level or harder undergraduate course refers to courseLevel = 'Level_400'\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.p_id FROM course AS T1 INNER JOIN taughtBy AS T2 ON T1.course_id = T2.course_id WHERE T1.courseLevel = 'Level_400'"}],"db_id":"computer_student"}278{"prompt":"List down the names of platform where the games released in 2016 can be played on.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the names of platform where the games released in 2016 can be played on.\n        \n        ### Context:\n        #### Query Context:\n        name of platform refers to platform_name; released in 2016 refers to release_year = 2016\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.platform_name FROM platform AS T1 INNER JOIN game_platform AS T2 ON T1.id = T2.platform_id WHERE T2.release_year = 2016"}],"db_id":"video_games"}279{"prompt":"Among the adult films, how many of them have a rental duration of fewer than 4 days?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the adult films, how many of them have a rental duration of fewer than 4 days?\n        \n        ### Context:\n        #### Query Context:\n        adult film refers to rating = 'NC-17'; rental duration of fewer than 4 days refers to rental_duration < 4\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(film_id) FROM film WHERE rating = 'NC-17' AND rental_duration < 4"}],"db_id":"movie_3"}280{"prompt":"List the brand IDs of the beers whose star rating is more than 3.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the brand IDs of the beers whose star rating is more than 3.\n        \n        ### Context:\n        #### Query Context:\n        star rating is more than 3 refers to StarRating > 3;\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT BrandID FROM rootbeerreview WHERE StarRating > 3"}],"db_id":"beer_factory"}281{"prompt":"In years 2011 to 2013, what is the total number of female students in university ID 40?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In years 2011 to 2013, what is the total number of female students in university ID 40?\n        \n        ### Context:\n        #### Query Context:\n        total number of female students refers to SUM(DIVIDE(MULTIPLY(pct_female_students, num_students), 100)); In years 2011 to 2013 refers to year BETWEEN 2011 AND 2013\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CAST(num_students * pct_female_students AS REAL) \/ 100) FROM university_year WHERE year BETWEEN 2011 AND 2013 AND university_id = 40"}],"db_id":"university"}282{"prompt":"What are the games that were released in 2006?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the games that were released in 2006?\n        \n        ### Context:\n        #### Query Context:\n        game refers to game_name; released in 2006 refers to release_year = 2006\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.game_name FROM game_platform AS T1 INNER JOIN game_publisher AS T2 ON T1.game_publisher_id = T2.id INNER JOIN game AS T3 ON T2.game_id = T3.id WHERE T1.release_year = 2006"}],"db_id":"video_games"}283{"prompt":"Please list the employees who have more than 20 vacations hours and wish to receive e-mail promotions.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the employees who have more than 20 vacations hours and wish to receive e-mail promotions.\n        \n        ### Context:\n        #### Query Context:\n        Contact does wish to receive e-mail promotions refers to EmailPromotion = (1,2); more than 20 vacations hours refers to VacationHours>20\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission 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'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.BusinessEntityID FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.EmailPromotion = 1 AND T1.VacationHours > 20"}],"db_id":"works_cycles"}284{"prompt":"How many female customers permit the company to send regular emails to them?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many female customers permit the company to send regular emails to them?\n        \n        ### Context:\n        #### Query Context:\n        female refers to Gender = 'F'; customer permits the company to send regular emails to them refers to SubscribedToEmailList = 'TRUE';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(CustomerID) FROM customers WHERE Gender = 'F' AND SubscribedToEmailList = 'TRUE'"}],"db_id":"beer_factory"}285{"prompt":"Among the trains that run in the east direction, how many of them have at least one car in a non-regular shape?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the trains that run in the east direction, how many of them have at least one car in a non-regular shape?\n        \n        ### Context:\n        #### Query Context:\n        non-regular shape refers to shape in ('elipse', 'bucket')\n        \n        #### Database Schema:\n        {'db_id': 'trains', 'table_names_original': ['cars', 'trains'], 'table_names': ['cars', 'trains'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'train_id'], [0, 'position'], [0, 'shape'], [0, 'len'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load_num'], [1, 'id'], [1, 'direction']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'train id'], [0, 'position'], [0, 'shape'], [0, 'length'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load number'], [1, 'id'], [1, 'direction']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 11], 'foreign_keys': [[2, 11]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.shape IN ('bucket', 'elipse') THEN 1 ELSE 0 end)as count FROM cars AS T1 INNER JOIN trains AS T2 ON T1.train_id = T2.id WHERE T2.direction = 'east'"}],"db_id":"trains"}286{"prompt":"Which restaurant had more low risk violation in inspections, Tiramisu Kitchen or OMNI S.F. Hotel - 2nd Floor Pantry?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which restaurant had more low risk violation in inspections, Tiramisu Kitchen or OMNI S.F. Hotel - 2nd Floor Pantry?\n        \n        ### Context:\n        #### Query Context:\n        Tiramisu Kitchen and OMNI S.F. Hotel - 2nd Floor Pantry are names of the business; more low risk violations refer to MAX(COUNT(risk_category = 'Low Risk'));\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN SUM(CASE WHEN T2.name = 'OMNI S.F. Hotel - 2nd Floor Pantry' THEN 1 ELSE 0 END) > SUM(CASE WHEN T2.name = 'Tiramisu Kitchen' THEN 1 ELSE 0 END) THEN 'OMNI S.F. Hotel - 2nd Floor Pantry' ELSE 'Tiramisu Kitchen' END AS result FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.risk_category = 'Low Risk'"}],"db_id":"food_inspection"}287{"prompt":"Indicate the last number of each street.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Indicate the last number of each street.\n        \n        ### Context:\n        #### Query Context:\n        street refers to street_name; last number of each street refers to Substr (street_number, -1)\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT street_number FROM address"}],"db_id":"books"}288{"prompt":"Which movie title has the lowest movie rental in the horror category?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which movie title has the lowest movie rental in the horror category?\n        \n        ### Context:\n        #### Query Context:\n        the lowest movie rental refers to min(rental_rate); the horror category refers to category.name = 'Horror'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T2.category_id = T3.category_id WHERE T3.`name` = 'Horror' ORDER BY T1.rental_rate LIMIT 1"}],"db_id":"movie_3"}289{"prompt":"What's the nickname for Dan Castellaneta?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What's the nickname for Dan Castellaneta?\n        \n        ### Context:\n        #### Query Context:\n        \"Dan Castellaneta\" is the name of Person\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT nickname FROM Person WHERE name = 'Dan Castellaneta';"}],"db_id":"simpson_episodes"}290{"prompt":"For the donation of the project 'Awesome Audiobooks Make Avid Readers', what was the percentage of the tip in the total amount?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For the donation of the project 'Awesome Audiobooks Make Avid Readers', what was the percentage of the tip in the total amount?\n        \n        ### Context:\n        #### Query Context:\n        Awesome Audiobooks Make Avid Readers' is the title; percentage = Divie(donation_optional_support, donation_total)*100;\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T2.donation_optional_support) AS REAL) * 100 \/ SUM(T2.donation_total) FROM essays AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.title LIKE 'Awesome Audiobooks Make Avid Readers'"}],"db_id":"donor"}291{"prompt":"Among the female users of the devices, how many of them are over 30?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the female users of the devices, how many of them are over 30?\n        \n        ### Context:\n        #### Query Context:\n        female refers to gender = 'F'; over 30 refers to age > 30;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(device_id) FROM gender_age WHERE age > 30 AND gender = 'F'"}],"db_id":"talkingdata"}292{"prompt":"Current legislator Roger F. Wicker has not been a representative for how many terms?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Current legislator Roger F. Wicker has not been a representative for how many terms?\n        \n        ### Context:\n        #### Query Context:\n        Roger F. Wicker is an official_full_name; not a representative refers to district IS NULL OR district = ''\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.official_full_name = 'Roger F. Wicker' THEN 1 ELSE 0 END) AS count FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.district IS NULL OR T2.district = ''"}],"db_id":"legislator"}293{"prompt":"In which episodes was Anthony Azzara not credited?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which episodes was Anthony Azzara not credited?\n        \n        ### Context:\n        #### Query Context:\n        which episode refers to title; not credited refers to credited = ''\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T3.person_id = T2.person_id WHERE T2.credited = 'false' AND T3.name = 'Anthony Azzara'"}],"db_id":"law_episode"}294{"prompt":"What percentage of elderly customers who are never married in the place with geographic ID 24?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of elderly customers who are never married in the place with geographic ID 24?\n        \n        ### Context:\n        #### Query Context:\n        elderly customers refer to age > 65; DIVIDE(COUNT(ID where age > 65, MARITAL_STATUS = 'never married' and GEOID = 24), COUNT(ID where GEOID = 24)) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.MARITAL_STATUS = 'never married' THEN 1.0 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.GEOID = 24"}],"db_id":"software_company"}295{"prompt":"Which district commander was responsible for more incidents in January, 2018, Robert A. Rubio or Glenn White?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which district commander was responsible for more incidents in January, 2018, Robert A. Rubio or Glenn White?\n        \n        ### Context:\n        #### Query Context:\n        in January 2018 refers to Substr(date, 1, 1) = '1' AND Substr(date, 5, 4) = '2018'; 'Robert A. Rubio' and 'Glenn White' are both commander; responsible for more incident refers to Max(count(ward_no))\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.commander FROM District AS T1 INNER JOIN Crime AS T2 ON T1.district_no = T2.district_no WHERE T1.commander IN ('Robert A. Rubio', 'Glenn White') AND SUBSTR(T2.date, 1, 1) = '1' AND SUBSTR(T2.date, 5, 4) = '2018' GROUP BY T1.commander"}],"db_id":"chicago_crime"}296{"prompt":"How many movies were produced by \"Eddie Murphy Productions\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many movies were produced by \"Eddie Murphy Productions\"?\n        \n        ### Context:\n        #### Query Context:\n        produced by \"Eddie Murphy Productions\" refers to company_name = 'Eddie Murphy Productions'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.movie_id) FROM movie_company AS T1 INNER JOIN production_company AS T2 ON T1.company_id = T2.company_id WHERE T2.company_name = 'Eddie Murphy Productions'"}],"db_id":"movies_4"}297{"prompt":"What is the number of State Province of France that doesn't have a State Province Code?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the number of State Province of France that doesn't have a State Province Code?\n        \n        ### Context:\n        #### Query Context:\n        Doesn't have a State Province Code refers to IsOnlyStateProvinceFlag = 1 where StateProvinceCode is unavailable\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 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[64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email 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'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 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'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.CountryRegionCode FROM StateProvince AS T1 INNER JOIN CountryRegion AS T2 ON T1.CountryRegionCode = T2.CountryRegionCode WHERE T2.Name = 'France' AND T1.IsOnlyStateProvinceFlag = 1"}],"db_id":"works_cycles"}298{"prompt":"Among the suppliers providing parts under the type \"promo brushed steel\", how many of them are in debt?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the suppliers providing parts under the type \"promo brushed steel\", how many of them are in debt?\n        \n        ### Context:\n        #### Query Context:\n        type \"promo brushed steel\" refers to p_type = 'PROMO BRUSHED STEEL'; in debt refers to s_acctbal < 0\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T3.s_name) FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey INNER JOIN supplier AS T3 ON T2.ps_suppkey = T3.s_suppkey WHERE T3.s_acctbal < 0 AND T1.p_type = 'PROMO BRUSHED STEEL'"}],"db_id":"retails"}299{"prompt":"Provide the facebook ID of the facebook account named \"RepWilson\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the facebook ID of the facebook account named \"RepWilson\".\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT facebook_id FROM `social-media` WHERE facebook = 'RepWilson'"}],"db_id":"legislator"}300{"prompt":"Which team(s) had 90% games won. List the coach ID for the team and year played.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which team(s) had 90% games won. List the coach ID for the team and year played.\n        \n        ### Context:\n        #### Query Context:\n        team with 90% games won refers to Divide (won, games) > 0.9\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 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'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.name, T1.year, T1.coachID FROM coaches AS T1 INNER JOIN teams AS T2 ON T1.tmID = T2.tmID WHERE CAST(T2.won AS REAL) * 100 \/ T2.games > 90"}],"db_id":"professional_basketball"}301{"prompt":"List all goalies with more lost than won games for two seasons or more. State the name of the player and team he played.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all goalies with more lost than won games for two seasons or more. State the name of the player and team he played.\n        \n        ### Context:\n        #### Query Context:\n        lost refers to L; won refers to W\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], 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'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.firstName, T1.lastName, T3.name FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID INNER JOIN Teams AS T3 ON T2.year = T3.year AND T2.tmID = T3.tmID WHERE T1.pos = 'G' AND T2.L > T2.W GROUP BY T1.firstName, T1.lastName, T3.name HAVING COUNT(T3.year) > 2"}],"db_id":"hockey"}302{"prompt":"How many non-active clients have not returned the rented material?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many non-active clients have not returned the rented material?\n        \n        ### Context:\n        #### Query Context:\n        non-active clients refers to active = 0; not returning a rented material refers to rental_date is null\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.customer_id) FROM rental AS T1 INNER JOIN customer AS T2 ON T1.customer_id = T2.customer_id WHERE T2.active = 0"}],"db_id":"movie_3"}303{"prompt":"What percentage of the movies are PG-13?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of the movies are PG-13?\n        \n        ### Context:\n        #### Query Context:\n        PG-13 is a rating; calculation = DIVIDE(SUM(rating = PG-13), SUM(rating)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(rating = 'PG-13', 1, 0)) AS REAL) * 100 \/ COUNT(film_id) FROM film"}],"db_id":"movie_3"}304{"prompt":"How many scenes are there in work id 7, act 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many scenes are there in work id 7, act 1?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(id) FROM chapters WHERE work_id = 7 AND Act = 1"}],"db_id":"shakespeare"}305{"prompt":"What is the population density of the Petropavl's home country?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the population density of the Petropavl's home country?\n        \n        ### Context:\n        #### Query Context:\n        Population density = Population \/ area\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(T1.Population AS REAL) \/ T1.Area FROM country AS T1 INNER JOIN city AS T2 ON T1.Code = T2.Country WHERE T2.Name = 'Petropavl'"}],"db_id":"mondial_geo"}306{"prompt":"For the rides that started at Market at 10th station and ended at South Van Ness at Market station in August of 2013, which day had the coldest temperature?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For the rides that started at Market at 10th station and ended at South Van Ness at Market station in August of 2013, which day had the coldest temperature?\n        \n        ### Context:\n        #### Query Context:\n        started at refers to start_station_name; start_station_name = 'Market at 10th'; ended at refers to end_station_name; end_station_name = 'South Van Ness at Market'; in August of 2013 refers to start_date BETWEEN '8\/1\/2013 0:00' AND '8\/31\/2013 12:59'; day that had the coldest temperature refers to MIN(min_temperature_f);\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.start_date FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code AND T2.date = SUBSTR(CAST(T1.start_date AS TEXT), 1, INSTR(T1.start_date, ' ') - 1) WHERE T2.date LIKE '8\/%\/2013' AND T1.start_station_name = 'Market at 10th' AND T1.end_station_name = 'South Van Ness at Market' AND T2.min_temperature_f = ( SELECT MIN(T2.min_temperature_f) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code AND T2.date = SUBSTR(CAST(T1.start_date AS TEXT), 1, INSTR(T1.start_date, ' ') - 1) WHERE T2.date LIKE '8\/%\/2013' AND T1.start_station_name = 'Market at 10th' AND T1.end_station_name = 'South Van Ness at Market' )"}],"db_id":"bike_share_1"}307{"prompt":"How many territories are there in the region with the highest number of territories?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many territories are there in the region with the highest number of territories?\n        \n        ### Context:\n        #### Query Context:\n        highest number of territories refers to max(TerritoryID)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.RegionDescription), T1.TerritoryDescription, COUNT(*) AS num FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID GROUP BY T1.TerritoryDescription ORDER BY num DESC LIMIT 1"}],"db_id":"retail_world"}308{"prompt":"Among male legislators, state number of the legislators who are not the senator.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among male legislators, state number of the legislators who are not the senator.\n        \n        ### Context:\n        #### Query Context:\n        male refers to gender_bio = M; not the senator refers to class IS NULL OR class = ''\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T3.state) FROM ( SELECT T2.state FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.gender_bio = 'M' AND (T2.class IS NULL OR T2.class = '') GROUP BY T2.state ) T3"}],"db_id":"legislator"}309{"prompt":"What are the highest salary earn by the the employee and what is his\/her position in the company?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the highest salary earn by the the employee and what is his\/her position in the company?\n        \n        ### Context:\n        #### Query Context:\n        highest salary refers to max(salary); position refers to Title\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Salary, Title FROM Employees WHERE Salary = ( SELECT MAX(Salary) FROM Employees )"}],"db_id":"retail_world"}310{"prompt":"What is the number of sales in region ID 2 with game platform ID 9615?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the number of sales in region ID 2 with game platform ID 9615?\n        \n        ### Context:\n        #### Query Context:\n        number of sales refers to multiply(num_sales, 100000)\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.num_sales * 100000 FROM region_sales AS T WHERE T.region_id = 2 AND T.game_platform_id = 9615"}],"db_id":"video_games"}311{"prompt":"Which paper ID cited the most word? In which class label does it belongs to?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which paper ID cited the most word? In which class label does it belongs to?\n        \n        ### Context:\n        #### Query Context:\n        most cited word refers to max(word_cited_id);\n        \n        #### Database Schema:\n        {'db_id': 'citeseer', 'table_names_original': ['cites', 'paper', 'content'], 'table_names': ['cites', 'paper', 'content'], 'column_names_original': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper_id'], [1, 'class_label'], [2, 'paper_id'], [2, 'word_cited_id']], 'column_names': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper id'], [1, 'class label'], [2, 'paper id'], [2, 'word cited id']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [[1, 2], 3, [5, 6]], 'foreign_keys': [[5, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.paper_id, T1.class_label FROM paper AS T1 INNER JOIN content AS T2 ON T1.paper_id = T2.paper_id GROUP BY T1.paper_id, T1.class_label ORDER BY COUNT(T2.word_cited_id) DESC LIMIT 1"}],"db_id":"citeseer"}312{"prompt":"How many rentals were returned on 5\/27\/2005?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many rentals were returned on 5\/27\/2005?\n        \n        ### Context:\n        #### Query Context:\n        return on 5\/27\/2005 refers to return_date = '2005-05-27'; rental refers to rental_id\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(rental_id) FROM rental WHERE rental_date = '2005-05-27'"}],"db_id":"movie_3"}313{"prompt":"Which word has the most repetitions in the Catalan language? Give the ID of the word.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which word has the most repetitions in the Catalan language? Give the ID of the word.\n        \n        ### Context:\n        #### Query Context:\n        word with the most repetitions refers to MAX(occurrences); ID of the word refers to wid\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT wid FROM langs_words WHERE occurrences = ( SELECT MAX(occurrences) FROM langs_words )"}],"db_id":"language_corpus"}314{"prompt":"What is the full name of the Vice President of Production?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of the Vice President of Production?\n        \n        ### Context:\n        #### Query Context:\n        full name = FirstName+MiddleName+LastName; Vice President of Production is a JobTitle;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 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'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email 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Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], 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'integer', 'integer', 'integer', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'datetime', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.FirstName, T2.MiddleName, T2.LastName FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.JobTitle = 'Vice President of Production'"}],"db_id":"works_cycles"}315{"prompt":"For how many times has Aimee Bixby ordered the product Xerox 1952?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For how many times has Aimee Bixby ordered the product Xerox 1952?\n        \n        ### Context:\n        #### Query Context:\n        Xerox 1952 is a \"Product Name\"; Aimee Bixby ordered refers to \"Customer Name\" = 'Aimee Bixby';\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.`Order ID`) FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.`Customer Name` = 'Aimee Bixby' AND T3.`Product Name` = 'Xerox 1952'"}],"db_id":"superstore"}316{"prompt":"How many players were born after the year 1985?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many players were born after the year 1985?\n        \n        ### Context:\n        #### Query Context:\n        born after the year 1985 refers to SUBSTR(DOB, 1, 4) > 1985\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(Player_Id) FROM Player WHERE SUBSTR(DOB, 1, 4) > 1985"}],"db_id":"soccer_2016"}317{"prompt":"How many cities are there in the United States?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cities are there in the United States?\n        \n        ### Context:\n        #### Query Context:\n        \"United States\" is the country\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.city) FROM country AS T1 INNER JOIN city AS T2 ON T1.country_id = T2.country_id WHERE T1.country = 'United States'"}],"db_id":"movie_3"}318{"prompt":"Find the difference between the number of female senators and representatives born between 1930 to 1970.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find the difference between the number of female senators and representatives born between 1930 to 1970.\n        \n        ### Context:\n        #### Query Context:\n        difference = SUBTRACT(SUM(type = 'sen' WHERE gender_bio = 'F' AND strftime('%Y', birthday_bio) between '1930' and '1970'), SUM(type = 'rep' WHERE gender_bio = 'F' AND strftime('%Y', birthday_bio) between '1930' and '1970')); female refers to gender_bio = 'F'; senators refers to type = 'sen'; representatives refers to type = 'rep'; born between 1930 to 1970 strftime('%Y', birthday_bio) between '1930' and '1970';\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.type = 'sen' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.type = 'rep' THEN 1 ELSE 0 END) FROM historical AS T1 INNER JOIN `historical-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.gender_bio = 'F' AND strftime('%Y', T1.birthday_bio) BETWEEN '1930' AND '1970'"}],"db_id":"legislator"}319{"prompt":"Which job level is O'Rourke at?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which job level is O'Rourke at?\n        \n        ### Context:\n        #### Query Context:\n        job level refers to job_lvl\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT job_lvl FROM employee WHERE lname = 'O''Rourke'"}],"db_id":"book_publishing_company"}320{"prompt":"Among the lists created by user 4208563, which one has the highest number of followers? Indicate how many followers it has and whether the user was a subscriber or not when he created the list.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the lists created by user 4208563, which one has the highest number of followers? Indicate how many followers it has and whether the user was a subscriber or not when he created the list.\n        \n        ### Context:\n        #### Query Context:\n        User 4208563 refers to user_id;highest number of followers refers to MAX(list_followers); user_subscriber = 1 means that the user was a subscriber when he created the list; user_subscriber = 0 means the user was not a subscriber when he created the list (to replace)\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.list_followers, T2.user_subscriber = 1 FROM lists AS T1 INNER JOIN lists_users AS T2 ON T1.user_id = T2.user_id AND T2.list_id = T2.list_id WHERE T2.user_id = 4208563 ORDER BY T1.list_followers DESC LIMIT 1"}],"db_id":"movie_platform"}321{"prompt":"What is the average age of the survey respondents in the United States?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average age of the survey respondents in the United States?\n        \n        ### Context:\n        #### Query Context:\n        Average age refer to AVG(AnswerText) where questionid = 1; respondents in the United States refers to AnswerText = 'United States' where questionid = 3\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T1.AnswerText) AS REAL) \/ COUNT(T1.UserID) FROM Answer AS T1 INNER JOIN ( SELECT T1.UserID FROM Answer AS T1 INNER JOIN Question AS T2 ON T1.QuestionID = T2.questionid WHERE T2.questionid = 3 AND T1.AnswerText = 'United States' ) AS T2 ON T1.UserID = T2.UserID INNER JOIN Question AS T3 ON T1.QuestionID = T3.questionid WHERE T3.questionid = 1"}],"db_id":"mental_health_survey"}322{"prompt":"Among all the employees, how many percent more for the publishers than designers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the employees, how many percent more for the publishers than designers?\n        \n        ### Context:\n        #### Query Context:\n        publisher and designer are job descriptions which refers to job_desc; percentage more = 100*(SUBTRACT(SUM(CASE WHERE job_desc = 'publisher), SUM(CASE WHERE job_desc = 'designer'))\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.job_desc = 'publisher' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.job_desc = 'designer' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.job_id) FROM employee AS T1 INNER JOIN jobs AS T2 ON T1.job_id = T2.job_id"}],"db_id":"book_publishing_company"}323{"prompt":"What is the full name of the customer who ordered the most books of all time?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of the customer who ordered the most books of all time?\n        \n        ### Context:\n        #### Query Context:\n        customer who ordered the most book refers to customer_id where Max(count(order_id)); full name refers to first_name, last_name\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first_name, T1.last_name FROM customer AS T1 INNER JOIN cust_order AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.first_name, T1.last_name ORDER BY COUNT(*) DESC LIMIT 1"}],"db_id":"books"}324{"prompt":"What percentage of sell orders on 04\/04\/2020 were for the state of New York?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of sell orders on 04\/04\/2020 were for the state of New York?\n        \n        ### Context:\n        #### Query Context:\n        sales order on 04\/04\/2020 refers to OrderDate = '4\/4\/20'; 'New York' is\u00a0 the City Name; percentage = Divide (Sum(OrderNumber where City Name = 'New York'), Count (OrderNumber)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.State = 'New York' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.OrderNumber) FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID WHERE T1.OrderDate = '4\/4\/20'"}],"db_id":"regional_sales"}325{"prompt":"Name the dishes that cost 180,000.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the dishes that cost 180,000.\n        \n        ### Context:\n        #### Query Context:\n        cost 180,000 refers to price = 180000;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.name FROM Dish AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.dish_id WHERE T2.price = 180000"}],"db_id":"menu"}326{"prompt":"What category does the item ordered by Katherine Murray on 11\/4\/2018 in the South region belong to?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What category does the item ordered by Katherine Murray on 11\/4\/2018 in the South region belong to?\n        \n        ### Context:\n        #### Query Context:\n        ordered by Katherine Murray refers to \"Customer Name\" = 'Katherine Murray'; on 11\/4\/2018 refers to \"Order Date\" = Date('2018-11-04');\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.Category FROM south_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T1.`Product ID` WHERE T2.`Customer Name` = 'Katherine Murray' AND T1.`Order Date` = '2018-11-04' AND T2.Region = 'South'"}],"db_id":"superstore"}327{"prompt":"List owner's name of businesses with a 100 score.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List owner's name of businesses with a 100 score.\n        \n        ### Context:\n        #### Query Context:\n        owner's name of businesses refers to owner_name;\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.owner_name FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.score = 100"}],"db_id":"food_inspection"}328{"prompt":"List all inspection IDs where the employee named \"Rosemary Kennedy\" was involved.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all inspection IDs where the employee named \"Rosemary Kennedy\" was involved.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.inspection_id FROM employee AS T1 INNER JOIN inspection AS T2 ON T1.employee_id = T2.employee_id WHERE T1.first_name = 'Rosemary' AND T1.last_name = 'Kennedy'"}],"db_id":"food_inspection_2"}329{"prompt":"How tall is the player from Yale University who picked up 28 penalty minutes in the 2005-2006 season?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How tall is the player from Yale University who picked up 28 penalty minutes in the 2005-2006 season?\n        \n        ### Context:\n        #### Query Context:\n        how tall refers to height_in_cm; Yale University refers to TEAM = 'Yale Univ.'; 28 penalty minutes refers to PIM = '28'; 2005-2006 season refers to SEASON = '2005-2006';\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.height_in_cm FROM SeasonStatus AS T1 INNER JOIN PlayerInfo AS T2 ON T1.ELITEID = T2.ELITEID INNER JOIN height_info AS T3 ON T2.height = T3.height_id WHERE T1.SEASON = '2005-2006' AND T1.TEAM = 'Yale Univ.' AND T1.PIM = 28"}],"db_id":"ice_hockey_draft"}330{"prompt":"Among the employees who handled orders to Brazil, who has the highest salary and calculate the average salary of them.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the employees who handled orders to Brazil, who has the highest salary and calculate the average salary of them.\n        \n        ### Context:\n        #### Query Context:\n        orders to Brazil refers to ShipCountry = 'Brazil'; highest salary refers to max(salary); average salary = AVG(Salary)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.FirstName, T1.LastName, AVG(T1.Salary) FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T2.ShipCountry = 'Brazil' GROUP BY T1.FirstName, T1.LastName ORDER BY SUM(T1.Salary) DESC LIMIT 1"}],"db_id":"retail_world"}331{"prompt":"How many action games are there in total?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many action games are there in total?\n        \n        ### Context:\n        #### Query Context:\n        action game refers to genre_name = 'Action'\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.id) FROM game AS T1 INNER JOIN genre AS T2 ON T1.genre_id = T2.id WHERE T2.genre_name = 'Action'"}],"db_id":"video_games"}332{"prompt":"What is the name of the airline that flew the most flights to Chicago Midway International?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the airline that flew the most flights to Chicago Midway International?\n        \n        ### Context:\n        #### Query Context:\n        name of the airline refers to Description; flights to refers to DEST; Chicago Midway International refers to Description = 'Chicago, IL: Chicago Midway International'; most flights to Chicago Midway International refers to MAX(COUNT(DEST WHERE Description = 'Chicago, IL: Chicago Midway International'));\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.Description FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T1.Description = 'Chicago, IL: Chicago Midway International' AND T2.DEST = 'MDW' GROUP BY T3.Description ORDER BY COUNT(T3.Description) DESC LIMIT 1"}],"db_id":"airline"}333{"prompt":"Which region is \"Columbia\" in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which region is \"Columbia\" in?\n        \n        ### Context:\n        #### Query Context:\n        region refers to RegionID; \"Columbia\" refers to TerritoryDescription = 'Columbia'\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.RegionDescription FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID WHERE T1.TerritoryDescription = 'Columbia'"}],"db_id":"retail_world"}334{"prompt":"Of the first 60,000 customers who sent a true response to the incentive mailing sent by the marketing department, how many of them are divorced males?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Of the first 60,000 customers who sent a true response to the incentive mailing sent by the marketing department, how many of them are divorced males?\n        \n        ### Context:\n        #### Query Context:\n        RESPONSE = 'true'; SEX = 'Male'; MARITAL_STATUS = 'Divorced';\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.ID) FROM Customers AS T1 INNER JOIN Mailings1_2 AS T2 ON T1.ID = T2.REFID WHERE T1.SEX = 'Male' AND T1.MARITAL_STATUS = 'Divorced' AND T2.RESPONSE = 'true'"}],"db_id":"software_company"}335{"prompt":"How many times is the number of Indian cities than Italian cities?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many times is the number of Indian cities than Italian cities?\n        \n        ### Context:\n        #### Query Context:\n        indian refers to country = 'India'; Italian refers to country = 'Italy'; times = Divide(Count(city where country = 'India), Count(city where country = 'Italy'))\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T1.country = 'India', 1, 0)) AS REAL) \/ SUM(IIF(T1.country = 'Italy', 1, 0)) FROM country AS T1 INNER JOIN city AS T2 ON T1.country_id = T2.country_id"}],"db_id":"movie_3"}336{"prompt":"What is the average weight of the players who have won the award of Rookie of the year?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average weight of the players who have won the award of Rookie of the year?\n        \n        ### Context:\n        #### Query Context:\n        \"Rookie of the Year\" is the award; average weight = Divide (Sum(weight), Count(playerID))\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.weight) FROM players AS T1 INNER JOIN awards_players AS T2 ON T1.playerID = T2.playerID WHERE T2.award = 'Rookie of the Year'"}],"db_id":"professional_basketball"}337{"prompt":"What are the names of the products with a profit of no less than 1,000 in one single order?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the names of the products with a profit of no less than 1,000 in one single order?\n        \n        ### Context:\n        #### Query Context:\n        profit of no less than 1,000 refers to Profit > = 1000; names of the products refers to \"Product Name\"\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.`Product Name` FROM west_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T1.Profit > 1000"}],"db_id":"superstore"}338{"prompt":"What are the opening hours of business number 53 on Friday?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the opening hours of business number 53 on Friday?\n        \n        ### Context:\n        #### Query Context:\n        opening hours refers to opening_time; business number refers to business_id; Friday refers to day_of_week = 'Friday';\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.closing_time - T1.opening_time AS \"opening hours\" FROM Business_Hours AS T1 INNER JOIN Days AS T2 ON T1.day_id = T2.day_id WHERE T2.day_of_week LIKE 'Friday' AND T1.business_id = 53"}],"db_id":"public_review_platform"}339{"prompt":"Of the matches that were won by runs by team 1, what percentage have team 1 won the toss and decided to field?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Of the matches that were won by runs by team 1, what percentage have team 1 won the toss and decided to field?\n        \n        ### Context:\n        #### Query Context:\n        won by runs refers to Win_Type = 'runs'; won the toss and decided to field refers to Toss_Winner and Toss_Name = 'field'; percentage = divide(count(Team_1) when Match_Winner = Team_1 and Toss_Winner = Team_1, count(Team_1)) as percentage\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T1.Team_1 = T1.Match_Winner = T1.Toss_Winner THEN 1 ELSE 0 END) AS REAL) * 100 \/ TOTAL(T1.Team_1) FROM `Match` AS T1 INNER JOIN Win_By AS T2 ON T1.Win_Type = T2.Win_Id INNER JOIN Toss_Decision AS T3 ON T1.Toss_Decide = T3.Toss_Id WHERE T3.Toss_Name = 'field' AND T2.Win_Type = 'runs'"}],"db_id":"soccer_2016"}340{"prompt":"Among the businesses in Scottsdale, list the attribute of the business with a high review count.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the businesses in Scottsdale, list the attribute of the business with a high review count.\n        \n        ### Context:\n        #### Query Context:\n        \"Scottsdale\" is the name of city; high review count refers to review_count = 'High'; attribute of the business refers to attribute_name\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.attribute_name FROM Business AS T1 INNER JOIN Business_Attributes AS T2 ON T1.business_id = T2.business_id INNER JOIN Attributes AS T3 ON T2.attribute_id = T3.attribute_id WHERE T1.review_count LIKE 'High' AND T1.city LIKE 'Scottsdale' GROUP BY T3.attribute_name"}],"db_id":"public_review_platform"}341{"prompt":"What are the states with an above-average female population?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the states with an above-average female population?\n        \n        ### Context:\n        #### Query Context:\n        above-average female population refers to female_population > DIVIDE(SUM(female_population), COUNT(state));\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data )"}],"db_id":"address"}342{"prompt":"Identify all restaurants in Contra Costa County by id.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify all restaurants in Contra Costa County by id.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.id_restaurant FROM location AS T1 INNER JOIN geographic AS T2 ON T1.city = T2.city WHERE T2.county = 'contra costa county'"}],"db_id":"restaurant"}343{"prompt":"Name the advisors for students in Year 3 of the program.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the advisors for students in Year 3 of the program.\n        \n        ### Context:\n        #### Query Context:\n        advisors refers to p_id_dummy; students in Year 3 of the program refers to yearsInProgram = 'Year_3'\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.p_id FROM advisedBy AS T1 INNER JOIN person AS T2 ON T1.p_id = T2.p_id WHERE T2.yearsInProgram = 'Year_3'"}],"db_id":"computer_student"}344{"prompt":"What is the precise location of Sac State Union?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the precise location of Sac State Union?\n        \n        ### Context:\n        #### Query Context:\n        precise location = Latitude, Longitude; Sac State Union refers to LocationName = 'Sac State Union';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Latitude, T2.Longitude FROM location AS T1 INNER JOIN geolocation AS T2 ON T1.LocationID = T2.LocationID WHERE T1.LocationName = 'Sac State Union'"}],"db_id":"beer_factory"}345{"prompt":"What is the full name of players origin from Finland?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of players origin from Finland?\n        \n        ### Context:\n        #### Query Context:\n        origin from Finland refers to birthCountry = 'Finland';\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], 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[15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], 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'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], [13, 'year'], [13, 'stint'], [13, 'team ID'], [13, 'shots'], [13, 'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 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'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT firstName, lastName FROM Master WHERE birthCountry = 'Finland'"}],"db_id":"hockey"}346{"prompt":"How many devices belong to model \"A51\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many devices belong to model \"A51\"?\n        \n        ### Context:\n        #### Query Context:\n        model refers to device_model; device_model = 'A51';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(device_id) FROM phone_brand_device_model2 WHERE device_model = 'A51'"}],"db_id":"talkingdata"}347{"prompt":"Who is the winner of the game happened on 2009\/10\/10, between \"East Fife\" and \"Dumbarton\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the winner of the game happened on 2009\/10\/10, between \"East Fife\" and \"Dumbarton\"?\n        \n        ### Context:\n        #### Query Context:\n        2009\/10\/10 is a date; the winner refers to FTR = 'A'; East Fife and Dumbarton are name of teams where HomeTeam = 'East Fife'; AwayTeam = 'Dumbarton';\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN FTR = 'H' THEN 'East Fife' ELSE 'Dumbarton' END WINNER FROM matchs WHERE Date = '2009-10-10' AND HomeTeam = 'East Fife' AND AwayTeam = 'Dumbarton'"}],"db_id":"european_football_1"}348{"prompt":"How many respondents of the mental health survey were diagnosed with 'Substance Use Disorder'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many respondents of the mental health survey were diagnosed with 'Substance Use Disorder'?\n        \n        ### Context:\n        #### Query Context:\n        respondents' and 'users' are synonyms\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(AnswerText) FROM Answer WHERE AnswerText LIKE 'Substance Use Disorder'"}],"db_id":"mental_health_survey"}349{"prompt":"Among all the active customers, how many of them live in Arlington?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the active customers, how many of them live in Arlington?\n        \n        ### Context:\n        #### Query Context:\n        active refers to active = 1; Arlington refers to city = 'Arlington'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.customer_id) FROM address AS T1 INNER JOIN customer AS T2 ON T1.address_id = T2.address_id INNER JOIN city AS T3 ON T1.city_id = T3.city_id WHERE T2.active = 1 AND T3.city = 'Arlington'"}],"db_id":"movie_3"}350{"prompt":"What is the average alcohol content per 12-ounce beer bottle produced by Boston Beer Company?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average alcohol content per 12-ounce beer bottle produced by Boston Beer Company?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'craftbeer', 'table_names_original': ['breweries', 'beers'], 'table_names': ['Breweries', 'Beers'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'city'], [0, 'state'], [1, 'id'], [1, 'brewery_id'], [1, 'abv'], [1, 'ibu'], [1, 'name'], [1, 'style'], [1, 'ounces']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'city'], [0, 'state'], [1, 'id'], [1, 'brewery id'], [1, 'alcohol by volume'], [1, 'International Bitterness Units'], [1, 'name'], [1, 'style'], [1, 'ounces']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'real'], 'primary_keys': [1, 5], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.abv) FROM beers AS T1 INNER JOIN breweries AS T2 ON T1.brewery_id = T2.id WHERE T2.name = 'Boston Beer Company' AND T1.ounces = 12"}],"db_id":"craftbeer"}351{"prompt":"Which product was ordered the most in 2018?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which product was ordered the most in 2018?\n        \n        ### Context:\n        #### Query Context:\n        product refers to Product Name; ordered the most in 2018 refers to MAX(COUNT(OrderNumber)) where SUBSTR(OrderDate, -2) = '18';\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Product Name` FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID WHERE T1.OrderDate LIKE '%\/%\/18' GROUP BY T1._ProductID ORDER BY COUNT(T1._ProductID) DESC LIMIT 1"}],"db_id":"regional_sales"}352{"prompt":"What is the average quantity of Ikura ordered in one order?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average quantity of Ikura ordered in one order?\n        \n        ### Context:\n        #### Query Context:\n        average = AVG(Quantity); Ikura refers to ProductName = 'Ikura';\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T2.Quantity) AS REAL) \/ COUNT(T2.OrderID) FROM Products AS T1 INNER JOIN `Order Details` AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ProductName = 'Ikura'"}],"db_id":"retail_world"}353{"prompt":"What is the prefix for the teacher who handled the 'Reading About Other Cultures' project?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the prefix for the teacher who handled the 'Reading About Other Cultures' project?\n        \n        ### Context:\n        #### Query Context:\n        Reading About Other Cultures' project refers to title = 'Reading About Other Cultures'; prefix for the teacher refers to teacher_prefix\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.teacher_prefix FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T1.title LIKE 'Reading About Other Cultures'"}],"db_id":"donor"}354{"prompt":"Calculate the average of object samples for the image.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average of object samples for the image.\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID));\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(OBJ_SAMPLE_ID) AS REAL) \/ COUNT(DISTINCT IMG_ID) FROM IMG_OBJ"}],"db_id":"image_and_language"}355{"prompt":"What is the total number of games sold in region ID 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the total number of games sold in region ID 1?\n        \n        ### Context:\n        #### Query Context:\n        total number of games sold = MULTIPLY(SUM(num_sales), 100000);\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T.num_sales * 100000) FROM region_sales AS T WHERE T.region_id = 1"}],"db_id":"video_games"}356{"prompt":"Among the current legislators who have served for more than 6 terms, how many of them were born after 1960?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the current legislators who have served for more than 6 terms, how many of them were born after 1960?\n        \n        ### Context:\n        #### Query Context:\n        served for more than 6 terms refers to COUNT(bioguide > 6); born after 1960 refers to birthday_bio > = '1960-01-01'\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(CID) FROM ( SELECT T1.bioguide_id AS CID FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.birthday_bio >= '1960-01-01' GROUP BY T2.bioguide HAVING COUNT(T2.bioguide) > 6 )"}],"db_id":"legislator"}357{"prompt":"How many questions in 2014's survey had more than 200 answers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many questions in 2014's survey had more than 200 answers?\n        \n        ### Context:\n        #### Query Context:\n        2014 refer to SurveyID; COUNT(QuestionID) where COUNT(AnswerText) > 200\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(QuestionID) FROM Answer WHERE SurveyID LIKE 2014 GROUP BY QuestionID ORDER BY COUNT(QuestionID) > 200 LIMIT 1"}],"db_id":"mental_health_survey"}358{"prompt":"Describe the number of postal points and the countries in West Virginia.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Describe the number of postal points and the countries in West Virginia.\n        \n        ### Context:\n        #### Query Context:\n        postal points refer to zip_code; West Virginia is the name of the state, in which name = 'West Virginia';\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.zip_code), COUNT(DISTINCT T2.county) FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'West Virginia'"}],"db_id":"address"}359{"prompt":"Please list the names of the characters in the movie Look Who's Talking.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the names of the characters in the movie Look Who's Talking.\n        \n        ### Context:\n        #### Query Context:\n        movie Look Who's Talking refers to title = 'Look Who's Talking'\n        \n        #### Database Schema:\n        {'db_id': 'movie', 'table_names_original': ['actor', 'movie', 'characters'], 'table_names': ['actor', 'movie', 'characters'], 'column_names_original': [[-1, '*'], [0, 'ActorID'], [0, 'Name'], [0, 'Date of Birth'], [0, 'Birth City'], [0, 'Birth Country'], [0, 'Height (Inches)'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'MovieID'], [1, 'Title'], [1, 'MPAA Rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'Rating Count'], [1, 'Summary'], [2, 'MovieID'], [2, 'ActorID'], [2, 'Character Name'], [2, 'creditOrder'], [2, 'pay'], [2, 'screentime']], 'column_names': [[-1, '*'], [0, 'actor id'], [0, 'Name'], [0, 'date of birth'], [0, 'birth city'], [0, 'birth country'], [0, 'height inches'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'movie id'], [1, 'Title'], [1, 'motion picture association of america rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'rating count'], [1, 'Summary'], [2, 'movie id'], [2, 'actor id'], [2, 'character name'], [2, 'credit order'], [2, 'pay'], [2, 'screentime']], 'column_types': ['text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text'], 'primary_keys': [1, 11, [22, 23]], 'foreign_keys': [[22, 11], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Character Name` FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID WHERE T1.Title = 'Look Who''s Talking'"}],"db_id":"movie"}360{"prompt":"What format does the method number 8's comment have?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What format does the method number 8's comment have?\n        \n        ### Context:\n        #### Query Context:\n        format refers CommentIsXml; method number refers to Method_100k.Id; Method_100k.Id = 8;\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN CommentIsXml = 0 THEN 'isNotXMLFormat' WHEN CommentIsXml = 1 THEN 'isXMLFormat' END format FROM Method WHERE Id = 8"}],"db_id":"codebase_comments"}361{"prompt":"Between match nos. 335989 and 337000, how many times did a batsman score more than 3 runs during over no. 1, ball no. 1, and inning no. 1 of the matches?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Between match nos. 335989 and 337000, how many times did a batsman score more than 3 runs during over no. 1, ball no. 1, and inning no. 1 of the matches?\n        \n        ### Context:\n        #### Query Context:\n        Between match no. 335989 and 337000 refers to 335989 < Match_Id < 337000; batsman score more than 3 runs during over no. 1, ball no. 1, and inning no. 1 of the matches refers to Runs_Scored > 3 and\u00a0 Over_Id = 1 and Ball_Id = 1 and Innings_No = 1\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN Runs_Scored > 3 THEN 1 ELSE 0 END) FROM Batsman_Scored WHERE 335989 < Match_Id < 337000 AND Innings_No = 1 AND Over_Id = 1 AND Ball_Id = 1"}],"db_id":"soccer_2016"}362{"prompt":"Among the players who died in Massachussets, how many of them have won an award?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the players who died in Massachussets, how many of them have won an award?\n        \n        ### Context:\n        #### Query Context:\n        died in Massachussets refers to deathState = 'Massachussets'\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 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'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.playerID) FROM Master AS T1 INNER JOIN AwardsPlayers AS T2 ON T1.playerID = T2.playerID WHERE T1.deathState = 'MA'"}],"db_id":"hockey"}363{"prompt":"List the name, address, and phone number of companies that supply products for more than thirty dollars per unit.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the name, address, and phone number of companies that supply products for more than thirty dollars per unit.\n        \n        ### Context:\n        #### Query Context:\n        more than thirty dollars per unit refers to UnitPrice > 30\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.CompanyName, T2.Address, T2.Phone FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T1.UnitPrice > 30"}],"db_id":"retail_world"}364{"prompt":"Identify the players with the same height as Brian Gionta. How tall are they?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the players with the same height as Brian Gionta. How tall are they?\n        \n        ### Context:\n        #### Query Context:\n        players refers to PlayerName; height refers to height_in_cm;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.PlayerName, T1.height_in_cm FROM height_info AS T1 INNER JOIN PlayerInfo AS T2 ON T1.height_id = T2.height WHERE T2.height = ( SELECT height FROM PlayerInfo WHERE PlayerName = 'Brian Gionta' )"}],"db_id":"ice_hockey_draft"}365{"prompt":"Among the employees who are married and wish to receive e-mail promotions, how much higher is their highest pay rate from the average pay rate?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the employees who are married and wish to receive e-mail promotions, how much higher is their highest pay rate from the average pay rate?\n        \n        ### Context:\n        #### Query Context:\n        married refers to MaritalStatus = 'M'; Contact does wish to receive e-mail promotions from Adventure Works refers to EmailPromotion = 1; Average = Divide (Sum(Rate (MaritalStatus = 'M' & EmailPromotion = 1))), Count (BusinessEntityID (MaritalStatus = 'M' & EmailPromotion = 1)); MAX(Rate (MaritalStatus = 'M' & EmailPromotion = 1) - Average;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 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357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(T1.Rate) - SUM(T1.Rate) \/ COUNT(T1.BusinessEntityID) FROM EmployeePayHistory AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN Employee AS T3 ON T2.BusinessEntityID = T3.BusinessEntityID WHERE T2.EmailPromotion = 2 AND T3.MaritalStatus = 'M'"}],"db_id":"works_cycles"}366{"prompt":"List the full names of Republican legislators who have a nickname.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the full names of Republican legislators who have a nickname.\n        \n        ### Context:\n        #### Query Context:\n        full names refers to official_full_name; Republican refers to party = 'Republican'; nickname refers to nickname_name\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.official_full_name FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.party = 'Republican' AND T1.nickname_name IS NOT NULL GROUP BY T1.official_full_name"}],"db_id":"legislator"}367{"prompt":"Among the orders made by Aimee Bixby, how many of them included at least one kind of product under the category \"Furniture\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the orders made by Aimee Bixby, how many of them included at least one kind of product under the category \"Furniture\"?\n        \n        ### Context:\n        #### Query Context:\n        made by Aimee Bixby refers to \"Customer Name\" = 'Aimee Bixby';\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.`Order ID`) FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T3.Category = 'Furniture' AND T1.`Customer Name` = 'Aimee Bixby'"}],"db_id":"superstore"}368{"prompt":"In which region were the assistant location managers born?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which region were the assistant location managers born?\n        \n        ### Context:\n        #### Query Context:\n        region refers to birth_region; assistant location manager refers to role = 'assistant location manager'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.birth_region FROM Credit AS T1 INNER JOIN Person AS T2 ON T2.person_id = T1.person_id WHERE T1.role = 'president of NBC West Coast'"}],"db_id":"law_episode"}369{"prompt":"How many users who are between 20 and 60 use phone brand of TCL?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many users who are between 20 and 60 use phone brand of TCL?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.device_id) FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T1.age BETWEEN 20 AND 60 AND T2.phone_brand = 'TCL'"}],"db_id":"talkingdata"}370{"prompt":"How many biword pairs contain the word \"base\" as the second word?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many biword pairs contain the word \"base\" as the second word?\n        \n        ### Context:\n        #### Query Context:\n        base refers to word = 'base'; SUM(w2nd) where w2nd = wid for word = 'base'\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(w1st) FROM biwords WHERE w2nd = ( SELECT wid FROM words WHERE word = 'base' )"}],"db_id":"language_corpus"}371{"prompt":"Which state is \"Mercer University\" located in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which state is \"Mercer University\" located in?\n        \n        ### Context:\n        #### Query Context:\n        Mercer University refers to chronname = 'Mercer University';\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T FROM ( SELECT DISTINCT CASE WHEN chronname = 'Mercer University' THEN state ELSE NULL END AS T FROM institution_details ) WHERE T IS NOT NULL"}],"db_id":"college_completion"}372{"prompt":"How many customers with a canceled shipment have a credit limit greater than 115,000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many customers with a canceled shipment have a credit limit greater than 115,000?\n        \n        ### Context:\n        #### Query Context:\n        cancelled shipment refers to status = 'cancelled'; creditLimit > 115000;\n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.customerNumber) FROM customers AS T1 INNER JOIN orders AS T2 ON T1.customerNumber = T2.customerNumber WHERE T2.status = 'Cancelled' AND T1.creditLimit > 115000"}],"db_id":"car_retails"}373{"prompt":"Among the suppliers in the European region, what percentage have a below-average account balance?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the suppliers in the European region, what percentage have a below-average account balance?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(s_acctbal < AVG(s_acctbal)), COUNT(s_suppkey)) as percentage where r_name = 'EUROPE';\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T3.s_acctbal < ( SELECT AVG(supplier.s_acctbal) FROM supplier ), 1, 0)) AS REAL) * 100 \/ COUNT(T1.n_nationkey) FROM nation AS T1 INNER JOIN region AS T2 ON T1.n_regionkey = T2.r_regionkey INNER JOIN supplier AS T3 ON T1.n_nationkey = T3.s_nationkey WHERE T2.r_name = 'EUROPE'"}],"db_id":"retails"}374{"prompt":"How many disabled male students joined an organization?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many disabled male students joined an organization?\n        \n        ### Context:\n        #### Query Context:\n        organization refers to organ; disabled male students refers to disabled.name who are IN male.name;\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.name) FROM disabled AS T1 LEFT JOIN male AS T2 ON T2.name = T1.name INNER JOIN enlist AS T3 ON T3.name = T2.name"}],"db_id":"student_loan"}375{"prompt":"For all the users who have been asked \"Have you ever been diagnosed with a mental health disorder?\", how many of them said \"Yes\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For all the users who have been asked \"Have you ever been diagnosed with a mental health disorder?\", how many of them said \"Yes\"?\n        \n        ### Context:\n        #### Query Context:\n        have asked refers to questiontext; said 'Yes' refers to AnswerText = 'Yes'\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.UserID) FROM Answer AS T1 INNER JOIN Question AS T2 ON T1.QuestionID = T2.questionid WHERE T2.questiontext LIKE 'Have you ever been diagnosed with a mental health disorder?' AND T1.AnswerText = 'Yes'"}],"db_id":"mental_health_survey"}376{"prompt":"Please name any three parts that have an available quantity of more than 9998.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please name any three parts that have an available quantity of more than 9998.\n        \n        ### Context:\n        #### Query Context:\n        part name refers to p_name; an available quantity of more than 9998 refers to ps_availqty > 9998\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.p_name FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey WHERE T2.ps_availqty > 9998 LIMIT 3"}],"db_id":"retails"}377{"prompt":"List the full name of employees and titles who have to report to Sales Manager.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the full name of employees and titles who have to report to Sales Manager.\n        \n        ### Context:\n        #### Query Context:\n        Sales Manager refers to Title = 'Sales Manager'; full name refers to FirstName, LastName\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT FirstName, LastName, Title FROM Employees WHERE ReportsTo = ( SELECT EmployeeID FROM Employees WHERE Title = 'Sales Manager' )"}],"db_id":"retail_world"}378{"prompt":"List the name of England players.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the name of England players.\n        \n        ### Context:\n        #### Query Context:\n        England players refers to Country_Name = 'England'\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Player_Name FROM Player AS T1 INNER JOIN Country AS T2 ON T1.Country_Name = T2.Country_ID WHERE T2.Country_Name = 'England'"}],"db_id":"soccer_2016"}379{"prompt":"Tally the player IDs of \"Man of the Series\" awardees for the seasons from 2011 to 2015.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tally the player IDs of \"Man of the Series\" awardees for the seasons from 2011 to 2015.\n        \n        ### Context:\n        #### Query Context:\n        seasons from 2011 to 2015 refers to 2011 < Season_Year < 2015\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Man_of_the_Series FROM Season WHERE 2011 < Season_Year < 2015"}],"db_id":"soccer_2016"}380{"prompt":"How many Belgian men have competed in an Olympic Games?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Belgian men have competed in an Olympic Games?\n        \n        ### Context:\n        #### Query Context:\n        Belgian men refer to gender = 'M' where region_name = 'Belgium';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.person_id) FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.region_name = 'Belgium' AND T3.gender = 'M'"}],"db_id":"olympics"}381{"prompt":"How many Folsom customers prefer to pay with Visa?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Folsom customers prefer to pay with Visa?\n        \n        ### Context:\n        #### Query Context:\n        Folsom refers to City = 'Folsom'; Visa refers to CreditCardType = 'Visa';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.CustomerID) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.City = 'Folsom' AND T2.CreditCardType = 'Visa'"}],"db_id":"beer_factory"}382{"prompt":"Please list the emails of the clients whose complaint date received is 7\/3\/2014.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the emails of the clients whose complaint date received is 7\/3\/2014.\n        \n        ### Context:\n        #### Query Context:\n        7\/3\/2014 refers to Date received = '2014-07-03'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.email FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.`Date received` = '2014-07-03'"}],"db_id":"retail_complains"}383{"prompt":"What is the school and gender of student34?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the school and gender of student34?\n        \n        ### Context:\n        #### Query Context:\n        male.name = 'student34' means student34's gender is male; if 'student34' can't be found in 'male.name', it means student34 is female; student34 is a name of student;\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.school , IIF(T3.name IS NULL, 'female', 'male') AS gender FROM enrolled AS T1 INNER JOIN person AS T2 ON T1.name = T2.name LEFT JOIN male AS T3 ON T2.name = T3.name WHERE T2.name = 'student34'"}],"db_id":"student_loan"}384{"prompt":"How many objects are there in the attribute class id with the highest number of objects?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many objects are there in the attribute class id with the highest number of objects?\n        \n        ### Context:\n        #### Query Context:\n        objects refers to OBJ_SAMPLE_ID; attribute class id with the highest number of objects refers to max(COUNT(ATT_CLASS_ID))\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(ATT_CLASS_ID) FROM IMG_OBJ_att GROUP BY IMG_ID ORDER BY COUNT(ATT_CLASS_ID) DESC LIMIT 1"}],"db_id":"image_and_language"}385{"prompt":"How many black-colored products are there that cannot be sold?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many black-colored products are there that cannot be sold?\n        \n        ### Context:\n        #### Query Context:\n        cannot be sold means product is not a salable item which refers to FinishedGoodsFlag = 0\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 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'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(ProductID) FROM Product WHERE FinishedGoodsFlag = 0 AND Color = 'Black'"}],"db_id":"works_cycles"}386{"prompt":"What is the name of the product with the highest net profit?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the product with the highest net profit?\n        \n        ### Context:\n        #### Query Context:\n        highest net profit = Max(Subtract (Unit Price, Unit Cost)); name of product refers to Product Name\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Product Name` FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID ORDER BY REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '') DESC LIMIT 1"}],"db_id":"regional_sales"}387{"prompt":"Which product has the highest net profit in 2019?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which product has the highest net profit in 2019?\n        \n        ### Context:\n        #### Query Context:\n        net profit can be computed as SUBTRACT(Unit Price, Unit Cost); the highest net profit in 2019 refers to MAX(Net Profit) where OrderDate LIKE '%\/19'; product refers to Product Name;\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Product Name` FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID WHERE T1.OrderDate LIKE '%\/%\/19' ORDER BY REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '') DESC LIMIT 1"}],"db_id":"regional_sales"}388{"prompt":"What is the geographic identifier and income of the oldest customer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the geographic identifier and income of the oldest customer?\n        \n        ### Context:\n        #### Query Context:\n        the oldest customer refers to MAX(age); geographic identifier refers to GEOID; income refers to INCOME_K;\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.GEOID, T2.INCOME_K FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID ORDER BY T1.age DESC LIMIT 1"}],"db_id":"software_company"}389{"prompt":"What is the name of the publisher who published Agatha Christie's first book?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the publisher who published Agatha Christie's first book?\n        \n        ### Context:\n        #### Query Context:\n        \"Agatha Christie\" is the author_name; name of publisher refers to publisher_name; first book refers to Min(publication_date)\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T4.publisher_name FROM book AS T1 INNER JOIN book_author AS T2 ON T1.book_id = T2.book_id INNER JOIN author AS T3 ON T3.author_id = T2.author_id INNER JOIN publisher AS T4 ON T4.publisher_id = T1.publisher_id WHERE T3.author_name = 'Agatha Christie' ORDER BY T1.publication_date ASC LIMIT 1"}],"db_id":"books"}390{"prompt":"What is the title of the 3 worst rated episodes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of the 3 worst rated episodes?\n        \n        ### Context:\n        #### Query Context:\n        worst rated refers to min(rating)\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT title FROM Episode ORDER BY rating LIMIT 3"}],"db_id":"law_episode"}391{"prompt":"Please list the top three employees with the most unused sick leave along with their position titles.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the top three employees with the most unused sick leave along with their position titles.\n        \n        ### Context:\n        #### Query Context:\n        employees refers to BusinessEntityID; most unused sick leave refers to MAX(SickLeaveHours); position title refers to JobTitle\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product 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What is the projected yearly sales quota in 2011 for this person?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which sales person achieved the highest sales YTD? What is the projected yearly sales quota in 2011 for this person?\n        \n        ### Context:\n        #### Query Context:\n        Sales people refer to PersonType = 'SP'; projected yearly sales refers to SalesQuota\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 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[196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.BusinessEntityID, SUM(T1.SalesQuota) FROM SalesPerson AS T1 INNER JOIN SalesPersonQuotaHistory AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE STRFTIME('%Y', T2.QuotaDate) = '2011' GROUP BY T1.BusinessEntityID ORDER BY SUM(T1.SalesYTD) DESC LIMIT 1"}],"db_id":"works_cycles"}393{"prompt":"Which of Cooperativa de Quesos 'Las Cabras' products have a unit price greater than 20?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which of Cooperativa de Quesos 'Las Cabras' products have a unit price greater than 20?\n        \n        ### Context:\n        #### Query Context:\n        Cooperativa de Quesos 'Las Cabras'' is a CompanyName; unit price greater than 20 refers to UnitPrice > 20\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ProductName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T2.CompanyName LIKE 'Cooperativa de Quesos%' AND T1.UnitPrice > 20"}],"db_id":"retail_world"}394{"prompt":"How many paragraphs are there in Act 1, Scene 1 in Twelfth Night?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many paragraphs are there in Act 1, Scene 1 in Twelfth Night?\n        \n        ### Context:\n        #### Query Context:\n        Twelfth Night refers to Title = 'Twelfth Night'\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T3.ParagraphNum) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id INNER JOIN paragraphs AS T3 ON T2.id = T3.chapter_id WHERE T2.Act = 1 AND T2.Scene = 1 AND T1.Title = 'Twelfth Night'"}],"db_id":"shakespeare"}395{"prompt":"Who is the \"Next-Door Bike Store\" point of contact?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the \"Next-Door Bike Store\" point of contact?\n        \n        ### Context:\n        #### Query Context:\n        Next-Door Bike Store is name of a store\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 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'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM ContactType AS T1 INNER JOIN BusinessEntityContact AS T2 ON T1.ContactTypeID = T2.ContactTypeID INNER JOIN Store AS T3 ON T2.BusinessEntityID = T3.BusinessEntityID WHERE T3.Name = 'Next-Door Bike Store'"}],"db_id":"works_cycles"}396{"prompt":"List down all the tweet text posted from Australia.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down all the tweet text posted from Australia.\n        \n        ### Context:\n        #### Query Context:\n        \"Australia\" is the Country\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.text FROM twitter AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID WHERE T2.Country = 'Australia'"}],"db_id":"social_media"}397{"prompt":"What is the highest price at which a customer bought the book 'The Prophet'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the highest price at which a customer bought the book 'The Prophet'?\n        \n        ### Context:\n        #### Query Context:\n        \"The Prophet\" is the title of the book; highest price refers to Max(price)\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(T2.price) FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id WHERE T1.title = 'The Prophet'"}],"db_id":"books"}398{"prompt":"Provide the genre of a movie title with a tagline of \"A long time ago in a galaxy far, far away\u2026\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the genre of a movie title with a tagline of \"A long time ago in a galaxy far, far away\u2026\".\n        \n        ### Context:\n        #### Query Context:\n        genre refers to genre_name; tagline of \"A long time ago in a galaxy far, far away\u2026\" refers to tagline = 'A long time ago in a galaxy far, far away\u2026'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.genre_name FROM movie AS T1 INNER JOIN movie_genres AS T2 ON T1.movie_id = T2.movie_id INNER JOIN genre AS T3 ON T3.genre_id = T2.genre_id WHERE T1.tagline = 'A long time ago in a galaxy far, far away...'"}],"db_id":"movies_4"}399{"prompt":"List the store located cities with regions in no water area of California state.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the store located cities with regions in no water area of California state.\n        \n        ### Context:\n        #### Query Context:\n        cities refer to City Name; no water area refers to Water Area = 0;\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.`City Name` FROM Regions AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StateCode = T1.StateCode WHERE T2.State = 'California' AND T2.`Water Area` = '0'"}],"db_id":"regional_sales"}400{"prompt":"Which near-death incident did a policeman by the name of Ruben Fredirick look into? What is the victim in this incident's race and gender?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which near-death incident did a policeman by the name of Ruben Fredirick look into? What is the victim in this incident's race and gender?\n        \n        ### Context:\n        #### Query Context:\n        near-death refers to subject_statuses = 'Deceased Injured'; incident refers to case_number; Ruben Fredirick refers to full_name = 'Ruben Fredirick'\n        \n        #### Database Schema:\n        {'db_id': 'shooting', 'table_names_original': ['incidents', 'officers', 'subjects'], 'table_names': ['Incidents', 'officers', 'subjects'], 'column_names_original': [[-1, '*'], [0, 'case_number'], [0, 'date'], [0, 'location'], [0, 'subject_statuses'], [0, 'subject_weapon'], [0, 'subjects'], [0, 'subject_count'], [0, 'officers'], [1, 'case_number'], [1, 'race'], [1, 'gender'], [1, 'last_name'], [1, 'first_name'], [1, 'full_name'], [2, 'case_number'], [2, 'race'], [2, 'gender'], [2, 'last_name'], [2, 'first_name'], [2, 'full_name']], 'column_names': [[-1, '*'], [0, 'case number'], [0, 'date'], [0, 'location'], [0, 'subject statuses'], [0, 'subject weapon'], [0, 'subjects'], [0, 'subject_count'], [0, 'officers'], [1, 'case number'], [1, 'race'], [1, 'gender'], [1, 'last name'], [1, 'first name'], [1, 'full name'], [2, 'case number'], [2, 'race'], [2, 'gender'], [2, 'last name'], [2, 'first name'], [2, 'full name']], 'column_types': ['text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[9, 1], [15, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.case_number, T3.race, T3.gender FROM incidents AS T1 INNER JOIN officers AS T2 ON T1.case_number = T2.case_number INNER JOIN subjects AS T3 ON T1.case_number = T3.case_number WHERE T2.first_name = 'Fredirick' AND T2.last_name = 'Ruben'"}],"db_id":"shooting"}401{"prompt":"What is the shape of the tail car on train no.1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the shape of the tail car on train no.1?\n        \n        ### Context:\n        #### Query Context:\n        train no.1 refers to train_id = 1; tail car refers to position = 4\n        \n        #### Database Schema:\n        {'db_id': 'trains', 'table_names_original': ['cars', 'trains'], 'table_names': ['cars', 'trains'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'train_id'], [0, 'position'], [0, 'shape'], [0, 'len'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load_num'], [1, 'id'], [1, 'direction']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'train id'], [0, 'position'], [0, 'shape'], [0, 'length'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load number'], [1, 'id'], [1, 'direction']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 11], 'foreign_keys': [[2, 11]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT shape FROM cars WHERE train_id = 1 AND position = 4"}],"db_id":"trains"}402{"prompt":"Which state did Veronica Grace Boland represent and which party is she affiliated?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which state did Veronica Grace Boland represent and which party is she affiliated?\n        \n        ### Context:\n        #### Query Context:\n        Veronica Grace Boland is a full name; full name refers to first_name, middle_name, last_name; party affiliated refers to party\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.state, T2.party FROM historical AS T1 INNER JOIN `historical-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.first_name OR T1.middle_name OR T1.last_name = 'VeronicaGraceBoland'"}],"db_id":"legislator"}403{"prompt":"Calculate the average production rate per year from 1971 to 1980. Among them, name the cars with a weight of fewer than 1800 lbs.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average production rate per year from 1971 to 1980. Among them, name the cars with a weight of fewer than 1800 lbs.\n        \n        ### Context:\n        #### Query Context:\n        from 1971 to 1980 refers to model_year between 1971 and 1980; average production rate per year = divide(count(ID where model_year between 1971 and 1980), 9); car's name refers to car_name; a weight of fewer than 1800 lbs refers to weight < 1800\n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(T1.ID) AS REAL) \/ 9 FROM production AS T1 INNER JOIN data AS T2 ON T2.ID = T1.ID WHERE T1.model_year BETWEEN 1971 AND 1980 UNION ALL SELECT DISTINCT T2.car_name FROM production AS T1 INNER JOIN data AS T2 ON T2.ID = T1.ID WHERE T1.model_year BETWEEN 1971 AND 1980 AND T2.weight < 1800"}],"db_id":"cars"}404{"prompt":"What percentage of customers engaged in the household segment are from Iran?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of customers engaged in the household segment are from Iran?\n        \n        ### Context:\n        #### Query Context:\n        the household segment refers to c_mktsegment = 'HOUSEHOLD'; Iran is nation name which refers to n_name = 'IRAN'; percentage = divide(count(c_custkey where n_name = 'IRAN'), count(c_custkey)) where c_mktsegment = 'HOUSEHOLD' * 100%\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T2.n_name = 'IRAN', 1, 0)) AS REAL) * 100 \/ COUNT(T2.n_name) FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T1.c_mktsegment = 'HOUSEHOLD'"}],"db_id":"retails"}405{"prompt":"Give the number of female employees.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the number of female employees.\n        \n        ### Context:\n        #### Query Context:\n        number of female employees means COUNT(gender = 'F')\n        \n        #### Database Schema:\n        {'db_id': 'human_resources', 'table_names_original': ['location', 'position', 'employee'], 'table_names': ['location', 'position', 'employee'], 'column_names_original': [[-1, '*'], [0, 'locationID'], [0, 'locationcity'], [0, 'address'], [0, 'state'], [0, 'zipcode'], [0, 'officephone'], [1, 'positionID'], [1, 'positiontitle'], [1, 'educationrequired'], [1, 'minsalary'], [1, 'maxsalary'], [2, 'ssn'], [2, 'lastname'], [2, 'firstname'], [2, 'hiredate'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'positionID'], [2, 'locationID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'location city'], [0, 'address'], [0, 'state'], [0, 'zip code'], [0, 'office phone'], [1, 'position id'], [1, 'position title'], [1, 'education required'], [1, 'minimum salary'], [1, 'maximum salary'], [2, 'social security number'], [2, 'last name'], [2, 'first name'], [2, 'hire date'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'position id'], [2, 'location id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 7, 12], 'foreign_keys': [[19, 7], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM employee WHERE gender = 'F'"}],"db_id":"human_resources"}406{"prompt":"Of all the orders placed and shipped throughout the year 2005, what percentage of those orders corresponds to customer number 186?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Of all the orders placed and shipped throughout the year 2005, what percentage of those orders corresponds to customer number 186?\n        \n        ### Context:\n        #### Query Context:\n        shipped orders refers to status = 'shipped'; year(shippedDate) = 2005; percentage = DIVIDE(SUM(customerNumber = 186)), COUNT(orderNumber)) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN customerNumber = 186 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(orderNumber) FROM orders WHERE status = 'Shipped' AND shippedDate BETWEEN '2005-01-01' AND '2005-12-31'"}],"db_id":"car_retails"}407{"prompt":"Sate the order number and calculate the net profit for each order under Joshua Bennett.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Sate the order number and calculate the net profit for each order under Joshua Bennett.\n        \n        ### Context:\n        #### Query Context:\n        net profit can be computed as SUBTRACT(Unit Price, Unit Cost); Joshua Bennett is the name of Sales Team;\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.OrderNumber , REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '')  FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T2.`Sales Team` = 'Joshua Bennett'"}],"db_id":"regional_sales"}408{"prompt":"How many inspections were done under the personnel category?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many inspections were done under the personnel category?\n        \n        ### Context:\n        #### Query Context:\n        under the personnel category refers to category = 'Personnel'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.inspection_id) FROM violation AS T1 INNER JOIN inspection_point AS T2 ON T1.point_id = T2.point_id WHERE T2.category = 'Personnel'"}],"db_id":"food_inspection_2"}409{"prompt":"List all Zimbabwean players.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all Zimbabwean players.\n        \n        ### Context:\n        #### Query Context:\n        Zimbabwean refers to Country_Name = 'Zimbabwea'; players refers to Player_Name\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Player_Name FROM Player AS T1 INNER JOIN Country AS T2 ON T1.Country_Name = T2.Country_Id WHERE T2.Country_Name = 'Zimbabwea'"}],"db_id":"soccer_2016"}410{"prompt":"What are the names of the movie that was rated by the user between 1\/1\/2013 to 12\/31\/2013 by the user who created the list \"100 Greatest Living American Filmmakers\"? Calculate for the average rating score of those movies in 2013.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the names of the movie that was rated by the user between 1\/1\/2013 to 12\/31\/2013 by the user who created the list \"100 Greatest Living American Filmmakers\"? Calculate for the average rating score of those movies in 2013.\n        \n        ### Context:\n        #### Query Context:\n        Between 1\/1\/2013 to 12\/31\/2013 refer to rating_timestamp_utc; 100 Greatest Living American Filmmakers refer to list_title; average rating score refer to DIVIDE( ADD(rating_score where rating_timestamp_utc = '1\/1\/2013-12\/31\/2013'), COUNT(rating_timestamp_utc = '1\/1\/2013-12\/31\/2013'))\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.movie_title FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id INNER JOIN lists AS T3 ON T3.user_id = T1.user_id WHERE T1.rating_timestamp_utc BETWEEN '2013-01-01' AND '2013-12-31' AND T3.list_title = '100 Greatest Living American Filmmakers'"}],"db_id":"movie_platform"}411{"prompt":"Among the papers published in the journal \"Molecular Brain\", how many of them were published in the year 2011?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the papers published in the journal \"Molecular Brain\", how many of them were published in the year 2011?\n        \n        ### Context:\n        #### Query Context:\n        \"Molecular Brain\" is the FullName of journal\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.Id) FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T2.Year = 2011 AND T1.FullName = 'Molecular Brain'"}],"db_id":"authors"}412{"prompt":"In how many categories were podcast reviews created in the last six months of 2016? List them.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In how many categories were podcast reviews created in the last six months of 2016? List them.\n        \n        ### Context:\n        #### Query Context:\n        created in last six months of 2016 refers to created_at BETWEEN '2016-07-01T00:00:00-07:00' and '2016-12-31T23:59:59-07:00'\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.category) FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.created_at BETWEEN '2016-07-01T00:00:00-07:00' AND '2016-12-31T23:59:59-07:00'"}],"db_id":"music_platform_2"}413{"prompt":"Which publisher has published the most games in the 'Puzzle' genre?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which publisher has published the most games in the 'Puzzle' genre?\n        \n        ### Context:\n        #### Query Context:\n        which publisher refers to publisher_name; publisher that has published the most games refers to MAX(COUNT(publisher_name)); puzzle genre refers to genre_name = 'Puzzle';\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.publisher_name FROM ( SELECT T1.publisher_name, COUNT(T3.id) FROM publisher AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.publisher_id INNER JOIN game AS T3 ON T2.game_id = T3.id INNER JOIN genre AS T4 ON T3.genre_id = T4.id WHERE T4.genre_name = 'Puzzle' GROUP BY T1.publisher_name ORDER BY COUNT(T3.id) DESC LIMIT 1 ) t"}],"db_id":"video_games"}414{"prompt":"Who are the employees that submitted resume to Human Resource Department and got hired? State the last name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who are the employees that submitted resume to Human Resource Department and got hired? State the last name.\n        \n        ### Context:\n        #### Query Context:\n        employees that submitted resume to Human Resource Department and got hired refers to BusinessEntittyID NOT null;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 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[218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.LastName FROM Employee AS T1 INNER JOIN JobCandidate AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN Person AS T3 ON T1.BusinessEntityID = T3.BusinessEntityID WHERE T1.BusinessEntityID IN (212, 274)"}],"db_id":"works_cycles"}415{"prompt":"List down the cities belongs to the country that has surface area greater than 7000000.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the cities belongs to the country that has surface area greater than 7000000.\n        \n        ### Context:\n        #### Query Context:\n        surface area greater than 7000000 refers to SurfaceArea > 7000000;\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name, T1.Name FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T2.SurfaceArea > 7000000"}],"db_id":"world"}416{"prompt":"What is the average age of the athletes from the United States of America who competed in the 2016 Summer Olympics?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average age of the athletes from the United States of America who competed in the 2016 Summer Olympics?\n        \n        ### Context:\n        #### Query Context:\n        AVG(age) where games_name = '2016 Summer' and region_name = 'USA';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.age) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person_region AS T3 ON T2.person_id = T3.person_id INNER JOIN noc_region AS T4 ON T3.region_id = T4.id WHERE T1.games_name = '2016 Summer' AND T4.region_name = 'USA'"}],"db_id":"olympics"}417{"prompt":"What is the average salary of the worst performing managers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average salary of the worst performing managers?\n        \n        ### Context:\n        #### Query Context:\n        the worst performing refers to performance = 'Poor'; manager is a positiontitle; average salary refers to AVG(salary)\n        \n        #### Database Schema:\n        {'db_id': 'human_resources', 'table_names_original': ['location', 'position', 'employee'], 'table_names': ['location', 'position', 'employee'], 'column_names_original': [[-1, '*'], [0, 'locationID'], [0, 'locationcity'], [0, 'address'], [0, 'state'], [0, 'zipcode'], [0, 'officephone'], [1, 'positionID'], [1, 'positiontitle'], [1, 'educationrequired'], [1, 'minsalary'], [1, 'maxsalary'], [2, 'ssn'], [2, 'lastname'], [2, 'firstname'], [2, 'hiredate'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'positionID'], [2, 'locationID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'location city'], [0, 'address'], [0, 'state'], [0, 'zip code'], [0, 'office phone'], [1, 'position id'], [1, 'position title'], [1, 'education required'], [1, 'minimum salary'], [1, 'maximum salary'], [2, 'social security number'], [2, 'last name'], [2, 'first name'], [2, 'hire date'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'position id'], [2, 'location id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 7, 12], 'foreign_keys': [[19, 7], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL)) FROM employee AS T1 INNER JOIN position AS T2  ON T1.positionID = T2.positionID WHERE T1.performance = 'Poor' AND T2.positiontitle = 'Manager'"}],"db_id":"human_resources"}418{"prompt":"List all the titles of the Spanish books published by Alfaguara.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the titles of the Spanish books published by Alfaguara.\n        \n        ### Context:\n        #### Query Context:\n        \"Spanish\" is the language_name; 'Alfaguara' is the publisher_name\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.title FROM book_language AS T1 INNER JOIN book AS T2 ON T2.language_id = T1.language_id INNER JOIN publisher AS T3 ON T3.publisher_id = T2.publisher_id WHERE T1.language_name = 'Spanish' AND T3.publisher_name = 'Alfaguara' GROUP BY T2.title"}],"db_id":"books"}419{"prompt":"Which director has made the most movies?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which director has made the most movies?\n        \n        ### Context:\n        #### Query Context:\n        the most movies refers to MAX(COUNT(name));\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT director, COUNT(name) FROM director GROUP BY director ORDER BY COUNT(name) DESC LIMIT 1"}],"db_id":"disney"}420{"prompt":"List at least 3 categories with the lowest number of users.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List at least 3 categories with the lowest number of users.\n        \n        ### Context:\n        #### Query Context:\n        lowest number of users refers to MIN(COUNT(label_id));\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.category FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id ORDER BY T2.label_id LIMIT 3"}],"db_id":"talkingdata"}421{"prompt":"What is the percentage of businesses with \"Good for Kids\" attribute over the other attributes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of businesses with \"Good for Kids\" attribute over the other attributes?\n        \n        ### Context:\n        #### Query Context:\n        \"Good for Kids\" attribute refers to attribute_name = 'Good for Kids' AND attribute_value = 'true'; Calculation = DIVIDE(SUM(attribute_name = 'Good for Kids' AND attribute_value = 'true')), SUM(business_id) * 100\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN attribute_name = 'Good for Kids' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.business_id) FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T2.attribute_value = 'true'"}],"db_id":"public_review_platform"}422{"prompt":"Please list the first 3 teams that got the most penalty minutes in 2006.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the first 3 teams that got the most penalty minutes in 2006.\n        \n        ### Context:\n        #### Query Context:\n        the first 3 teams that got the most penalty minutes refer to name where MAX(PIM) limit to 3; year = 2006;\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 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[19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power 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'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on 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[16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals 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'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT name FROM Teams WHERE year = 2006 GROUP BY tmID, name ORDER BY SUM(PIM) DESC LIMIT 3"}],"db_id":"hockey"}423{"prompt":"Identify the number of movies rented by Maria Miller.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the number of movies rented by Maria Miller.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.rental_id) FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id WHERE T1.first_name = 'Maria' AND T1.last_name = 'Miller'"}],"db_id":"movie_3"}424{"prompt":"Tell me the reason for Lavelle Vandervort's encounter on 11\/20\/2013?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tell me the reason for Lavelle Vandervort's encounter on 11\/20\/2013?\n        \n        ### Context:\n        #### Query Context:\n        reason for encounter refers to encounters.REASONDESCRIPTION; on 11\/20\/2013 refers to encounters.DATE = '2013-11-20';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.REASONDESCRIPTION FROM patients AS T1 INNER JOIN encounters AS T2 ON T1.patient = T2.PATIENT WHERE T2.DATE = '2013-11-20' AND T1.first = 'Lavelle' AND T1.last = 'Vandervort'"}],"db_id":"synthea"}425{"prompt":"Name the title of the top three releases with the highest number of downloads.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the title of the top three releases with the highest number of downloads.\n        \n        ### Context:\n        #### Query Context:\n        title refers to groupName; the highest number of downloads refers to MAX(totalSnatched);\n        \n        #### Database Schema:\n        {'db_id': 'music_tracker', 'table_names_original': ['torrents', 'tags'], 'table_names': ['torrents', 'tags'], 'column_names_original': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_names': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_types': ['text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [7, 8], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT groupName FROM torrents ORDER BY totalSnatched DESC LIMIT 3"}],"db_id":"music_tracker"}426{"prompt":"Among the customers with an average income per inhabitant above 3000, what percentage are in their eighties?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the customers with an average income per inhabitant above 3000, what percentage are in their eighties?\n        \n        ### Context:\n        #### Query Context:\n        average income per inhabitant above 3000 refers to INCOME_K > 3000; eighties refer to age BETWEEN 80 AND 89; DIVIDE(COUNT(INCOME_K > 3000 and age BETWEEN 80 AND 89), COUNT(INCOME_K > 3000 )) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.age BETWEEN 80 AND 89 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.ID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T2.INCOME_K > 3000"}],"db_id":"software_company"}427{"prompt":"Who is the director of the most popular movie of all time and when was it released? Indicate the average rating score of the users who were on a trialist when they rated the movie.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the director of the most popular movie of all time and when was it released? Indicate the average rating score of the users who were on a trialist when they rated the movie.\n        \n        ### Context:\n        #### Query Context:\n        most popular movie of all time refers to MAX(movie_popularity); a trialist refers to user_trialist = 1; average rating score = AVG(rating_score)\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.director_name, T1.movie_release_year , SUM(T2.rating_score) \/ COUNT(T2.user_id) FROM movies AS T1 INNER JOIN ratings AS T2 ON T1.movie_id = T2.movie_id WHERE T2.user_trialist = 1 ORDER BY T1.movie_popularity DESC LIMIT 1"}],"db_id":"movie_platform"}428{"prompt":"Among all the residential areas in Delaware, how many of them implement daylight saving?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the residential areas in Delaware, how many of them implement daylight saving?\n        \n        ### Context:\n        #### Query Context:\n        \"Delaware\" is a county; implement daylight savings refers to daylight_saving = 'Yes'\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.zip_code) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'DELAWARE' AND T1.daylight_savings = 'Yes'"}],"db_id":"address"}429{"prompt":"How many incidents have the general description of \"ASSAULT\" in the IUCR classification?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many incidents have the general description of \"ASSAULT\" in the IUCR classification?\n        \n        ### Context:\n        #### Query Context:\n        general description refers to primary_description; 'ASSAULT' is the primary_description; incidents refers to iucr_no\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM IUCR WHERE primary_description = 'ASSAULT'"}],"db_id":"chicago_crime"}430{"prompt":"In how many cases where the subject was a female was the subject's status listed as Deceased?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In how many cases where the subject was a female was the subject's status listed as Deceased?\n        \n        ### Context:\n        #### Query Context:\n         female refers to gender = 'F'; subject's status listed as Deceased refers to subject_statuses = 'Deceased'\n        \n        #### Database Schema:\n        {'db_id': 'shooting', 'table_names_original': ['incidents', 'officers', 'subjects'], 'table_names': ['Incidents', 'officers', 'subjects'], 'column_names_original': [[-1, '*'], [0, 'case_number'], [0, 'date'], [0, 'location'], [0, 'subject_statuses'], [0, 'subject_weapon'], [0, 'subjects'], [0, 'subject_count'], [0, 'officers'], [1, 'case_number'], [1, 'race'], [1, 'gender'], [1, 'last_name'], [1, 'first_name'], [1, 'full_name'], [2, 'case_number'], [2, 'race'], [2, 'gender'], [2, 'last_name'], [2, 'first_name'], [2, 'full_name']], 'column_names': [[-1, '*'], [0, 'case number'], [0, 'date'], [0, 'location'], [0, 'subject statuses'], [0, 'subject weapon'], [0, 'subjects'], [0, 'subject_count'], [0, 'officers'], [1, 'case number'], [1, 'race'], [1, 'gender'], [1, 'last name'], [1, 'first name'], [1, 'full name'], [2, 'case number'], [2, 'race'], [2, 'gender'], [2, 'last name'], [2, 'first name'], [2, 'full name']], 'column_types': ['text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[9, 1], [15, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.case_number) FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T2.gender = 'F' AND T1.subject_statuses = 'Deceased'"}],"db_id":"shooting"}431{"prompt":"What is the short name for the journal that published the paper \"A Case of Unilateral Ashy Dermatosis\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the short name for the journal that published the paper \"A Case of Unilateral Ashy Dermatosis\"?\n        \n        ### Context:\n        #### Query Context:\n        A Case of Unilateral Ashy Dermatosis refer to Title\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ShortName FROM Paper AS T1 INNER JOIN Journal AS T2 ON T1.JournalId = T2.Id WHERE T1.Title = 'A Case of Unilateral Ashy Dermatosis'"}],"db_id":"authors"}432{"prompt":"Name the goalies with the most seasons played. State the average time he played for each season.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the goalies with the most seasons played. State the average time he played for each season.\n        \n        ### Context:\n        #### Query Context:\n        most seasons played refers to MAX(COUNT(playerID)); average time he played for each season refers to DIVIDE(SUM(T2.Min),COUNT(T2.playerID))\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], 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[11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], [12, 'league ID'], [12, 'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], 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'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 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'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.firstName, T1.lastName, T2.year, AVG(T2.Min) FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID WHERE T1.playerID = ( SELECT playerID FROM Goalies GROUP BY playerID ORDER BY COUNT(playerID) DESC LIMIT 1 ) GROUP BY T1.firstName, T1.lastName, T2.year"}],"db_id":"hockey"}433{"prompt":"Who are the suppliers of the discontinued products?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who are the suppliers of the discontinued products?\n        \n        ### Context:\n        #### Query Context:\n        discontinued product refers to Discontinued = 1; supplier refers to CompanyName\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.CompanyName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T1.Discontinued = 1"}],"db_id":"retail_world"}434{"prompt":"To which categories do the podcasts of the reviewer whose id is EFB34EAC8E9397C belong?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        To which categories do the podcasts of the reviewer whose id is EFB34EAC8E9397C belong?\n        \n        ### Context:\n        #### Query Context:\n        reviewer whose id is EFB34EAC8E9397C refers to author_id = 'EFB34EAC8E9397C'\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.category FROM categories AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.author_id = 'EFB34EAC8E9397C'"}],"db_id":"music_platform_2"}435{"prompt":"What's Pedro S Afonso's job title?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What's Pedro S Afonso's job title?\n        \n        ### Context:\n        #### Query Context:\n        job title means job description which refers to job_desc\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.job_desc FROM employee AS T1 INNER JOIN jobs AS T2 ON T1.job_id = T2.job_id WHERE T1.fname = 'Pedro' AND T1.minit = 'S' AND T1.lname = 'Afonso'"}],"db_id":"book_publishing_company"}436{"prompt":"From 1960 to 1965, which country has the lowest value of indicator belongs to Health: Population: Structure?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        From 1960 to 1965, which country has the lowest value of indicator belongs to Health: Population: Structure?\n        \n        ### Context:\n        #### Query Context:\n        From 1960 to 1965 refers to Year between '1960' and '1965'; the lowest value implies MIN(Value); country refers to CountryName;\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CountryName FROM Indicators WHERE Value = ( SELECT MIN(T1.Value) FROM Indicators AS T1 INNER JOIN Series AS T2 ON T1.IndicatorName = T2.IndicatorName WHERE T1.Year >= 1960 AND T1.Year < 1966 AND T2.Topic = 'Health: Population: Structure' )"}],"db_id":"world_development_indicators"}437{"prompt":"Write down the last name of employee who did inspection ID 52238?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Write down the last name of employee who did inspection ID 52238?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.last_name FROM employee AS T1 INNER JOIN inspection AS T2 ON T1.employee_id = T2.employee_id WHERE T2.inspection_id = 52238"}],"db_id":"food_inspection_2"}438{"prompt":"Please list the brands of all the root beer that Frank-Paul Santangelo had purchased on 2014\/7\/7.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the brands of all the root beer that Frank-Paul Santangelo had purchased on 2014\/7\/7.\n        \n        ### Context:\n        #### Query Context:\n        brands of the root beer refers to BrandName; purchased on 2014\/7\/7 refers to transactiondate = '2014-07-07';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T4.BrandName FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN rootbeer AS T3 ON T2.RootBeerID = T3.RootBeerID INNER JOIN rootbeerbrand AS T4 ON T3.BrandID = T4.BrandID WHERE T1.First = 'Frank-Paul' AND T1.Last = 'Santangelo' AND T2.TransactionDate = '2014-07-07'"}],"db_id":"beer_factory"}439{"prompt":"List down film titles from id 1 to 10.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down film titles from id 1 to 10.\n        \n        ### Context:\n        #### Query Context:\n        id 1 to 10 refers to film_id BETWEEN 1 and 10\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT title FROM film WHERE film_id BETWEEN 1 AND 10"}],"db_id":"movie_3"}440{"prompt":"Identify the number of movies that starred Nick Stallone.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the number of movies that starred Nick Stallone.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.film_id) FROM film_actor AS T1 INNER JOIN actor AS T2 ON T1.actor_id = T2.actor_id AND T2.first_name = 'NICK' AND T2.last_name = 'STALLONE'"}],"db_id":"movie_3"}441{"prompt":"Which region has the highest medal number?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which region has the highest medal number?\n        \n        ### Context:\n        #### Query Context:\n        region refers to region_name; the highest medal number refers to MAX(COUNT(medal_id <> 4));\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T5.region_name FROM medal AS T1 INNER JOIN competitor_event AS T2 ON T1.id = T2.medal_id INNER JOIN games_competitor AS T3 ON T2.competitor_id = T3.id INNER JOIN person_region AS T4 ON T3.person_id = T4.person_id INNER JOIN noc_region AS T5 ON T4.region_id = T5.id WHERE T1.id != 4 GROUP BY T5.region_name ORDER BY COUNT(T2.competitor_id) DESC LIMIT 1"}],"db_id":"olympics"}442{"prompt":"Please give the height of the tallest coach of the Montreal Canadiens.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please give the height of the tallest coach of the Montreal Canadiens.\n        \n        ### Context:\n        #### Query Context:\n        the tallest refers to MAX(height); Montreal Canadiens is the name of team;\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 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[5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], 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ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 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'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.height FROM Coaches AS T1 INNER JOIN Teams AS T2 ON T1.year = T2.year AND T1.tmID = T2.tmID INNER JOIN Master AS T3 ON T1.coachID = T3.coachID WHERE T2.name = 'Montreal Canadiens' AND T3.coachID IS NOT NULL ORDER BY T3.height DESC LIMIT 1"}],"db_id":"hockey"}443{"prompt":"Which title is about helpful hints on how to use your electronic resources, which publisher published it and what is the price of this book?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which title is about helpful hints on how to use your electronic resources, which publisher published it and what is the price of this book?\n        \n        ### Context:\n        #### Query Context:\n        publisher refers to pub_name; about the title refers to notes\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title, T2.pub_name, T1.price FROM titles AS T1 INNER JOIN publishers AS T2 ON T1.pub_id = T2.pub_id WHERE T1.notes = 'Helpful hints on how to use your electronic resources to the best advantage.'"}],"db_id":"book_publishing_company"}444{"prompt":"Which category has the highest number of users?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which category has the highest number of users?\n        \n        ### Context:\n        #### Query Context:\n        highest number of users refers to MAX(COUNT(app_id));\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.category FROM ( SELECT T2.category, COUNT(T1.app_id) AS num FROM app_labels AS T1 INNER JOIN label_categories AS T2 ON T2.label_id = T1.label_id GROUP BY T1.app_id, T2.category ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"talkingdata"}445{"prompt":"Which legislators are woman?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which legislators are woman?\n        \n        ### Context:\n        #### Query Context:\n        woman refers to gender_bio = 'F';\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT first_name, last_name FROM historical WHERE gender_bio = 'F'"}],"db_id":"legislator"}446{"prompt":"What is the gender of the user who tweeted `tw-715909161071091712`?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the gender of the user who tweeted `tw-715909161071091712`?\n        \n        ### Context:\n        #### Query Context:\n        \"tw-715909161071091712\" is the TweetID\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Gender FROM twitter AS T1 INNER JOIN user AS T2 ON T1.UserID = T2.UserID WHERE T1.TweetID = 'tw-715909161071091712'"}],"db_id":"social_media"}447{"prompt":"What is the URL to the user profile image on Mubi of the user who gave the movie id of 1103 a 5 ratinng score on 4\/19\/2020?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the URL to the user profile image on Mubi of the user who gave the movie id of 1103 a 5 ratinng score on 4\/19\/2020?\n        \n        ### Context:\n        #### Query Context:\n        URL to the user profile image on Mubi\u00a0 refers to user_avatar_image_url;\u00a0 4\/19\/2020 refers to rating_date_utc\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.user_avatar_image_url FROM ratings AS T1 INNER JOIN ratings_users AS T2 ON T1.user_id = T2.user_id WHERE T2.user_id = 1103 AND rating_score = 5 AND T2.rating_date_utc = '2020-04-19'"}],"db_id":"movie_platform"}448{"prompt":"Tell the number of 4-year private not-for-profit schools in the home state of \"Brevard Community College\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tell the number of 4-year private not-for-profit schools in the home state of \"Brevard Community College\".\n        \n        ### Context:\n        #### Query Context:\n        4-year refers to level = '4-year'; private not-for-profit refers to control = 'Private not-for-profit'; Brevard Community College refers to chronname = 'Brevard Community College';\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.chronname) FROM institution_details AS T1 INNER JOIN state_sector_details AS T2 ON T2.state = T1.state WHERE T2.level = '4-year' AND T2.control = 'Private not-for-profit' AND T1.chronname = 'Brevard Community College'"}],"db_id":"college_completion"}449{"prompt":"Among the users who use a Galaxy Note 2, how many of them are female?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the users who use a Galaxy Note 2, how many of them are female?\n        \n        ### Context:\n        #### Query Context:\n        Galaxy Note 2 refers to device_model = 'Galaxy Note 2'; female refers to gender = 'F';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.device_id) FROM phone_brand_device_model2 AS T1 INNER JOIN gender_age AS T2 ON T2.device_id = T1.device_id WHERE T2.gender = 'F' AND T1.device_model = 'Galaxy Note 2'"}],"db_id":"talkingdata"}450{"prompt":"State the order number where Qualitest ordered the highest product quantity.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the order number where Qualitest ordered the highest product quantity.\n        \n        ### Context:\n        #### Query Context:\n        Qualitest ordered the highest product quantity refers to Customer Names where MAX(Order Quantity);\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.OrderNumber FROM `Sales Orders` AS T1 INNER JOIN Customers AS T2 ON T2.CustomerID = T1._CustomerID WHERE T2.`Customer Names` = 'Qualitest ' ORDER BY T1.`Order Quantity` DESC LIMIT 1"}],"db_id":"regional_sales"}451{"prompt":"Which order has a higher priority, order no. 4 or order no. 36?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which order has a higher priority, order no. 4 or order no. 36?\n        \n        ### Context:\n        #### Query Context:\n        earlier orderdate have higher priority in delivery; which order no. 4 or order no. 36 refers to o_orderkey in (4, 36) where MIN(o_orderdate);\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT l_orderkey FROM lineitem WHERE l_orderkey IN (4, 36) ORDER BY l_shipdate DESC LIMIT 1"}],"db_id":"retails"}452{"prompt":"What is the most purchased products during the rainy days in June 2013 in weather station 9?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the most purchased products during the rainy days in June 2013 in weather station 9?\n        \n        ### Context:\n        #### Query Context:\n        most purchased product refers to Max(units); during the rainy day refers to codesum = RA; in June 2013 refers to SUBSTR(date, 1, 7) = '2013-06'; weather station 9 refers to station_nbr = 9; product refers to item_nbr\n        \n        #### Database Schema:\n        {'db_id': 'sales_in_weather', 'table_names_original': ['sales_in_weather', 'weather', 'relation'], 'table_names': ['sales_in_weather', 'weather', 'relation'], 'column_names_original': [[-1, '*'], [0, 'date'], [0, 'store_nbr'], [0, 'item_nbr'], [0, 'units'], [1, 'station_nbr'], [1, 'date'], [1, 'tmax'], [1, 'tmin'], [1, 'tavg'], [1, 'depart'], [1, 'dewpoint'], [1, 'wetbulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'codesum'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'stnpressure'], [1, 'sealevel'], [1, 'resultspeed'], [1, 'resultdir'], [1, 'avgspeed'], [2, 'store_nbr'], [2, 'station_nbr']], 'column_names': [[-1, '*'], [0, 'date'], [0, 'store number'], [0, 'item number'], [0, 'units'], [1, 'station number'], [1, 'date'], [1, 'temperature max'], [1, 'temperature min'], [1, 'temperature average'], [1, 'departure from normal'], [1, 'dew point'], [1, 'wet bulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'code summarization'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'station pressure'], [1, 'sea level'], [1, 'resultant speed'], [1, 'resultant direction'], [1, 'average speed'], [2, 'store number'], [2, 'station number']], 'column_types': ['text', 'date', 'integer', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [5, 6], 25], 'foreign_keys': [[26, 5], [25, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.item_nbr FROM sales_in_weather AS T1 INNER JOIN relation AS T2 ON T1.store_nbr = T2.store_nbr INNER JOIN weather AS T3 ON T2.station_nbr = T3.station_nbr WHERE T3.station_nbr = 9 AND T1.`date` LIKE '%2013-06%' AND codesum = 'RA' ORDER BY T1.units DESC LIMIT 1"}],"db_id":"sales_in_weather"}453{"prompt":"What is the weight in pounds of the style that is produced the most by the company? If there are multiple products sharing the same weight, indicate the name of each one of them and their corresponding weights.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the weight in pounds of the style that is produced the most by the company? If there are multiple products sharing the same weight, indicate the name of each one of them and their corresponding weights.\n        \n        ### Context:\n        #### Query Context:\n        weight in pounds refers to WeightUnitMeasureCode = 'LB';\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 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'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the paper IDs of papers presented in conferences has a homepage starts with \"http:\/\/www.informatik.uni-trier.de\/~ley\/db\/conf\/\"?\n        \n        ### Context:\n        #### Query Context:\n        homepage starts with \"http:\/\/www.informatik.uni-trier.de\/~ley\/db\/conf\/\" refers to HomePage LIKE 'http:\/\/www.informatik.uni-trier.de\/~ley\/db\/conf\/%'\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Id FROM Paper AS T1 INNER JOIN Conference AS T2 ON T1.ConferenceId = T2.Id WHERE T2.HomePage LIKE 'http:\/\/www.informatik.uni-trier.de\/~ley\/db\/conf\/%'"}],"db_id":"authors"}455{"prompt":"What is the population gap between the United Kingdom and Italy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. 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'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(Population) - MIN(Population) FROM country WHERE Name = 'United Kingdom' OR Name = 'Italy'"}],"db_id":"mondial_geo"}456{"prompt":"How many of the businesses are located at 1825 POST St #223, San Francisco?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the businesses are located at 1825 POST St #223, San Francisco?\n        \n        ### Context:\n        #### Query Context:\n        1825 POST St #223 refers to address = '1825 POST St #223', San Francisco is the name of the city;\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM businesses WHERE address = '1825 POST St #223' AND city = 'SAN FRANCISCO'"}],"db_id":"food_inspection"}457{"prompt":"What was the deposit interest rate in the Commonwealth of Australia in 1979 in percentage?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What was the deposit interest rate in the Commonwealth of Australia in 1979 in percentage?\n        \n        ### Context:\n        #### Query Context:\n        deposit interest rate refers to value where IndicatorName = 'Deposit interest rate (%)'; in the Commonwealth of Australia refers to\u00a0 LongName = 'Commonwealth of Australia'; in 1979 refers to Year = '1979'\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Value FROM Indicators AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.CountryCode WHERE T2.LongName = 'Commonwealth of Australia' AND T1.IndicatorName = 'Deposit interest rate (%)' AND T1.Year = 1979"}],"db_id":"world_development_indicators"}458{"prompt":"Among all the root beers sold in 2014, what is the percentage of the root beers produced by the brewery AJ Stephans Beverages?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the root beers sold in 2014, what is the percentage of the root beers produced by the brewery AJ Stephans Beverages?\n        \n        ### Context:\n        #### Query Context:\n        sold in 2014 refers to SUBSTR(TransactionDate, 1, 4) = '2014'; percentage = DIVIDE(MULTIPLY(SUM(BreweryName = 'AJ Stephans Beverages'), 1.0), COUNT(RootBeerID)) WHERE SUBSTR(TransactionDate, 1, 4) = '2014'; AJ Stephans Beverages refers to BreweryName = 'AJ Stephans Beverages';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T3.BreweryName = 'AJ Stephans Beverages' THEN 1 ELSE NULL END) AS REAL) * 100 \/ COUNT(T1.BrandID) FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID WHERE T2.TransactionDate LIKE '2014%'"}],"db_id":"beer_factory"}459{"prompt":"How much is the total gross of the movie with a song titled \"Little Wonders\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much is the total gross of the movie with a song titled \"Little Wonders\"?\n        \n        ### Context:\n        #### Query Context:\n        song = 'Little Wonders'\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.total_gross FROM movies_total_gross AS T1 INNER JOIN characters AS T2 ON T2.movie_title = T1.movie_title WHERE T2.song = 'Little Wonders'"}],"db_id":"disney"}460{"prompt":"Count the male actors born in USA that starred in Ghost.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Count the male actors born in USA that starred in Ghost.\n        \n        ### Context:\n        #### Query Context:\n        male refers to Gender = 'Male'; born in USA refers to Birth Country = 'USA'; Ghost refers to Title = 'Ghost'\n        \n        #### Database Schema:\n        {'db_id': 'movie', 'table_names_original': ['actor', 'movie', 'characters'], 'table_names': ['actor', 'movie', 'characters'], 'column_names_original': [[-1, '*'], [0, 'ActorID'], [0, 'Name'], [0, 'Date of Birth'], [0, 'Birth City'], [0, 'Birth Country'], [0, 'Height (Inches)'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'MovieID'], [1, 'Title'], [1, 'MPAA Rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'Rating Count'], [1, 'Summary'], [2, 'MovieID'], [2, 'ActorID'], [2, 'Character Name'], [2, 'creditOrder'], [2, 'pay'], [2, 'screentime']], 'column_names': [[-1, '*'], [0, 'actor id'], [0, 'Name'], [0, 'date of birth'], [0, 'birth city'], [0, 'birth country'], [0, 'height inches'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'movie id'], [1, 'Title'], [1, 'motion picture association of america rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'rating count'], [1, 'Summary'], [2, 'movie id'], [2, 'actor id'], [2, 'character name'], [2, 'credit order'], [2, 'pay'], [2, 'screentime']], 'column_types': ['text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text'], 'primary_keys': [1, 11, [22, 23]], 'foreign_keys': [[22, 11], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID INNER JOIN actor AS T3 ON T3.ActorID = T2.ActorID WHERE T1.Title = 'Ghost' AND T3.Gender = 'Male' AND T3.`Birth Country` = 'USA'"}],"db_id":"movie"}461{"prompt":"How many teachers that have Literature & Writing as their primary focus subject use 'Mr' as their teacher prefix?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many teachers that have Literature & Writing as their primary focus subject use 'Mr' as their teacher prefix?\n        \n        ### Context:\n        #### Query Context:\n        Literature & Writing' is primary_focus_subject; use 'Mr' as their teacher prefix refers to teacher_prefix = 'Mr';\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(teacher_acctid) FROM projects WHERE teacher_prefix = 'Mr.' AND primary_focus_subject = 'Literature & Writing'"}],"db_id":"donor"}462{"prompt":"Find and list the full name of customers who rented more than five types of movies.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find and list the full name of customers who rented more than five types of movies.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name; types of movies means category of movies; \nrented more than five types of movies refers to COUNT(film_category) > 5\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.first_name, T.last_name FROM ( SELECT T1.first_name, T1.last_name, COUNT(T1.customer_id) AS num FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id INNER JOIN inventory AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN film AS T4 ON T3.film_id = T4.film_id INNER JOIN film_category AS T5 ON T4.film_id = T5.film_id GROUP BY T1.first_name, T1.last_name ) AS T WHERE T.num > 5"}],"db_id":"movie_3"}463{"prompt":"In the history of team id NJD, which goalie saved the most goal attempts? Give his full name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In the history of team id NJD, which goalie saved the most goal attempts? Give his full name.\n        \n        ### Context:\n        #### Query Context:\n        saved the most goal attempts refers to max(subtract(SA, GA)); team id refers to tmID\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], 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'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 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'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 'September ties'], [16, 'September overtime loses'], [16, 'October wins'], [16, 'October loses'], [16, 'October ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 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'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.firstName, T1.lastName FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID WHERE T2.tmID = 'NJD' GROUP BY T2.playerID ORDER BY SUM(T2.SA - T2.GA) DESC LIMIT 1"}],"db_id":"hockey"}464{"prompt":"How many matches were held in 2011?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many matches were held in 2011?\n        \n        ### Context:\n        #### Query Context:\n        held in 2011 refers to Match_Date like '2011%';\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(Match_Id) FROM `Match` WHERE Match_Date LIKE '2011%'"}],"db_id":"soccer_2016"}465{"prompt":"State the address of drivers who transported the shipment with weight greater than 50000 pounds.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the address of drivers who transported the shipment with weight greater than 50000 pounds.\n        \n        ### Context:\n        #### Query Context:\n        shipment with weight greater than 50000 pounds refers to Sum(weight) > 50000\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.address FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id GROUP BY T2.driver_id HAVING SUM(T1.weight) > 50000"}],"db_id":"shipping"}466{"prompt":"What is the discount percentage of \"LL Road Frame Sale\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the discount percentage of \"LL Road Frame Sale\"?\n        \n        ### Context:\n        #### Query Context:\n        discount percentage refers to DiscountPct; LL Road Frame Sale is a description of special offer;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 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'integer', 'integer', 'integer', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'datetime', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DiscountPct FROM SpecialOffer WHERE Description = 'LL Road Frame Sale'"}],"db_id":"works_cycles"}467{"prompt":"What was the role of Jason Kuschner in episode 9?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What was the role of Jason Kuschner in episode 9?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.role FROM Credit AS T1 INNER JOIN Person AS T2 ON T2.person_id = T1.person_id INNER JOIN Episode AS T3 ON T1.episode_id = T3.episode_id WHERE T3.episode = 9 AND T2.name = 'Jason Kuschner'"}],"db_id":"law_episode"}468{"prompt":"What are the years when countries have indicator name of \"Air transport, passengers carried\"? List the table name of these countries.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the years when countries have indicator name of \"Air transport, passengers carried\"? List the table name of these countries.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.Year, T1.TableName FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T2.IndicatorName = 'Air transport, passengers carried'"}],"db_id":"world_development_indicators"}469{"prompt":"What is the zip code of the district represented by Steven A King?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the zip code of the district represented by Steven A King?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.zip_code FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.first_name = 'King' AND T1.last_name = 'Steven A'"}],"db_id":"address"}470{"prompt":"Find the average ride time of the bikes that started at Steuart at Market station and ended at Embarcadero at Sansome station in July 2014.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find the average ride time of the bikes that started at Steuart at Market station and ended at Embarcadero at Sansome station in July 2014.\n        \n        ### Context:\n        #### Query Context:\n        started at refers to start_station_name; start_station_name = 'Steuart at Market'; ended at refers to end_station_name; end_station_name = 'Embarcadero at Sansome'; rides in July 2004 refers to start_date = '7\/1\/2014 0:00'AND end_date = '7\/31\/2014 12:59';average ride time = DIVIDE(SUM(duration), COUNT(id))\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(duration) FROM trip WHERE start_date = '7\/1\/2014%' AND end_date = '7\/31\/2014%' AND start_station_name = 'Steuart at Market' AND end_station_name = 'Embarcadero at Sansome'"}],"db_id":"bike_share_1"}471{"prompt":"What is the transaction ratio being made at Sac State American River Courtyard and Sac State Union?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the transaction ratio being made at Sac State American River Courtyard and Sac State Union?\n        \n        ### Context:\n        #### Query Context:\n        transaction ratio = DIVIDE(SUM(TransactionID WHERE LocationName = 'Sac State American River Courtyard'), SUM(TransactionID WHERE LocationName = 'Sac State Union')); Sac State American River Courtyard refers to LocationName = 'Sac State American River Courtyard'; Sac State Union refers to LocationName = 'Sac State Union';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T2.LocationName = 'Sac State American River Courtyard' THEN T1.TransactionID ELSE NULL END) AS REAL) * 100 \/ COUNT(CASE WHEN T2.LocationName = 'Sac State Union' THEN T1.TransactionID ELSE NULL END) FROM `transaction` AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID"}],"db_id":"beer_factory"}472{"prompt":"Who is the narrator of the \"Flight\" episode?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the narrator of the \"Flight\" episode?\n        \n        ### Context:\n        #### Query Context:\n        who refers to name; narrator refers to role = 'Narrator'; the \"Flight\" episode refers to title = 'Flight'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.name FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T3.person_id = T2.person_id WHERE T1.title = 'Flight' AND T2.role = 'Narrator'"}],"db_id":"law_episode"}473{"prompt":"Provide the name of artists who had no more than 100 downloads and are tagged \"funk\" in 1980.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the name of artists who had no more than 100 downloads and are tagged \"funk\" in 1980.\n        \n        ### Context:\n        #### Query Context:\n        no more than 100 downloads refer to totalSnatched \u2264 100; groupYear = 1980; tag = 'funk';\n        \n        #### Database Schema:\n        {'db_id': 'music_tracker', 'table_names_original': ['torrents', 'tags'], 'table_names': ['torrents', 'tags'], 'column_names_original': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_names': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_types': ['text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [7, 8], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.artist FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T2.tag = 'funk' AND T1.groupYear = 1980 AND T1.totalSnatched <= 100"}],"db_id":"music_tracker"}474{"prompt":"List down the business ID and attribute value of the attribute name of \"payment_types_visa\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the business ID and attribute value of the attribute name of \"payment_types_visa\".\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.business_id, T2.attribute_value FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T1.attribute_name = 'payment_types_visa'"}],"db_id":"public_review_platform"}475{"prompt":"Which two countries does the Detroit River flow through? Give the full name of the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which two countries does the Detroit River flow through? Give the full name of the country.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.Name FROM located AS T1 INNER JOIN river AS T2 ON T1.River = T2.Name INNER JOIN country AS T3 ON T3.Code = T1.Country WHERE T2.Name = 'Detroit River'"}],"db_id":"mondial_geo"}476{"prompt":"Which word that has 274499 repetitions in the Catalan language?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which word that has 274499 repetitions in the Catalan language?\n        \n        ### Context:\n        #### Query Context:\n        lid = 1 menas it's Catalan language; 274499 repetitions refers to occurrences = 274499\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.word FROM words AS T1 INNER JOIN langs_words AS T2 ON T1.wid = T2.wid WHERE T2.occurrences = 274499 AND T2.lid = 1"}],"db_id":"language_corpus"}477{"prompt":"List all the cities that belong to United Arab Emirates.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the cities that belong to United Arab Emirates.\n        \n        ### Context:\n        #### Query Context:\n        United Arab Emirates refers to country = 'United Arab Emirates'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.city FROM city AS T1 INNER JOIN country AS T2 ON T2.country_id = T1.country_id WHERE country = 'United Arab Emirates'"}],"db_id":"movie_3"}478{"prompt":"Identify the allergy period for Isadora Moen and what triggered it.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the allergy period for Isadora Moen and what triggered it.\n        \n        ### Context:\n        #### Query Context:\n        allergy period = SUBTRACT(allergies.START, allergies.STOP); what triggered the allergy refers to allergies.DESCRIPTION;\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.START, T2.STOP, T2.DESCRIPTION FROM patients AS T1 INNER JOIN allergies AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Isadora' AND T1.last = 'Moen'"}],"db_id":"synthea"}479{"prompt":"Provide the ward number with the highest population.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the ward number with the highest population.\n        \n        ### Context:\n        #### Query Context:\n        highest population refers to Max(Population); ward number refers to ward_no\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT ward_no FROM Ward ORDER BY Population DESC LIMIT 1"}],"db_id":"chicago_crime"}480{"prompt":"Among the projects created by a teacher from New York, how many of them have a donor from the same city?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the projects created by a teacher from New York, how many of them have a donor from the same city?\n        \n        ### Context:\n        #### Query Context:\n        New York is the donor city; donor from the same city refers to donor from New York in which teacher_ny_teaching_fellow = 't';\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.projectid) FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.teacher_ny_teaching_fellow = 't' AND T2.donor_city = 'New York'"}],"db_id":"donor"}481{"prompt":"List the genre id of the game Pro Evolution Soccer 2012.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the genre id of the game Pro Evolution Soccer 2012.\n        \n        ### Context:\n        #### Query Context:\n        Pro Evolution Soccer 2012 refers to game_name = 'Pro Evolution Soccer 2012'\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.genre_id FROM game AS T WHERE T.game_name = 'Pro Evolution Soccer 2012'"}],"db_id":"video_games"}482{"prompt":"What is the genre ID of the game named 25 to Life?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the genre ID of the game named 25 to Life?\n        \n        ### Context:\n        #### Query Context:\n        the game named 25 to Life refers to game_name = '25 to Life'\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.genre_id FROM game AS T WHERE T.game_name = '25 to Life'"}],"db_id":"video_games"}483{"prompt":"Who was the customer in the South Region superstore who bought the most \u201cHon Multipurpose Stacking Arm Chairs\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who was the customer in the South Region superstore who bought the most \u201cHon Multipurpose Stacking Arm Chairs\"?\n        \n        ### Context:\n        #### Query Context:\n        customer refers to \"Customer Name\"; \u201cHon Multipurpose Stacking Arm Chairs\" is the \"Product Name\";\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Customer Name` FROM south_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T1.`Product ID` WHERE T3.`Product Name` = 'Hon Multipurpose Stacking Arm Chairs' GROUP BY T2.`Customer Name` ORDER BY COUNT(T2.`Customer Name`) DESC LIMIT 1"}],"db_id":"superstore"}484{"prompt":"Provide the number of credits, category, role and birthplace of the crew member who was born in North Korea.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the number of credits, category, role and birthplace of the crew member who was born in North Korea.\n        \n        ### Context:\n        #### Query Context:\n        number of credits refers to credited; born in North Korea refers to birth_country = 'North Korea'\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.credited, T2.category, T2.role, T1.birth_place FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.name = T2.person WHERE T1.birth_country = 'North Korea';"}],"db_id":"simpson_episodes"}485{"prompt":"Please list the case numbers of all the crimes whose short description of the kind of crime is \"Homicide 1st & 2nd Degree\" in the FBI classification.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the case numbers of all the crimes whose short description of the kind of crime is \"Homicide 1st & 2nd Degree\" in the FBI classification.\n        \n        ### Context:\n        #### Query Context:\n        \"Homicide 1st & 2nd Degree\" is the title\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.case_number FROM FBI_Code AS T1 INNER JOIN Crime AS T2 ON T1.fbi_code_no = T2.fbi_code_no WHERE T1.title = 'Homicide 1st & 2nd Degree'"}],"db_id":"chicago_crime"}486{"prompt":"Among all recipes containing cheese, what is the percentage of recipes with calories greater than 200?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all recipes containing cheese, what is the percentage of recipes with calories greater than 200?\n        \n        ### Context:\n        #### Query Context:\n        cheese is a category; calories greater than 200 refers to calories > 200; calculation = MULTIPLY(DIVIDE(COUNT(calories > 200 THEN recipe_id), COUNT(recipe_id)), 100)\n        \n        #### Database Schema:\n        {'db_id': 'cookbook', 'table_names_original': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'table_names': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'column_names_original': [[-1, '*'], [0, 'ingredient_id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe_id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield_unit'], [1, 'prep_min'], [1, 'cook_min'], [1, 'stnd_min'], [1, 'source'], [1, 'intro'], [1, 'directions'], [2, 'recipe_id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total_fat'], [2, 'sat_fat'], [2, 'cholestrl'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin_c'], [2, 'vitamin_a'], [2, 'fiber'], [2, 'pcnt_cal_carb'], [2, 'pcnt_cal_fat'], [2, 'pcnt_cal_prot'], [2, 'calories'], [3, 'quantity_id'], [3, 'recipe_id'], [3, 'ingredient_id'], [3, 'max_qty'], [3, 'min_qty'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_names': [[-1, '*'], [0, 'ingredient id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield unit'], [1, 'preparation minute'], [1, 'cooked minute'], [1, 'stand minute'], [1, 'source'], [1, 'introduction'], [1, 'directions'], [2, 'recipe id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total fat'], [2, 'saturated fat'], [2, 'cholesterol'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin c'], [2, 'vitamin a'], [2, 'fiber'], [2, 'percentage calculation carbo'], [2, 'percentage calculation fat'], [2, 'percentage calculation protein'], [2, 'calories'], [3, 'quantity id'], [3, 'recipe id'], [3, 'ingredient id'], [3, 'maximum quantity'], [3, 'minimum quantity'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text'], 'primary_keys': [1, 5, 16, 32], 'foreign_keys': [[16, 5], [33, 16], [34, 1], [33, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T4.calories > 200 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM Recipe AS T1 INNER JOIN Quantity AS T2 ON T1.recipe_id = T2.recipe_id INNER JOIN Ingredient AS T3 ON T3.ingredient_id = T2.ingredient_id INNER JOIN Nutrition AS T4 ON T4.recipe_id = T1.recipe_id WHERE T3.category = 'cheese'"}],"db_id":"cookbook"}487{"prompt":"What profit will the company gain if they sell 10 items of the product that has the lightest weight?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What profit will the company gain if they sell 10 items of the product that has the lightest weight?\n        \n        ### Context:\n        #### Query Context:\n        Lightest product refers to Min(Weight); profit if they sell 10 items refers to Subtract (ListPrice , StandardCost) *10;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many employees do not have any suffix and what are their organization level?\n        \n        ### Context:\n        #### Query Context:\n        Do not have any suffix means Suffix is null\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 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89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T3.BusinessEntityID) FROM ( SELECT T1.BusinessEntityID FROM Employee AS T1 INNER JOIN Person AS T2 USING (BusinessEntityID) WHERE T2.Suffix IS NULL GROUP BY T1.BusinessEntityID ) AS T3"}],"db_id":"works_cycles"}489{"prompt":"What is the total trip duration made within Palo Alto city? Convert the duration to hour.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the total trip duration made within Palo Alto city? Convert the duration to hour.\n        \n        ### Context:\n        #### Query Context:\n        total trip duration to hour = DIVIDE(SUM(duration), 3600);\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T1.duration) AS REAL) \/ 3600 FROM trip AS T1 LEFT JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.city = 'Palo Alto'"}],"db_id":"bike_share_1"}490{"prompt":"Name the products via wholesale channel of the store under Pacific\/Honolulu time zone.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the products via wholesale channel of the store under Pacific\/Honolulu time zone.\n        \n        ### Context:\n        #### Query Context:\n        products refers to Product Name; via wholesale channel refers to Sales Channel = 'Wholesale'\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T FROM ( SELECT DISTINCT CASE  WHEN T3.`Time Zone` = 'Pacific\/Honolulu' AND T2.`Sales Channel` = 'Wholesale' THEN T1.`Product Name` ELSE NULL END AS T FROM Products T1 INNER JOIN `Sales Orders` T2 ON T2._ProductID = T1.ProductID INNER JOIN `Store Locations` T3 ON T3.StoreID = T2._StoreID ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}491{"prompt":"Give the height of the tallest athlete from Portugal.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the height of the tallest athlete from Portugal.\n        \n        ### Context:\n        #### Query Context:\n        the tallest athlete refers to id where MAX(height); from Portugal refers to region_name = 'Portugal';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.height FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.region_name = 'Portugal' ORDER BY T3.height DESC LIMIT 1"}],"db_id":"olympics"}492{"prompt":"What percentage of people have worked on the True North episode as additional crew?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of people have worked on the True North episode as additional crew?\n        \n        ### Context:\n        #### Query Context:\n        the True North episode refers to title = 'True North'; additional crew refers to role = 'Additional Crew'; percentage = divide(count(episode_id where role = 'Additional Crew'), count(episode_id)) * 100% where title = 'True North'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.role = 'Additional Crew' THEN 1 ELSE 0 END) AS REAL ) * 100 \/ COUNT(T1.episode_id) FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'True North'"}],"db_id":"law_episode"}493{"prompt":"How many mothers have taken immunization during prenatal visit?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many mothers have taken immunization during prenatal visit?\n        \n        ### Context:\n        #### Query Context:\n        expecting mothers can be attributed to encounters.REASONDESCRIPTION = 'Normal pregnancy';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.PATIENT) FROM encounters AS T1 INNER JOIN immunizations AS T2 ON T1.PATIENT = T2.PATIENT WHERE T1.REASONDESCRIPTION = 'Normal pregnancy' AND T1.DATE = T2.DATE"}],"db_id":"synthea"}494{"prompt":"Count the number of postal points under New York-Newark-Jersey City, NY-NJ-PA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Count the number of postal points under New York-Newark-Jersey City, NY-NJ-PA.\n        \n        ### Context:\n        #### Query Context:\n        postal points refer to zip_code; under New York-Newark-Jersey City, NY-NJ-PA refers to CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA';\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.zip_code) FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA'"}],"db_id":"address"}495{"prompt":"How many category names start with the word \"game\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many category names start with the word \"game\"?\n        \n        ### Context:\n        #### Query Context:\n        category names refers to category; start with the word game refers to category like 'game%';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(label_id) FROM label_categories WHERE category LIKE 'game%'"}],"db_id":"talkingdata"}496{"prompt":"To which city did donor \u201c22cbc920c9b5fa08dfb331422f5926b5\u201d donate?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        To which city did donor \u201c22cbc920c9b5fa08dfb331422f5926b5\u201d donate?\n        \n        ### Context:\n        #### Query Context:\n        donor \u201c22cbc920c9b5fa08dfb331422f5926b5\u201d refers to donor_acctid = '22cbc920c9b5fa08dfb331422f5926b5'; city refers to donor_city\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT donor_city FROM donations WHERE donor_acctid = '22cbc920c9b5fa08dfb331422f5926b5'"}],"db_id":"donor"}497{"prompt":"Give the name of the No.1 character in the credit list from the highest rating thriller movie.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the name of the No.1 character in the credit list from the highest rating thriller movie.\n        \n        ### Context:\n        #### Query Context:\n        No.1 character in the credit list refers to creditOrder = '1'; highest rating refers to max(rating); thriller movie refers to Genre = 'Thriller'\n        \n        #### Database Schema:\n        {'db_id': 'movie', 'table_names_original': ['actor', 'movie', 'characters'], 'table_names': ['actor', 'movie', 'characters'], 'column_names_original': [[-1, '*'], [0, 'ActorID'], [0, 'Name'], [0, 'Date of Birth'], [0, 'Birth City'], [0, 'Birth Country'], [0, 'Height (Inches)'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'MovieID'], [1, 'Title'], [1, 'MPAA Rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'Rating Count'], [1, 'Summary'], [2, 'MovieID'], [2, 'ActorID'], [2, 'Character Name'], [2, 'creditOrder'], [2, 'pay'], [2, 'screentime']], 'column_names': [[-1, '*'], [0, 'actor id'], [0, 'Name'], [0, 'date of birth'], [0, 'birth city'], [0, 'birth country'], [0, 'height inches'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'movie id'], [1, 'Title'], [1, 'motion picture association of america rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'rating count'], [1, 'Summary'], [2, 'movie id'], [2, 'actor id'], [2, 'character name'], [2, 'credit order'], [2, 'pay'], [2, 'screentime']], 'column_types': ['text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text'], 'primary_keys': [1, 11, [22, 23]], 'foreign_keys': [[22, 11], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.`Character Name` FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID WHERE T2.creditOrder = '1' AND T1.Genre = 'Thriller' ORDER BY T1.Rating DESC LIMIT 1"}],"db_id":"movie"}498{"prompt":"What is the percentage of Forks to Stars of repository for solution \"104086\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of Forks to Stars of repository for solution \"104086\"?\n        \n        ### Context:\n        #### Query Context:\n        solution refers to Solution.Id; Solution.Id = 104086; percentage = MULTIPLY(DIVIDE(Forkk, Stars), 100);\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(T1.Forks AS REAL) * 100 \/ T1.Stars FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T2.Id = 104086"}],"db_id":"codebase_comments"}499{"prompt":"Among the menu pages of \"Ritz Carlton\", how many of them have a width of over 1000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the menu pages of \"Ritz Carlton\", how many of them have a width of over 1000?\n        \n        ### Context:\n        #### Query Context:\n        Ritz Carlton is a name of menu; width of over 2490 refers to full_width > 1000;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.name = 'Ritz Carlton' THEN 1 ELSE 0 END) FROM Menu AS T1 INNER JOIN MenuPage AS T2 ON T1.id = T2.menu_id WHERE T2.full_width > 1000"}],"db_id":"menu"}500{"prompt":"How many transactions were made at Sac State Union?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many transactions were made at Sac State Union?\n        \n        ### Context:\n        #### Query Context:\n        Sac State Union refers to LocationName = 'Sac State Union';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.TransactionID) FROM `transaction` AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID WHERE T2.LocationName = 'Sac State Union'"}],"db_id":"beer_factory"}501{"prompt":"How many reviews does 'LifeAfter\/The Message' have which were rated below 3?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many reviews does 'LifeAfter\/The Message' have which were rated below 3?\n        \n        ### Context:\n        #### Query Context:\n        LifeAfter\/The Message' is the title of podcast; rated below 3 refers to rating < 3\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.rating) FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.title = 'LifeAfter\/The Message' AND T2.rating <= 3"}],"db_id":"music_platform_2"}502{"prompt":"What is the size of the smallest part in a jumbo case container?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the size of the smallest part in a jumbo case container?\n        \n        ### Context:\n        #### Query Context:\n        size refers to p_size; the smallest part refers to min(p_size); jumbo case container refers to p_container = 'JUMBO CASE'\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MIN(p_size) FROM part WHERE p_container = 'JUMBO CASE'"}],"db_id":"retails"}503{"prompt":"What is the longest trip duration that started and ended August 29, 2013?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the longest trip duration that started and ended August 29, 2013?\n        \n        ### Context:\n        #### Query Context:\n        started and ended August 29, 2013 refers to start_date = '8\/29\/2013' and end_date = '8\/29\/2013';\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(duration) FROM trip WHERE start_date LIKE '8\/29\/2013%' AND end_date LIKE '8\/29\/2013%'"}],"db_id":"bike_share_1"}504{"prompt":"Please list the titles of all the podcasts under the category \"arts-performing-arts\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the titles of all the podcasts under the category \"arts-performing-arts\".\n        \n        ### Context:\n        #### Query Context:\n        category 'arts-performing-arts' refers to category = 'arts-performing-arts';\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.title FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'arts-performing-arts'"}],"db_id":"music_platform_2"}505{"prompt":"State the publisher name for publisher ID 877? Calculate its average year to date sales.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the publisher name for publisher ID 877? Calculate its average year to date sales.\n        \n        ### Context:\n        #### Query Context:\n        publisher id refers to pub_id; publisher name refers to pub_name; average year to date sales = AVG(ytd_sales)\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.pub_name, AVG(T1.ytd_sales) FROM titles AS T1 INNER JOIN publishers AS T2 ON T1.pub_id = T2.pub_id WHERE T1.pub_id = '0877' GROUP BY T2.pub_name"}],"db_id":"book_publishing_company"}506{"prompt":"Among the transactions made in July, 2014, how many of them were made by a male customer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the transactions made in July, 2014, how many of them were made by a male customer?\n        \n        ### Context:\n        #### Query Context:\n        in July, 2014 refers to SUBSTR(TransactionDate, 1, 4) = '2014' AND SUBSTR(TransactionDate, 6, 2) = '07'; male customer refers to Gender = 'M';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.CustomerID) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.Gender = 'M' AND STRFTIME('%Y-%m', T2.TransactionDate) = '2014-07'"}],"db_id":"beer_factory"}507{"prompt":"Give the name of the film for inventory No.3479.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the name of the film for inventory No.3479.\n        \n        ### Context:\n        #### Query Context:\n        inventory no. 3479 refers to inventory_id = '3479'; name of film refers to title\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM film AS T1 INNER JOIN inventory AS T2 ON T1.film_id = T2.film_id WHERE T2.inventory_id = 3479"}],"db_id":"movie_3"}508{"prompt":"Please list the area name of the communities in the Far north side, which has a population of more than 50000 but less than 70000.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the area name of the communities in the Far north side, which has a population of more than 50000 but less than 70000.\n        \n        ### Context:\n        #### Query Context:\n        area name refers to community_area_name; the Far north side refers to side = 'Far North'; a population of more than 50000 but less than 70000 refers to population BETWEEN '50000' AND '70000'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT community_area_name, side FROM Community_Area WHERE side = 'Far North ' AND population BETWEEN 50000 AND 70000"}],"db_id":"chicago_crime"}509{"prompt":"Calculate the production volume of the dairy product 'Mascarpone Fabioli'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the production volume of the dairy product 'Mascarpone Fabioli'.\n        \n        ### Context:\n        #### Query Context:\n        'Mascarpone Fabioli' is a ProductName; calculation = SUM(UnitsInStock, UnitsOnOrder)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(UnitsInStock + UnitsOnOrder) FROM Products WHERE ProductName = 'Mascarpone Fabioli'"}],"db_id":"retail_world"}510{"prompt":"Provide the inspection ID of the establishment named \"PIZZA RUSTICA, INC.\"","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the inspection ID of the establishment named \"PIZZA RUSTICA, INC.\"\n        \n        ### Context:\n        #### Query Context:\n        \"PIZZA RUSTICA, INC.\" refers to dba_name = 'PIZZA RUSTICA, INC'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.inspection_id FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T1.dba_name = 'PIZZA RUSTICA, INC'"}],"db_id":"food_inspection_2"}511{"prompt":"What is the name of the team that won the most number of matches in season 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the team that won the most number of matches in season 1?\n        \n        ### Context:\n        #### Query Context:\n        name of the team refers to Team_Name; the most number of matches refers to max(count(Match_Winner)); season 1 refers to season_Id = 1\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Team_Name FROM Team WHERE Team_Id = ( SELECT Match_Winner FROM `Match` WHERE season_Id = 1 GROUP BY Match_Winner ORDER BY COUNT(Match_Winner) DESC LIMIT 1 )"}],"db_id":"soccer_2016"}512{"prompt":"What is the average vitamin C amount of all cakes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average vitamin C amount of all cakes?\n        \n        ### Context:\n        #### Query Context:\n        average vitamin C refers to AVG(vitamin_c); all cakes refers to title LIKE '%cake%'\n        \n        #### Database Schema:\n        {'db_id': 'cookbook', 'table_names_original': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'table_names': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'column_names_original': [[-1, '*'], [0, 'ingredient_id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe_id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield_unit'], [1, 'prep_min'], [1, 'cook_min'], [1, 'stnd_min'], [1, 'source'], [1, 'intro'], [1, 'directions'], [2, 'recipe_id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total_fat'], [2, 'sat_fat'], [2, 'cholestrl'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin_c'], [2, 'vitamin_a'], [2, 'fiber'], [2, 'pcnt_cal_carb'], [2, 'pcnt_cal_fat'], [2, 'pcnt_cal_prot'], [2, 'calories'], [3, 'quantity_id'], [3, 'recipe_id'], [3, 'ingredient_id'], [3, 'max_qty'], [3, 'min_qty'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_names': [[-1, '*'], [0, 'ingredient id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield unit'], [1, 'preparation minute'], [1, 'cooked minute'], [1, 'stand minute'], [1, 'source'], [1, 'introduction'], [1, 'directions'], [2, 'recipe id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total fat'], [2, 'saturated fat'], [2, 'cholesterol'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin c'], [2, 'vitamin a'], [2, 'fiber'], [2, 'percentage calculation carbo'], [2, 'percentage calculation fat'], [2, 'percentage calculation protein'], [2, 'calories'], [3, 'quantity id'], [3, 'recipe id'], [3, 'ingredient id'], [3, 'maximum quantity'], [3, 'minimum quantity'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text'], 'primary_keys': [1, 5, 16, 32], 'foreign_keys': [[16, 5], [33, 16], [34, 1], [33, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.vitamin_c) FROM Nutrition AS T1 INNER JOIN Recipe AS T2 ON T2.recipe_id = T1.recipe_id WHERE T2.title LIKE '%cake%'"}],"db_id":"cookbook"}513{"prompt":"Which country has the highest value of Merchandise imports by the reporting economy (current US$)?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country has the highest value of Merchandise imports by the reporting economy (current US$)?\n        \n        ### Context:\n        #### Query Context:\n        country refers to CountryName; the highest value implies MAX(Value); IndicatorName = 'Merchandise imports by the reporting economy (current US$)';\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments 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'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CountryName FROM Indicators WHERE IndicatorName = 'Merchandise imports by the reporting economy (current US$)' ORDER BY Value DESC LIMIT 1"}],"db_id":"world_development_indicators"}514{"prompt":"What is the release title of the single that was released by Ron Hunt in 1979 that was downloaded 239 times?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the release title of the single that was released by Ron Hunt in 1979 that was downloaded 239 times?\n        \n        ### Context:\n        #### Query Context:\n        release title refers to groupName; Ron Hunt is an artist; groupYear = 1979; releaseType = 'single'; downloaded 239 times refer to totalSnatched = 239;\n        \n        #### Database Schema:\n        {'db_id': 'music_tracker', 'table_names_original': ['torrents', 'tags'], 'table_names': ['torrents', 'tags'], 'column_names_original': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_names': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_types': ['text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [7, 8], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT groupName FROM torrents WHERE artist LIKE 'ron hunt & ronnie g & the sm crew' AND groupYear = 1979 AND releaseType LIKE 'single' AND totalSnatched = 239"}],"db_id":"music_tracker"}515{"prompt":"Average cost of purchase orders made during the first six months of 2012.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Average cost of purchase orders made during the first six months of 2012.\n        \n        ### Context:\n        #### Query Context:\n        purchase orders refers to TransactionType = 'P'; first six months of 2012 refers to TransactionDate bewteen '2012-01-01'and '2012-06-30'; average = DIVIDE(ActualCost where TransactionType = 'P', count(TransactionID))\n\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 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once belonged to the Engineering Department?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the employees who don't wish to receive promotion e-mails, how many of them belong to or once belonged to the Engineering Department?\n        \n        ### Context:\n        #### Query Context:\n        Employees who don't wish to receive e-mail promotions refers to EmailPromotion = 0;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 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'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 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Chicago?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the email address to contact the administrator of Central Chicago?\n        \n        ### Context:\n        #### Query Context:\n        email address refers to email; Central Chicago refers to district_name = 'Central'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT email FROM District WHERE district_name = 'Central'"}],"db_id":"chicago_crime"}518{"prompt":"List the languages used in Turkmenistan.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the languages used in Turkmenistan.\n        \n        ### Context:\n        #### Query Context:\n        Turkmenistan is a name of country;\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Language FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode WHERE T1.Name = 'Turkmenistan'"}],"db_id":"world"}519{"prompt":"What is the keyword for episodes with stars score of 10 at 30% and above?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the keyword for episodes with stars score of 10 at 30% and above?\n        \n        ### Context:\n        #### Query Context:\n        stars score of 10 at 30% and above refers to stars = 10 and percent > 29\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.keyword FROM Keyword AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T2.stars = 10 AND T2.percent > 29;"}],"db_id":"simpson_episodes"}520{"prompt":"How many of the papers are preprinted?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the papers are preprinted?\n        \n        ### Context:\n        #### Query Context:\n        year = 0 means this paper is preprint; papers refers to Paper.Id\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(Id) FROM Paper WHERE ConferenceId = 0 AND JournalId = 0"}],"db_id":"authors"}521{"prompt":"Name the item ordered by Jonathan Doherty with the highest quantity in the East region.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the item ordered by Jonathan Doherty with the highest quantity in the East region.\n        \n        ### Context:\n        #### Query Context:\n        Jonathan Doherty is the \"Customer Name\"; highest quantity refers to MAX(Quantity); Region = 'East'\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.`Product Name` FROM east_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T1.`Product ID` WHERE T2.`Customer Name` = 'Jonathan Doherty' AND T2.Region = 'East' ORDER BY T1.Quantity DESC LIMIT 1"}],"db_id":"superstore"}522{"prompt":"Name of the publisher of the game id 10031.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name of the publisher of the game id 10031.\n        \n        ### Context:\n        #### Query Context:\n        name of publisher refers to publisher_name; the game id 10031 refers to game_id = '10031'\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.publisher_name FROM game_publisher AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T1.game_id = 10031"}],"db_id":"video_games"}523{"prompt":"State the name of all territories in Northern region.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the name of all territories in Northern region.\n        \n        ### Context:\n        #### Query Context:\n        name of all territories refers to TerritoryDescription; Northern region refers to RegionDescription = 'Northern'\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.TerritoryDescription FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID WHERE T2.RegionDescription = 'Northern'"}],"db_id":"retail_world"}524{"prompt":"Among the students who got a B in the course Machine Learning Theory, how many of them have a gpa of over 3?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the students who got a B in the course Machine Learning Theory, how many of them have a gpa of over 3?\n        \n        ### Context:\n        #### Query Context:\n        B refers to grade; GPA is an abbreviated name of Grade Point Average where over 3 refers to gpa > 3;\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(student_id) FROM registration WHERE grade = 'B' AND student_id IN ( SELECT student_id FROM student WHERE gpa > 3 AND course_id IN ( SELECT course_id FROM course WHERE name = 'Machine Learning Theory' ) )"}],"db_id":"cs_semester"}525{"prompt":"Please list all the customers that have Steve Patterson as their sales representitive.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list all the customers that have Steve Patterson as their sales representitive.\n        \n        ### Context:\n        #### Query Context:\n        Steve Patterson is an employee;\n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT t1.customerName FROM customers AS t1 INNER JOIN employees AS t2 ON t1.salesRepEmployeeNumber = t2.employeeNumber WHERE t2.firstName = 'Steve' AND t2.lastName = 'Patterson'"}],"db_id":"car_retails"}526{"prompt":"Calculate the average runs scored during the first half of all first innings.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average runs scored during the first half of all first innings.\n        \n        ### Context:\n        #### Query Context:\n        first half refers to 1 < Over_Id and Over_Id < 25; average = divide(sum(Over_Id) when 1 < Over_Id and Over_Id < 25, sum(Runs_Scored)) as percentage; first innings refers to Innings_No = 1\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN 1 < Over_Id AND Over_Id < 25 THEN 1 ELSE 0 END) AS REAL) * 100 \/ SUM(Runs_Scored) FROM Batsman_Scored WHERE Innings_No = 1"}],"db_id":"soccer_2016"}527{"prompt":"What genre of movie has Taran as the main character?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What genre of movie has Taran as the main character?\n        \n        ### Context:\n        #### Query Context:\n        Taran is the main character of the movie which refers to hero = 'Taran';\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.genre FROM movies_total_gross AS T1 INNER JOIN characters AS T2 ON T1.movie_title = T2.movie_title WHERE T2.hero = 'Taran'"}],"db_id":"disney"}528{"prompt":"What is the total number of active businesses in AZ with a medium review count?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the total number of active businesses in AZ with a medium review count?\n        \n        ### Context:\n        #### Query Context:\n        active businesses refers to active = 'true'; AZ is a state; medium review count refers to review_count = 'Medium'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM Business WHERE review_count = 'Medium' AND state = 'AZ' AND active = 'true'"}],"db_id":"public_review_platform"}529{"prompt":"List all the first words of the biwords pair where the second word is 'antic'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the first words of the biwords pair where the second word is 'antic'.\n        \n        ### Context:\n        #### Query Context:\n        first words refer to w1st.word; second word is 'antic' refers to w2nd.word = 'antic'\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.word FROM words AS T1 INNER JOIN biwords AS T2 ON T1.wid = T2.w1st WHERE T2.w2nd = ( SELECT wid FROM words WHERE word = 'antic' )"}],"db_id":"language_corpus"}530{"prompt":"Identify the most popular and appealing active business in Gilbert based on users' reviews.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify the most popular and appealing active business in Gilbert based on users' reviews.\n        \n        ### Context:\n        #### Query Context:\n        most popular and appealing refers to review_count = 'High' and max(stars); active business refers to active = 'true'; in Gilbert refers to city = 'Gilbert'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT business_id FROM Business WHERE city = 'Gilbert' AND active = 'true' AND review_count = 'High' ORDER BY stars DESC LIMIT 1"}],"db_id":"public_review_platform"}531{"prompt":"How many \"food maintenance\" related violations did inspection no.1454071 have?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many \"food maintenance\" related violations did inspection no.1454071 have?\n        \n        ### Context:\n        #### Query Context:\n        \"food maintenance\" related refers to category = 'Food Maintenance'; inspection no.1454071 refers to inspection_id = '1454071'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.point_id) FROM inspection_point AS T1 INNER JOIN violation AS T2 ON T1.point_id = T2.point_id WHERE T2.inspection_id = '1454071' AND T1.category = 'Food Maintenance'"}],"db_id":"food_inspection_2"}532{"prompt":"List all the negative comments on the \"Dog Run - Pet Dog Simulator\" app.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the negative comments on the \"Dog Run - Pet Dog Simulator\" app.\n        \n        ### Context:\n        #### Query Context:\n        negative comment refers to Sentiment = 'Negative';\n        \n        #### Database Schema:\n        {'db_id': 'app_store', 'table_names_original': ['playstore', 'user_reviews'], 'table_names': ['googleplaystore', 'googleplaystore_user_reviews'], 'column_names_original': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment_Polarity'], [1, 'Sentiment_Subjectivity']], 'column_names': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment Polarity'], [1, 'Sentiment Subjectivity']], 'column_types': ['text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [], 'foreign_keys': [[11, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Translated_Review FROM user_reviews WHERE App = 'Dog Run - Pet Dog Simulator' AND Sentiment = 'Negative'"}],"db_id":"app_store"}533{"prompt":"Who is the oldest married male? State his job title.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the oldest married male? State his job title.\n        \n        ### Context:\n        #### Query Context:\n        Male refers to Gender = 'M'; married refers to MaritalStatus = 'M'; oldest refers to Min(BirthDate);\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 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[218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.FirstName, T2.LastName, T1.JobTitle FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.Gender = 'M' AND T1.MaritalStatus = 'M' ORDER BY T1.BirthDate LIMIT 1"}],"db_id":"works_cycles"}534{"prompt":"How much is the total purchase price, including freight, of the top 2 most expensive products?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much is the total purchase price, including freight, of the top 2 most expensive products?\n        \n        ### Context:\n        #### Query Context:\n        total purchase price including freight refers to add(multiply(UnitPrice , Quantity) , Freight); most expensive refers to max(UnitPrice)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.UnitPrice * T2.Quantity + T1.Freight FROM Orders AS T1 INNER JOIN `Order Details` AS T2 ON T1.OrderID = T2.OrderID ORDER BY T2.UnitPrice * T2.Quantity + T1.Freight DESC LIMIT 2"}],"db_id":"retail_world"}535{"prompt":"What is the category for the \"Moist Boys\" podcast?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the category for the \"Moist Boys\" podcast?\n        \n        ### Context:\n        #### Query Context:\n        \"Moist Boys\" refers to title of podcast\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT category FROM categories WHERE podcast_id IN ( SELECT podcast_id FROM podcasts WHERE title = 'Moist Boys' )"}],"db_id":"music_platform_2"}536{"prompt":"Name the streets in Dallas.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the streets in Dallas.\n        \n        ### Context:\n        #### Query Context:\n        \"Dallas\" is the city; streets refers to street_name\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT street_name FROM address WHERE city = 'Dallas'"}],"db_id":"books"}537{"prompt":"How many users participated in the mental health survey for 2014?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many users participated in the mental health survey for 2014?\n        \n        ### Context:\n        #### Query Context:\n        mental health survey for 2014 refers to SurveyID = 2014\n        \n        #### Database Schema:\n        {'db_id': 'mental_health_survey', 'table_names_original': ['Question', 'Survey', 'Answer'], 'table_names': ['Question', 'Survey', 'Answer'], 'column_names_original': [[-1, '*'], [0, 'questiontext'], [0, 'questionid'], [1, 'SurveyID'], [1, 'Description'], [2, 'AnswerText'], [2, 'SurveyID'], [2, 'UserID'], [2, 'QuestionID']], 'column_names': [[-1, '*'], [0, 'question text'], [0, 'question id'], [1, 'Survey ID'], [1, 'Description'], [2, 'Answer Text'], [2, 'Survey ID'], [2, 'User ID'], [2, 'Question ID']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [2, 3, [7, 8]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.UserID) FROM Answer AS T1 INNER JOIN Survey AS T2 ON T1.SurveyID = T2.SurveyID WHERE T2.Description = 'mental health survey for 2014'"}],"db_id":"mental_health_survey"}538{"prompt":"Which school requested the highest amount of resources from Amazon? State the school's ID.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which school requested the highest amount of resources from Amazon? State the school's ID.\n        \n        ### Context:\n        #### Query Context:\n        highest amount of resources refers to max(count(schoolid)); Amazon refers to vendor_name = 'Amazon'\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.schoolid FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T1.vendor_name LIKE 'Amazon' GROUP BY T2.schoolid ORDER BY COUNT(T1.vendor_name) DESC LIMIT 1"}],"db_id":"donor"}539{"prompt":"Please provide the names of any three away teams that competed in the Italian divisions.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please provide the names of any three away teams that competed in the Italian divisions.\n        \n        ### Context:\n        #### Query Context:\n        Italian means belong to country = 'Italy\";\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.AwayTeam FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div=T2.division WHERE T2.country = 'Italy' LIMIT 3"}],"db_id":"european_football_1"}540{"prompt":"Which winning team in the 1947 playoff quarterfinals managed to score 3,513 defensive points that same year?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which winning team in the 1947 playoff quarterfinals managed to score 3,513 defensive points that same year?\n        \n        ### Context:\n        #### Query Context:\n        team refers to tmID; quarterfinal refers to round = 'QF'; score 3,513 defensive points refers to d_pts > = 3513\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], 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'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.tmID FROM series_post AS T1 INNER JOIN teams AS T2 ON T1.tmIDWinner = T2.tmID WHERE T1.year = 1947 AND T1.round = 'QF' AND T2.d_pts = 3513"}],"db_id":"professional_basketball"}541{"prompt":"What is the title of the highest-budget film to date? Please include the revenue and name the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of the highest-budget film to date? Please include the revenue and name the country.\n        \n        ### Context:\n        #### Query Context:\n        highest-budget film refers to max(budget); name the country refers to country_name\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title, T1.revenue, T3.COUNTry_name FROM movie AS T1 INNER JOIN production_COUNTry AS T2 ON T1.movie_id = T2.movie_id INNER JOIN COUNTry AS T3 ON T2.COUNTry_id = T3.COUNTry_id ORDER BY T1.budget DESC LIMIT 1"}],"db_id":"movies_4"}542{"prompt":"What is the smallest border's length, and what form of government do the two nations bordering it have?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the smallest border's length, and what form of government do the two nations bordering it have?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Government, T3.Government FROM politics AS T1 INNER JOIN borders AS T2 ON T1.Country = T2.Country1 INNER JOIN politics AS T3 ON T3.Country = T2.Country2 ORDER BY T2.Length ASC LIMIT 1"}],"db_id":"mondial_geo"}543{"prompt":"Who is the employee that receives 82700 as their salary?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the employee that receives 82700 as their salary?\n        \n        ### Context:\n        #### Query Context:\n        employee name refers to first_name, last_name; receives 82700 as salary refers to salary = 82700\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT first_name, last_name FROM employee WHERE salary = 82700"}],"db_id":"food_inspection_2"}544{"prompt":"What is the discounted price of line item number 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the discounted price of line item number 1?\n        \n        ### Context:\n        #### Query Context:\n        discounted price refers to multiply(l_extendedprice, subtract(1, l_discount)); line item number 1 refers to l_linenumber = 1\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT l_extendedprice * (1 - l_discount) FROM lineitem WHERE l_linenumber = 1"}],"db_id":"retails"}545{"prompt":"What are the the total number of territory in each region?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the the total number of territory in each region?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(TerritoryDescription) FROM Territories WHERE RegionID IN (1, 2, 3, 4) GROUP BY RegionID"}],"db_id":"retail_world"}546{"prompt":"How many papers were published in the journal \"Concepts in Magnetic Resonance Part A\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many papers were published in the journal \"Concepts in Magnetic Resonance Part A\"?\n        \n        ### Context:\n        #### Query Context:\n        journal \"Concepts in Magnetic Resonance Part A\" refers to FullName = 'Concepts in Magnetic Resonance Part A'\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.Id) FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T1.FullName = 'Concepts in Magnetic Resonance Part A'"}],"db_id":"authors"}547{"prompt":"Who among the players won both \"Man of the Series\" and \"Orange Cap\" in the same season?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who among the players won both \"Man of the Series\" and \"Orange Cap\" in the same season?\n        \n        ### Context:\n        #### Query Context:\n        Who refers to Player_Name;\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Player_Name FROM Player AS T1 INNER JOIN Season AS T2 ON T1.Player_Id = T2.Man_of_the_Series = T2.Orange_Cap"}],"db_id":"soccer_2016"}548{"prompt":"What percentage of all tied games did the Sassuolo team play in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of all tied games did the Sassuolo team play in?\n        \n        ### Context:\n        #### Query Context:\n        tied games refer FTR = 'D'; DIVIDE(COUNT(Div where FTR = 'D', HomeTeam = 'Sassuolo' or AwayTeam = 'Sassuolo'), COUNT(Div where HomeTeam = 'Sassuolo' or AwayTeam = 'Sassuolo')) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN HomeTeam = 'Sassuolo' OR AwayTeam = 'Sassuolo' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(FTR) FROM matchs WHERE FTR = 'D'"}],"db_id":"european_football_1"}549{"prompt":"State name of students who have been absent for 5 months from school and do not have payment due.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State name of students who have been absent for 5 months from school and do not have payment due.\n        \n        ### Context:\n        #### Query Context:\n        absent for 5 months refers to month = 5; do not have payment due refers to bool = 'neg';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.name FROM longest_absense_from_school AS T1 INNER JOIN no_payment_due AS T2 ON T1.name = T2.name WHERE T1.month = 5 AND T2.bool = 'neg'"}],"db_id":"student_loan"}550{"prompt":"Between 2018 to 2020, what is the average amount of shipped orders per year under Carl Nguyen?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Between 2018 to 2020, what is the average amount of shipped orders per year under Carl Nguyen?\n        \n        ### Context:\n        #### Query Context:\n        shipped refers to ShipDate; between 2018 and 2020 refers to SUBSTR(ShipDate, -2) IN ('18', '19', '20'); 'Carl Nguyen' is the name of Sales Team; average shipped orders per year = Divide (Count(OrderNumber), 3)\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(T1.OrderNumber) AS REAL) \/ 3 FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE (T2.`Sales Team` = 'Carl Nguyen' AND ShipDate LIKE '%\/%\/18') OR (T2.`Sales Team` = 'Carl Nguyen' AND ShipDate LIKE '%\/%\/19') OR (T2.`Sales Team` = 'Carl Nguyen' AND ShipDate LIKE '%\/%\/20')"}],"db_id":"regional_sales"}551{"prompt":"What is the name of the most followed list?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the most followed list?\n        \n        ### Context:\n        #### Query Context:\n        most followed list refers to MAX(list_followers);\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT list_title FROM lists ORDER BY list_followers DESC LIMIT 1"}],"db_id":"movie_platform"}552{"prompt":"What is the percentage of crime cases that have been classified as \"drug abuse\" by the FBI and happened on the street?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of crime cases that have been classified as \"drug abuse\" by the FBI and happened on the street?\n        \n        ### Context:\n        #### Query Context:\n        \"Drug Abuse\" is the title of crime; happened on the street refers to location_description = 'STREET';\u00a0 percentage = Divide (Count(fbi_code_no where location_description = 'STREET'), Count(fbi_code_no)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.title = 'Drug Abuse' AND T1.location_description = 'STREET' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.fbi_code_no) FROM Crime AS T1 INNER JOIN FBI_Code AS T2 ON T1.fbi_code_no = T2.fbi_code_no"}],"db_id":"chicago_crime"}553{"prompt":"List all of the ids of the representatives belonging to the Democrat party in district 13 that ended their term on 1\/3\/2019?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all of the ids of the representatives belonging to the Democrat party in district 13 that ended their term on 1\/3\/2019?\n        \n        ### Context:\n        #### Query Context:\n        ids refers to bioguide; representatives refers to type = 'rep'; ended the term on 1\/3\/2019 refers to end = '2019-01-03';\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT bioguide FROM `current-terms` WHERE type = 'rep' AND party = 'Democrat' AND end = '2019-01-03' AND district = 13"}],"db_id":"legislator"}554{"prompt":"How many students enlisted in the fire-department?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students enlisted in the fire-department?\n        \n        ### Context:\n        #### Query Context:\n        enlisted in the fire-department refers to organ = 'fire_department';\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(name) FROM enlist WHERE organ = 'fire_department'"}],"db_id":"student_loan"}555{"prompt":"How many events did the 88-years-old male users participate on 4th May,2016?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many events did the 88-years-old male users participate on 4th May,2016?\n        \n        ### Context:\n        #### Query Context:\n        88-years-old refers to age = 88; male refers to gender = 'M'; on 4th May, 2016 refers to timestamp LIKE '2016-05-04%';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.event_id) FROM gender_age AS T1 INNER JOIN events AS T2 ON T2.device_id = T1.device_id WHERE T1.gender = 'M' AND SUBSTR(`timestamp`, 1, 10) = '2016-05-04' AND T1.age = 88"}],"db_id":"talkingdata"}556{"prompt":"How many draw games happened on 2018\/8\/7 for National League?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many draw games happened on 2018\/8\/7 for National League?\n        \n        ### Context:\n        #### Query Context:\n        National League is the name of division; Date = '2018-08-07'; draw refers to FTR = 'D'; games refer to Div;\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.FTR) FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.name = 'National League' AND T1.Date = '2018-08-07' AND T1.FTR = 'D'"}],"db_id":"european_football_1"}557{"prompt":"How many orders of O'Sullivan Plantations 2-Door Library in Landvery Oak in central superstore were shipped through the shipping mode with the fastest delivery speed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many orders of O'Sullivan Plantations 2-Door Library in Landvery Oak in central superstore were shipped through the shipping mode with the fastest delivery speed?\n        \n        ### Context:\n        #### Query Context:\n        'O'Sullivan Cherrywood Estates Traditional Bookcase' is the \"Product Name\"; shipping mode with the fastest delivery speed refers to \"Ship Mode\" = 'First Class'\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.`Order ID`) FROM central_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T2.`Product Name` = 'O''Sullivan Plantations 2-Door Library in Landvery Oak' AND T2.Region = 'Central' AND T1.`Ship Mode` = 'First Class'"}],"db_id":"superstore"}558{"prompt":"How many female actors acted in the movies of year 4?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many female actors acted in the movies of year 4?\n        \n        ### Context:\n        #### Query Context:\n        Female actors mean that a_gender = 'F'\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.actorid) FROM movies AS T1 INNER JOIN movies2actors AS T2 ON T1.movieid = T2.movieid INNER JOIN actors AS T3 ON T2.actorid = T3.actorid WHERE T3.a_gender = 'F' AND T1.year = 4"}],"db_id":"movielens"}559{"prompt":"How many platforms are available for the game Pro Evolution Soccer 2016?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many platforms are available for the game Pro Evolution Soccer 2016?\n        \n        ### Context:\n        #### Query Context:\n        game Pro Evolution Soccer 2016 refers to game_name = 'Pro Evolution Soccer 2016'\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.id) FROM game_platform AS T1 INNER JOIN platform AS T2 ON T1.platform_id = T2.id INNER JOIN game_publisher AS T3 ON T1.game_publisher_id = T3.id INNER JOIN game AS T4 ON T3.game_id = T4.id WHERE T4.game_name = 'Pro Evolution Soccer 2016'"}],"db_id":"video_games"}560{"prompt":"Give the name of the movie with a revenue of 559852396.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the name of the movie with a revenue of 559852396.\n        \n        ### Context:\n        #### Query Context:\n        name of the movie refers to title; revenue of 559852396 refers to revenue = '559852396'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT title FROM movie WHERE revenue = 559852396"}],"db_id":"movies_4"}561{"prompt":"Describe ID and active status of the business under category of \"Diagnostic Imaging\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Describe ID and active status of the business under category of \"Diagnostic Imaging\".\n        \n        ### Context:\n        #### Query Context:\n        ID refers to business_id; category of \"Diagnostic Imaging\" refers to category_name = 'Diagnostic Imaging';\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.business_id, T3.active FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T1.category_name = 'Diagnostic Imaging'"}],"db_id":"public_review_platform"}562{"prompt":"Which school district was Future Einsteins Of America project located at?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which school district was Future Einsteins Of America project located at?\n        \n        ### Context:\n        #### Query Context:\n        Future Einsteins of America refer to title\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.school_district FROM projects AS T1 INNER JOIN essays AS T2 ON T1.projectid = T2.projectid WHERE T2.title LIKE 'Future Einsteins Of America'"}],"db_id":"donor"}563{"prompt":"Calculate the percentage of products supplied by Gai pturage over all products.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the percentage of products supplied by Gai pturage over all products.\n        \n        ### Context:\n        #### Query Context:\n        \"Gai pturage\" is the CompanyName; Percentage = Divide (Count(SupplierID where CompanyName = 'Gai pturage'), Count (SupplierID)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T2.CompanyName = 'Gai pturage' THEN 1 ELSE NULL END) AS REAL) * 100 \/ COUNT(T1.SupplierID) FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID"}],"db_id":"retail_world"}564{"prompt":"Identify all installed and activated apps by their id.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Identify all installed and activated apps by their id.\n        \n        ### Context:\n        #### Query Context:\n        installed refers to is_installed = 1; activated refers to is_active = 1; id refers to app_id;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT app_id FROM app_events WHERE is_active = 1 AND is_installed = 1"}],"db_id":"talkingdata"}565{"prompt":"What is the discounted price of the part \"burnished seashell gainsboro navajo chocolate\" in order no.1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the discounted price of the part \"burnished seashell gainsboro navajo chocolate\" in order no.1?\n        \n        ### Context:\n        #### Query Context:\n        part \"burnished seashell gainsboro navajo chocolate\" refers to p_name = 'burnished seashell gainsboro navajo chocolate'; order no.1 refers to l_orderkey = 1; discounted price refers to multiply(l_extendedprice, subtract(1, l_discount))\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.l_extendedprice * (1 - T1.l_discount) FROM lineitem AS T1 INNER JOIN part AS T2 ON T1.l_partkey = T2.p_partkey WHERE T2.p_name = 'burnished seashell gainsboro navajo chocolate' AND T1.l_orderkey = 1"}],"db_id":"retails"}566{"prompt":"Is there a work order where the planned cost is different from the actual cost?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Is there a work order where the planned cost is different from the actual cost?\n        \n        ### Context:\n        #### Query Context:\n        planned cost is different from actual cost refers to ActualCost ! = PlannedCost;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, 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[64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email 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'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 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'text', 'real', 'real', 'text', 'datetime', 'integer', 'text', 'real', 'text', 'text', 'datetime', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'text', 'integer', 'real', 'datetime', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'datetime', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN ActualCost = PlannedCost THEN 'No' ELSE 'Yes' END FROM WorkOrderRouting"}],"db_id":"works_cycles"}567{"prompt":"How many stars on average does user no.3 give to Yelp_Business in Arizona?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many stars on average does user no.3 give to Yelp_Business in Arizona?\n        \n        ### Context:\n        #### Query Context:\n        user no.3 refers to user_id = 3; in Arizona refers to state = 'AZ'; stars on average = avg(review_stars(user_id = 3))\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.review_stars) FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id WHERE T1.state LIKE 'AZ' AND T2.user_id = 3"}],"db_id":"public_review_platform"}568{"prompt":"Give the object number of the sample which has the relationship of \"lying on\" with object sample no.1 from image no.2345524.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the object number of the sample which has the relationship of \"lying on\" with object sample no.1 from image no.2345524.\n        \n        ### Context:\n        #### Query Context:\n        object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.OBJ1_SAMPLE_ID FROM PRED_CLASSES AS T1 INNER JOIN IMG_REL AS T2 ON T1.PRED_CLASS_ID = T2.PRED_CLASS_ID WHERE T2.IMG_ID = 2345524 AND T1.PRED_CLASS = 'lying on' AND T2.OBJ2_SAMPLE_ID = 1"}],"db_id":"image_and_language"}569{"prompt":"What is the air carrier's description of the cancelled flights?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the air carrier's description of the cancelled flights?\n        \n        ### Context:\n        #### Query Context:\n        cancelled flights refers to CANCELLED = 1;\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Description FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.CANCELLED = 1 GROUP BY T1.Description"}],"db_id":"airline"}570{"prompt":"Write down the times when there is no available bike to borrow in a station. List down the stations name and location coordinate.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Write down the times when there is no available bike to borrow in a station. List down the stations name and location coordinate.\n        \n        ### Context:\n        #### Query Context:\n        no available bike to borrow refers to bikes_available = 0; latitude and longitude coordinates can be used to indicate a location;\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.time, T1.name, T1.lat, T1.long FROM station AS T1 INNER JOIN status AS T2 ON T2.station_id = T1.id WHERE T2.bikes_available = 0"}],"db_id":"bike_share_1"}571{"prompt":"How many times does the consumer have no dispute over a non-timely response from the company?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many times does the consumer have no dispute over a non-timely response from the company?\n        \n        ### Context:\n        #### Query Context:\n        no dispute refers to Consumer disputed? = 'No'; non-timely response refers to Timely response? = 'No'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(`Timely response?`) FROM events WHERE `Timely response?` = 'No' AND `Consumer disputed?` = 'No'"}],"db_id":"retail_complains"}572{"prompt":"Which product did Phillina Ober buy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which product did Phillina Ober buy?\n        \n        ### Context:\n        #### Query Context:\n        product refers to \"Product Name\"\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.`Product Name` FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.`Customer Name` = 'Phillina Ober'"}],"db_id":"superstore"}573{"prompt":"Among the customers from the United States, which market segment has the highest number of customers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the customers from the United States, which market segment has the highest number of customers?\n        \n        ### Context:\n        #### Query Context:\n        the highest number of customers refer to MAX(COUNT(c_custkey)); the United States is the name of the nation which refers to n_name = 'UNITED STATES'; market segment refers to c_mktsegment;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.c_mktsegment FROM ( SELECT T1.c_mktsegment, COUNT(T1.c_custkey) AS num FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T2.n_name = 'UNITED STATES' GROUP BY T1.c_mktsegment ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"retails"}574{"prompt":"How many businesses in the city of Scottsdale open on Sunday at 12PM?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many businesses in the city of Scottsdale open on Sunday at 12PM?\n        \n        ### Context:\n        #### Query Context:\n        businesses that opened on Sunday refers to day_of_week = 'Sunday'; businesses that opened at 12PM refers to opening_time = '12PM'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.business_id) FROM Business AS T1 INNER JOIN Business_hours AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T1.city = 'Scottsdale' AND T3.day_of_week = 'Sunday' AND T2.opening_time = '12PM'"}],"db_id":"public_review_platform"}575{"prompt":"Among the cars with 8 cylinders, what is the name of the one that's the most expensive?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the cars with 8 cylinders, what is the name of the one that's the most expensive?\n        \n        ### Context:\n        #### Query Context:\n        with 8 cylinders refers to cylinders = 8; name of the car refers to car_name; the most expensive refers to max(price)\n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.car_name FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID WHERE T1.cylinders = 8 ORDER BY T2.price DESC LIMIT 1"}],"db_id":"cars"}576{"prompt":"How many cars running east have double-sided tail cars?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cars running east have double-sided tail cars?\n        \n        ### Context:\n        #### Query Context:\n        east is an direction; double-sided refers to sides = 'double'; tail refers to carsposition = trailPosi\n        \n        #### Database Schema:\n        {'db_id': 'trains', 'table_names_original': ['cars', 'trains'], 'table_names': ['cars', 'trains'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'train_id'], [0, 'position'], [0, 'shape'], [0, 'len'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load_num'], [1, 'id'], [1, 'direction']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'train id'], [0, 'position'], [0, 'shape'], [0, 'length'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load number'], [1, 'id'], [1, 'direction']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 11], 'foreign_keys': [[2, 11]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.id) FROM trains AS T1 INNER JOIN cars AS T2 ON T1.id = T2.train_id INNER JOIN ( SELECT train_id, MAX(position) AS trailPosi FROM cars GROUP BY train_id ) AS T3 ON T1.id = T3.train_id WHERE T1.direction = 'east' AND T2.position = T3.trailPosi AND T2.sides = 'double'"}],"db_id":"trains"}577{"prompt":"Please provide the name of businesses with user id \"16328\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please provide the name of businesses with user id \"16328\".\n        \n        ### Context:\n        #### Query Context:\n        name of business refers to category_name;\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.category_name FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Tips AS T3 ON T2.business_id = T3.business_id WHERE T3.user_id = 16328"}],"db_id":"public_review_platform"}578{"prompt":"Which product has the highest standard cost?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which product has the highest standard cost?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 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[197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Name FROM Product ORDER BY StandardCost DESC LIMIT 1"}],"db_id":"works_cycles"}579{"prompt":"Among the employees that receive a salary between $75000 to $85000, what is the difference between the number of employees which undergone an inspection that fined 100 and 500?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the employees that receive a salary between $75000 to $85000, what is the difference between the number of employees which undergone an inspection that fined 100 and 500?\n        \n        ### Context:\n        #### Query Context:\n        salary between $75000 and $85000 refers to 75000 < = salary < = 80000; difference = subtract(count(inspection_id where fine = 100), count(inspection_id where fine = 500)) where 75000 < = salary < = 80000\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T3.fine = 100 THEN 1 ELSE 0 END) - SUM(CASE WHEN T3.fine = 500 THEN 1 ELSE 0 END) FROM employee AS T1 INNER JOIN inspection AS T2 ON T1.employee_id = T2.employee_id INNER JOIN violation AS T3 ON T2.inspection_id = T3.inspection_id WHERE T1.salary BETWEEN 75000 AND 80000"}],"db_id":"food_inspection_2"}580{"prompt":"Which episodes of the Law & Order have been nominated for the Primetime Emmy Awards?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which episodes of the Law & Order have been nominated for the Primetime Emmy Awards?\n        \n        ### Context:\n        #### Query Context:\n        episode refers to award; the Primetime Emmy Awards refers to award_category like 'Primetime Emmy'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT episode_id FROM Award WHERE award_category = 'Primetime Emmy'"}],"db_id":"law_episode"}581{"prompt":"What is the reason for sales order \"51883\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the reason for sales order \"51883\"?\n        \n        ### Context:\n        #### Query Context:\n        reason means the category of sales reason which refers to ReasonType\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 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'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], 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'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name FROM SalesOrderHeaderSalesReason AS T1 INNER JOIN SalesReason AS T2 ON T1.SalesReasonID = T2.SalesReasonID WHERE T1.SalesOrderID = 51883"}],"db_id":"works_cycles"}582{"prompt":"Who is the author of First Things First?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the author of First Things First?\n        \n        ### Context:\n        #### Query Context:\n        \"First Things First\" is the title of the book; author refers to author_name\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.author_name FROM book AS T1 INNER JOIN book_author AS T2 ON T1.book_id = T2.book_id INNER JOIN author AS T3 ON T3.author_id = T2.author_id WHERE T1.title = 'First Things First'"}],"db_id":"books"}583{"prompt":"What is the care plan, procedure, medication and the patient's full name for encounter 6f2e3935-b203-493e-a9c0-f23e847b9798?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the care plan, procedure, medication and the patient's full name for encounter 6f2e3935-b203-493e-a9c0-f23e847b9798?\n        \n        ### Context:\n        #### Query Context:\n        car plan refers to careplans.DESCRIPTION; procedure refers to procedures.DESCRIPTION; medication refers to medications.DESCRIPTION; full name = first, last; encounter refers to encounters.ID; encounters.ID = '6f2e3935-b203-493e-a9c0-f23e847b9798';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.DESCRIPTION, T4.DESCRIPTION, T5.DESCRIPTION, T1.first, T1.last FROM patients AS T1 INNER JOIN encounters AS T2 ON T1.patient = T2.PATIENT INNER JOIN careplans AS T3 ON T1.patient = T3.PATIENT INNER JOIN procedures AS T4 ON T1.patient = T4.PATIENT INNER JOIN medications AS T5 ON T1.patient = T5.PATIENT WHERE T2.ID = '6f2e3935-b203-493e-a9c0-f23e847b9798'"}],"db_id":"synthea"}584{"prompt":"What is the lowest minimum temperature recorded in store 16 on January 2012?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the lowest minimum temperature recorded in store 16 on January 2012?\n        \n        ### Context:\n        #### Query Context:\n        lowest minimum temperature refers to Min(tmin); store 16 refers to store_nbr = 16; on January 2012 refers to Substring (date, 1, 7) = '2012-01'\n        \n        #### Database Schema:\n        {'db_id': 'sales_in_weather', 'table_names_original': ['sales_in_weather', 'weather', 'relation'], 'table_names': ['sales_in_weather', 'weather', 'relation'], 'column_names_original': [[-1, '*'], [0, 'date'], [0, 'store_nbr'], [0, 'item_nbr'], [0, 'units'], [1, 'station_nbr'], [1, 'date'], [1, 'tmax'], [1, 'tmin'], [1, 'tavg'], [1, 'depart'], [1, 'dewpoint'], [1, 'wetbulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'codesum'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'stnpressure'], [1, 'sealevel'], [1, 'resultspeed'], [1, 'resultdir'], [1, 'avgspeed'], [2, 'store_nbr'], [2, 'station_nbr']], 'column_names': [[-1, '*'], [0, 'date'], [0, 'store number'], [0, 'item number'], [0, 'units'], [1, 'station number'], [1, 'date'], [1, 'temperature max'], [1, 'temperature min'], [1, 'temperature average'], [1, 'departure from normal'], [1, 'dew point'], [1, 'wet bulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'code summarization'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'station pressure'], [1, 'sea level'], [1, 'resultant speed'], [1, 'resultant direction'], [1, 'average speed'], [2, 'store number'], [2, 'station number']], 'column_types': ['text', 'date', 'integer', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [5, 6], 25], 'foreign_keys': [[26, 5], [25, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MIN(tmin) FROM weather AS T1 INNER JOIN relation AS T2 ON T1.station_nbr = T2.station_nbr WHERE T2.store_nbr = 16 AND T1.`date` LIKE '%2012-01%'"}],"db_id":"sales_in_weather"}585{"prompt":"How many restaurants failed the inspection in April 2010?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many restaurants failed the inspection in April 2010?\n        \n        ### Context:\n        #### Query Context:\n        restaurant refers to facility_type = 'Restaurant'; failed the inspection refers to results = 'Fail'; in April 2010 refers to inspection_date like '2010-04%'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.license_no) FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE strftime('%Y-%m', T2.inspection_date) = '2010-04' AND T1.facility_type = 'Restaurant' AND T2.results = 'Fail'"}],"db_id":"food_inspection_2"}586{"prompt":"How many online sales were made in May 2018 where products were shipped from Norman?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many online sales were made in May 2018 where products were shipped from Norman?\n        \n        ### Context:\n        #### Query Context:\n        online sales refer to OrderNumber where Sales Channel = 'Online'; May 2018 refers to OrderDate LIKE '5\/%\/18'; Norman is the name of the city;\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.OrderDate LIKE '5\/%\/18' AND T1.`Sales Channel` = 'Online' AND T2.`City Name` = 'Norman' THEN 1 ELSE 0 END) FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID"}],"db_id":"regional_sales"}587{"prompt":"What was the hottest temperature on the day of trip ID 4080?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What was the hottest temperature on the day of trip ID 4080?\n        \n        ### Context:\n        #### Query Context:\n        the hottest temperature refers to max_temperature_f;\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(T2.max_temperature_f) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code AND T2.date = SUBSTR(CAST(T1.start_date AS TEXT), 1, INSTR(T1.start_date, ' ') - 1) WHERE T1.id = 4080"}],"db_id":"bike_share_1"}588{"prompt":"What is the cost for the sports?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the cost for the sports?\n        \n        ### Context:\n        #### Query Context:\n        cost refers to StandardCost;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 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369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.StandardCost FROM Product AS T1 INNER JOIN ProductCostHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Name LIKE '%Sport%'"}],"db_id":"works_cycles"}589{"prompt":"What is the most expensive price paid by a customer for the book \"Bite Me If You Can (Argeneau #6)\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the most expensive price paid by a customer for the book \"Bite Me If You Can (Argeneau #6)\"?\n        \n        ### Context:\n        #### Query Context:\n        \"Bite Me If You Can (Argeneau #6)\" is the title of the book; most expensive price refers to Max(price)\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT MAX(T2.price) FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id WHERE T1.title = 'Bite Me If You Can (Argeneau #6)'"}],"db_id":"books"}590{"prompt":"Provide the phone brands and models of the users who were at the coordinates of (80,44).","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the phone brands and models of the users who were at the coordinates of (80,44).\n        \n        ### Context:\n        #### Query Context:\n        models refers to device_model; coordinates of (80,44) refers to longitude = 80 AND latitude = 44;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.phone_brand, T1.device_model FROM phone_brand_device_model2 AS T1 INNER JOIN events AS T2 ON T2.device_id = T1.device_id WHERE T2.longitude = 80 AND T2.latitude = 44"}],"db_id":"talkingdata"}591{"prompt":"For all the users who gave \"A Shot in the Dark\" a rating, how many percent of them is a paying subscriber?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For all the users who gave \"A Shot in the Dark\" a rating, how many percent of them is a paying subscriber?\n        \n        ### Context:\n        #### Query Context:\n        \"A Shot in the Dark\" refers to movie_title = 'A Shot in the Dark'; paying subscriber refers to user_has_payment_method = 1; percentage refers to DIVIDE(COUNT(user_has_payment_method = 1),COUNT(user_has_payment_method))*100\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.user_has_payment_method = 1 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id INNER JOIN lists_users AS T3 ON T1.user_id = T3.user_id WHERE T2.movie_title = 'A Shot in the Dark'"}],"db_id":"movie_platform"}592{"prompt":"With 100$, how many Cable Lock can you buy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        With 100$, how many Cable Lock can you buy?\n        \n        ### Context:\n        #### Query Context:\n        number of products a $100 can buy = DIVIDE(100, ListPrice);\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the students of the Advanced Database Systems course with the highest satisfaction.\n        \n        ### Context:\n        #### Query Context:\n        full the students = f_name, l_name; course refers to name; satisfaction refers to sat; highest satisfaction refers to MAX(sat);\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.f_name, T1.l_name FROM student AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T3.name = 'Advanced Database Systems' ORDER BY T2.sat DESC LIMIT 1"}],"db_id":"cs_semester"}594{"prompt":"What is the average household income of Glendale?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average household income of Glendale?\n        \n        ### Context:\n        #### Query Context:\n        \"Glendale\" is the City Name; Average household income refers to avg(Household Income)\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(`Household Income`) FROM `Store Locations` WHERE `City Name` = 'Glendale'"}],"db_id":"regional_sales"}595{"prompt":"What is the percentage of establishments with a risk level of 1 among all of the establishments that passed the inspection?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of establishments with a risk level of 1 among all of the establishments that passed the inspection?\n        \n        ### Context:\n        #### Query Context:\n        a risk level of 1 refers to risk_level = 1; pass the inspection refers to results = 'Pass'; percentage = divide(sum(license_no where risk_level = 1), count(license_no)) * 100% where results = 'Pass'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T1.risk_level = 1 THEN T1.license_no END) AS REAL) * 100 \/ COUNT(T1.license_no) FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T2.results = 'Pass'"}],"db_id":"food_inspection_2"}596{"prompt":"List the site of institution within the student count of 500 to 1000 that has the recent year of data release.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the site of institution within the student count of 500 to 1000 that has the recent year of data release.\n        \n        ### Context:\n        #### Query Context:\n        recent year of data release refers to newest year;\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.site FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.student_count BETWEEN 500 AND 1000 AND T2.year = ( SELECT MAX(T2.year) FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid )"}],"db_id":"college_completion"}597{"prompt":"Among the customers from Houston, Texas, what is the total profit of their orders in the Central region?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the customers from Houston, Texas, what is the total profit of their orders in the Central region?\n        \n        ### Context:\n        #### Query Context:\n        customers from Houston, Texas refers to State = 'Texas' and City = 'Houston'; total profit = sum(Profit)\n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.Profit) FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.City = 'Houston' AND T1.State = 'Texas' AND T2.Region = 'Central'"}],"db_id":"superstore"}598{"prompt":"List all of the user ids and ages who rated movies with the id 1695219?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all of the user ids and ages who rated movies with the id 1695219?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.userid, T2.age FROM u2base AS T1 INNER JOIN users AS T2 ON T1.userid = T2.userid WHERE T1.movieid = 1695219"}],"db_id":"movielens"}599{"prompt":"In the complains received in 2012, how many of them are submitted through email?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In the complains received in 2012, how many of them are submitted through email?\n        \n        ### Context:\n        #### Query Context:\n        received in 2012 refers to Date received LIKE '2012%'; submitted through email refers to Submitted via = 'Email'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(`Submitted via`) FROM events WHERE strftime('%Y', `Date received`) = '2012' AND `Submitted via` = 'Email'"}],"db_id":"retail_complains"}600{"prompt":"How much is the total amount of sales handled by Heather McBadden?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much is the total amount of sales handled by Heather McBadden?\n        \n        ### Context:\n        #### Query Context:\n        total amount of sales = SUM(MULTIPLY(Quantity, Price));\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.Quantity * T3.Price) FROM Employees AS T1 INNER JOIN Sales AS T2 ON T1.EmployeeID = T2.SalesPersonID INNER JOIN Products AS T3 ON T2.ProductID = T3.ProductID WHERE T1.FirstName = 'Heather' AND T1.LastName = 'McBadden'"}],"db_id":"sales"}601{"prompt":"Provide the number of predicted classes.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the number of predicted classes.\n        \n        ### Context:\n        #### Query Context:\n        predicted classes refers to PRED_CLASS\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(PRED_CLASS_ID) FROM PRED_CLASSES"}],"db_id":"image_and_language"}602{"prompt":"List 10 cities with a median age over 40. Include their zip codes and area codes.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List 10 cities with a median age over 40. Include their zip codes and area codes.\n        \n        ### Context:\n        #### Query Context:\n        median age over 40 refers to median_age > 40\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.city, T2.zip_code, T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.median_age >= 40 LIMIT 10"}],"db_id":"address"}603{"prompt":"Which Asian country gave its agricultural sector the largest share of its gross domestic product?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which Asian country gave its agricultural sector the largest share of its gross domestic product?\n        \n        ### Context:\n        #### Query Context:\n        Gross domestic product = GDP; Largest share of GDP in agricultural sector was mentioned in economy.Agriculture\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Country FROM continent AS T1 INNER JOIN encompasses AS T2 ON T1.Name = T2.Continent INNER JOIN country AS T3 ON T2.Country = T3.Code INNER JOIN economy AS T4 ON T4.Country = T3.Code WHERE T1.Name = 'Asia' ORDER BY T4.Agriculture DESC LIMIT 1"}],"db_id":"mondial_geo"}604{"prompt":"Calculate the percentage of high-level undergraduate course.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the percentage of high-level undergraduate course.\n        \n        ### Context:\n        #### Query Context:\n        high-level undergraduate course refers to courseLevel = 'Level_400'; percentage = divide(count(course.course_id) when courseLevel = 'Level_400', count(course.course_id)) * 100%\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE  WHEN courseLevel = 'Level_400' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) AS per FROM course"}],"db_id":"computer_student"}605{"prompt":"In the Winter and Summer Olympics of 1988, which game has the most number of competitors? Find the difference of the number of competitors between the two games.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In the Winter and Summer Olympics of 1988, which game has the most number of competitors? Find the difference of the number of competitors between the two games.\n        \n        ### Context:\n        #### Query Context:\n        the most number of competitors refer to MAX(COUNT(person_id)); SUBTRACT(COUNT(person_id where games_name = '1988 Summer'), COUNT(person_id where games_name = '1988 Winter'));\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT P1 , ( SELECT MAX(P2) - MIN(P2) FROM ( SELECT COUNT(T2.person_id) AS P2 FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.games_name IN ('1988 Winter', '1988 Summer') GROUP BY T1.season ) ORDER BY P2 DESC LIMIT 1 ) FROM ( SELECT T1.season AS P1, COUNT(T2.person_id) AS P2 FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.games_name IN ('1988 Winter', '1988 Summer') GROUP BY T1.season ) ORDER BY P2 DESC LIMIT 1"}],"db_id":"olympics"}606{"prompt":"What is the id of Rio de Janeiro?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the id of Rio de Janeiro?\n        \n        ### Context:\n        #### Query Context:\n        Rio de Janeiro refers to city_name = 'Rio de Janeiro';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT id FROM city WHERE city_name = 'Rio de Janeiro'"}],"db_id":"olympics"}607{"prompt":"What percentage of countries became independent during the year 1960?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of countries became independent during the year 1960?\n        \n        ### Context:\n        #### Query Context:\n        Percentage = count(countries independent 1960) \/ total num of countries\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN STRFTIME('%Y', Independence) = '1960' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(Country) FROM politics"}],"db_id":"mondial_geo"}608{"prompt":"State the full name of clients with server time of 20 minutes and above.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the full name of clients with server time of 20 minutes and above.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first, middle, last; server time of 20 minutes and above refers to ser_time > '00:20:00'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN callcenterlogs AS T2 ON T1.client_id = T2.`rand client` WHERE strftime('%M', T2.ser_time) > '20'"}],"db_id":"retail_complains"}609{"prompt":"What are the names of the parts manufactured by manufacturer 3 that have a supply cost of 1,000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the names of the parts manufactured by manufacturer 3 that have a supply cost of 1,000?\n        \n        ### Context:\n        #### Query Context:\n        names of the parts refer to p_name; manufacturer 3 refers to p_mfgr = 'Manufacturer#3'; ps_supplycost = 1000;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.p_name FROM partsupp AS T1 INNER JOIN part AS T2 ON T1.ps_partkey = T2.p_partkey WHERE T1.ps_supplycost = 1000 AND T2.p_mfgr = 'Manufacturer#3'"}],"db_id":"retails"}610{"prompt":"List out the birth name of crews who are co-executive producer and higher than 1.60 meters.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List out the birth name of crews who are co-executive producer and higher than 1.60 meters.\n        \n        ### Context:\n        #### Query Context:\n        co-executive producer refers to role = 'co-executive producer'; higher than 1.60 meters refers to height_meters > 1.60\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.birth_name FROM Person AS T1 INNER JOIN Award AS T2 ON T1.name = T2.person WHERE T2.role = 'co-executive producer' AND T1.height_meters > 1.60;"}],"db_id":"simpson_episodes"}611{"prompt":"What is the percentage of Professor Ogdon Zywicki's research assistants are taught postgraduate students?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of Professor Ogdon Zywicki's research assistants are taught postgraduate students?\n        \n        ### Context:\n        #### Query Context:\n        research assistant refers to the student who serves for research where the abbreviation is RA; taught postgraduate student refers to type = 'TPG'; DIVIDE(COUNT(student_id where type = 'TPG' and first_name = 'Ogdon', last_name = 'Zywicki'), COUNT(first_name = 'Ogdon', last_name = 'Zywicki')) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T3.type = 'TPG' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.student_id) FROM RA AS T1 INNER JOIN prof AS T2 ON T1.prof_id = T2.prof_id INNER JOIN student AS T3 ON T1.student_id = T3.student_id WHERE T2.first_name = 'Ogdon' AND T2.last_name = 'Zywicki'"}],"db_id":"cs_semester"}612{"prompt":"What is the NOC code of the region where the tallest male Olympic competitor is from?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the NOC code of the region where the tallest male Olympic competitor is from?\n        \n        ### Context:\n        #### Query Context:\n        NOC code of the region refers to noc; male refers to gender = 'M'; the tallest refers to MAX(height);\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.noc FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T3.gender = 'M' ORDER BY T3.height DESC LIMIT 1"}],"db_id":"olympics"}613{"prompt":"Please list the total number of companies with a commission percentage of 0.018 or above, along with each company's assigned geographical location.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the total number of companies with a commission percentage of 0.018 or above, along with each company's assigned geographical location.\n        \n        ### Context:\n        #### Query Context:\n        geographical location refers to group from SalesPerson; ComissionPct refers to commission percentage where ComissionPct > = 0.018;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'blob', 'text', 'blob', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'text', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'text', 'datetime', 'integer', 'datetime', 'real', 'text', 'datetime', 'integer', 'integer', 'datetime', 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'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.BusinessEntityID, T2.'Group' FROM SalesPerson AS T1 INNER JOIN SalesTerritory AS T2 USING (territoryid) WHERE T1.CommissionPct >= 0.018"}],"db_id":"works_cycles"}614{"prompt":"What is the max salary for 'Tracy Coulter' if he\/she stays on his\/her position?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the max salary for 'Tracy Coulter' if he\/she stays on his\/her position?\n        \n        ### Context:\n        #### Query Context:\n        Tracy Coulter is the full name of an employee; full name = firstname, lastname\n        \n        #### Database Schema:\n        {'db_id': 'human_resources', 'table_names_original': ['location', 'position', 'employee'], 'table_names': ['location', 'position', 'employee'], 'column_names_original': [[-1, '*'], [0, 'locationID'], [0, 'locationcity'], [0, 'address'], [0, 'state'], [0, 'zipcode'], [0, 'officephone'], [1, 'positionID'], [1, 'positiontitle'], [1, 'educationrequired'], [1, 'minsalary'], [1, 'maxsalary'], [2, 'ssn'], [2, 'lastname'], [2, 'firstname'], [2, 'hiredate'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'positionID'], [2, 'locationID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'location city'], [0, 'address'], [0, 'state'], [0, 'zip code'], [0, 'office phone'], [1, 'position id'], [1, 'position title'], [1, 'education required'], [1, 'minimum salary'], [1, 'maximum salary'], [2, 'social security number'], [2, 'last name'], [2, 'first name'], [2, 'hire date'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'position id'], [2, 'location id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 7, 12], 'foreign_keys': [[19, 7], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.maxsalary FROM employee AS T1  INNER JOIN position AS T2  ON T1.positionID = T2.positionID WHERE T1.firstname = 'Tracy' AND T1.lastname = 'Coulter'"}],"db_id":"human_resources"}615{"prompt":"How many PG adventure movies did Ron Clements direct?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many PG adventure movies did Ron Clements direct?\n        \n        ### Context:\n        #### Query Context:\n        Ron Clements refers to director = 'Ron Clements'; PG is an abbreviation for parental guidance and refers to MPAA_rating = 'PG'; adventure movie refers to genre = 'Adventure';\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM director AS T1 INNER JOIN movies_total_gross AS T2 ON T1.name = T2.movie_title WHERE T1.director = 'Ron Clements' AND T2.MPAA_rating = 'PG' AND T2.genre = 'Adventure'"}],"db_id":"disney"}616{"prompt":"What is the title of the paper published in 2003 by an author with affiliation with Department of Network Science, Graduate School of Information Systems, The University of Electro-Communications?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of the paper published in 2003 by an author with affiliation with Department of Network Science, Graduate School of Information Systems, The University of Electro-Communications?\n        \n        ### Context:\n        #### Query Context:\n        published in 2003 refers to Year = 2003; 'Department of Network Science, Graduate School of Information Systems, The University of Electro-Communications' is the Affiliation organization\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.Title FROM PaperAuthor AS T1 INNER JOIN Paper AS T2 ON T1.PaperId = T2.Id WHERE T1.Affiliation = 'Department of Network Science, Graduate School of Information Systems, The University of Electro-Communications' AND T2.Year = 2003"}],"db_id":"authors"}617{"prompt":"Which business in fashion category has the most review?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which business in fashion category has the most review?\n        \n        ### Context:\n        #### Query Context:\n        'Fashion' is the category_name; most review refers to Max(Count(user_id))\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.business_id FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id INNER JOIN Reviews AS T4 ON T3.business_id = T4.business_id WHERE T1.category_name LIKE 'Fashion' AND T1.category_id = 7 GROUP BY T3.business_id ORDER BY COUNT(T4.user_id) DESC LIMIT 1"}],"db_id":"public_review_platform"}618{"prompt":"Which team that didn't play in playoffs had the most total rebounds in the year 1997?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which team that didn't play in playoffs had the most total rebounds in the year 1997?\n        \n        ### Context:\n        #### Query Context:\n        didn't play in playoffs refers to PostGP = 0; in the year 1997 refers to year = 1997; the most total rebounds refers to max(o_tmRebound)\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.tmID FROM players_teams AS T1 INNER JOIN teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.PostGP = 0 AND T1.year = 1997 ORDER BY T1.rebounds DESC LIMIT 1"}],"db_id":"professional_basketball"}619{"prompt":"How many crimes happened in longitude -8772658001?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many crimes happened in longitude -8772658001?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM Crime WHERE longitude = '-87.72658001'"}],"db_id":"chicago_crime"}620{"prompt":"Please list the phone numbers of the top 3 customers that have the highest credit limit and have Leslie Jennings as their sales representitive.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the phone numbers of the top 3 customers that have the highest credit limit and have Leslie Jennings as their sales representitive.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT t1.phone FROM customers AS t1 INNER JOIN employees AS t2 ON t1.salesRepEmployeeNumber = t2.employeeNumber WHERE t2.firstName = 'Leslie' AND t2.lastName = 'Jennings' ORDER BY t1.creditLimit DESC LIMIT 3"}],"db_id":"car_retails"}621{"prompt":"What is the full name of the customer who purchased the highest amount of total price in a single purchase?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of the customer who purchased the highest amount of total price in a single purchase?\n        \n        ### Context:\n        #### Query Context:\n        full name of the customer = FirstName, MiddleInitial, LastName; highest amount of total price refers to MAX(MULTIPLY(Quantity, Price));\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.FirstName, T2.MiddleInitial, T2.LastName FROM Sales AS T1 INNER JOIN Customers AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN Products AS T3 ON T1.ProductID = T3.ProductID GROUP BY T1.SalesID, T1.Quantity, T3.Price, FirstName, MiddleInitial, LastName ORDER BY T1.Quantity * T3.Price DESC LIMIT 1"}],"db_id":"sales"}622{"prompt":"Name the vendors that sell the item Classroom Keepers Management Center.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the vendors that sell the item Classroom Keepers Management Center.\n        \n        ### Context:\n        #### Query Context:\n        vendor refer to vendor_name; Classroom Keepers Management Center refer to item_name\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT vendor_name FROM resources WHERE item_name = 'Classroom Keepers Management Center'"}],"db_id":"donor"}623{"prompt":"Sherri Rhodes rented a film at 12:27:27 on 2005\/7\/28, when did she\/he return that film?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Sherri Rhodes rented a film at 12:27:27 on 2005\/7\/28, when did she\/he return that film?\n        \n        ### Context:\n        #### Query Context:\n        rented at 12:27:27 on 2005\/7\/28 refers to rental_date = '2005-07-28 12:27:27'\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.return_date FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id WHERE T1.first_name = 'SHERRI' AND T1.last_name = 'RHODES' AND T2.rental_date = '2005-07-28 12:27:27'"}],"db_id":"movie_3"}624{"prompt":"Which customer has rented more movies, RUTH MARTINEZ or LINDA WILLIAMS?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which customer has rented more movies, RUTH MARTINEZ or LINDA WILLIAMS?\n        \n        ### Context:\n        #### Query Context:\n        rented more movie Max(Count(customer_id)); \"RUTH MARTINEZ\" and \"LINDA WILLIAMS\" are both full name of customer\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.first_name, T.last_name FROM ( SELECT T1.first_name, T1.last_name, COUNT(T1.customer_id) AS num FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id WHERE (T1.first_name = 'RUTH' AND T1.last_name = 'MARTINEZ') OR (T1.first_name = 'LINDA' AND T1.last_name = 'WILLIAMS') GROUP BY T1.first_name, T1.last_name ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"movie_3"}625{"prompt":"Between the years 1990 and 2007, of the total rebounds achieved by each player, how many managed to exceed 75% of defensive rebounds?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Between the years 1990 and 2007, of the total rebounds achieved by each player, how many managed to exceed 75% of defensive rebounds?\n        \n        ### Context:\n        #### Query Context:\n        the year 1990 and 2007 refers to year BETWEEN 1990 AND 2007; exceed 75% of defensive rebounds refers to Divide (dRebounds, rebounds) * 100 > 75\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT playerID) FROM player_allstar WHERE CAST(d_rebounds AS REAL) * 100 \/ rebounds > 75 AND season_id BETWEEN 1990 AND 2007"}],"db_id":"professional_basketball"}626{"prompt":"Please list the full official names of all the current legislators who served the term that started on 2013\/1\/3.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the full official names of all the current legislators who served the term that started on 2013\/1\/3.\n        \n        ### Context:\n        #### Query Context:\n        term that started on 2013\/1\/3 refers to start = '2013-01-03';\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political 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research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.official_full_name FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.start = '2013-01-03'"}],"db_id":"legislator"}627{"prompt":"What are the films that are least rented by the customers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the films that are least rented by the customers?\n        \n        ### Context:\n        #### Query Context:\n        film refers to title; least rented refers to count(min(customer_id))\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.title FROM ( SELECT T3.title, COUNT(T1.customer_id) AS num FROM rental AS T1 INNER JOIN inventory AS T2 ON T1.inventory_id = T2.inventory_id INNER JOIN film AS T3 ON T2.film_id = T3.film_id GROUP BY T3.title ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"movie_3"}628{"prompt":"From year 1991 to 2000, calculate the difference betweeen the total number of papers published under the conference \"International Conference on Supercomputing \" and \"Informatik & Schule\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        From year 1991 to 2000, calculate the difference betweeen the total number of papers published under the conference \"International Conference on Supercomputing \" and \"Informatik & Schule\"?\n        \n        ### Context:\n        #### Query Context:\n        From year 1991 to 2000 refers to Year BETWEEN 1991 AND 2000; papers refers to Paper.Id; 'International Conference on Supercomputing' AND 'Informatik & Schule' are the FullName of conference; calculate the difference between the total number of papers of these two conferences refers to SUBTRACT(SUM(Paper.Id where FullName = 'International Conference on Supercomputing'), SUM(Paper.Id where FullName = 'Informatik & Schule'))\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.FullName = 'Informatik & Schule' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.FullName = 'International Conference on Supercomputing' THEN 1 ELSE 0 END) AS DIFF FROM Paper AS T1 INNER JOIN Conference AS T2 ON T1.ConferenceId = T2.Id WHERE T1.Year > 1990 AND T1.Year < 2001"}],"db_id":"authors"}629{"prompt":"In how many businesses with the ambience_trendy attribute?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In how many businesses with the ambience_trendy attribute?\n        \n        ### Context:\n        #### Query Context:\n        ambience_trendy attribute refers to attribute_name = 'ambience_trendy' AND attribute_value = 'false'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.business_id) FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T1.attribute_name = 'ambience_trendy' AND T2.attribute_value IN ('none', 'no', 'false')"}],"db_id":"public_review_platform"}630{"prompt":"Give the full name and contact number of employees in descending order of age.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the full name and contact number of employees in descending order of age.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to FirstName LastName; contact number refers to HomePhone; descending order of age refers to order by BirthDate desc limit 1\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT FirstName, LastName, HomePhone FROM Employees ORDER BY BirthDate DESC"}],"db_id":"retail_world"}631{"prompt":"User No.\"63469\" has got \"1\" like for a tip to the Yelp business, which city is that business located in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        User No.\"63469\" has got \"1\" like for a tip to the Yelp business, which city is that business located in?\n        \n        ### Context:\n        #### Query Context:\n        user No. refers to user_id;\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.city FROM Business AS T1 INNER JOIN Tips AS T2 ON T1.business_id = T2.business_id WHERE T2.likes = 1 AND T2.user_id = 63469"}],"db_id":"public_review_platform"}632{"prompt":"Which are the rivers that flows to Black Sea?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which are the rivers that flows to Black Sea?\n        \n        ### Context:\n        #### Query Context:\n        Black Sea is a sea located in Eastern Europe and Western Asia\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Name FROM river WHERE Sea = 'Black Sea'"}],"db_id":"mondial_geo"}633{"prompt":"Who is the person to contact to get Camembert Pierrot?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the person to contact to get Camembert Pierrot?\n        \n        ### Context:\n        #### Query Context:\n        Camembert Pierrot is the name of the product; person to contact refers to ContactName;\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.ContactName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T1.ProductName = 'Camembert Pierrot'"}],"db_id":"retail_world"}634{"prompt":"For the customer who gave a 3 star rating to Frostie brand on 2014\/4\/24, did the user permit the company to send regular emails to him\/her?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For the customer who gave a 3 star rating to Frostie brand on 2014\/4\/24, did the user permit the company to send regular emails to him\/her?\n        \n        ### Context:\n        #### Query Context:\n        3 star rating refers to StarRating = 3; Frostie refers to\u00a0 BrandName = 'Frostie'; if SubscribedToEmailList = 'TRUE', it means the user permit the company to send regular emails to him\/her; if SubscribedToEmailList = FALSE', it means the user did not permit the company to send regular emails to him\/her; rating on 2014\/4\/24 refers to ReviewDate = '2014-04-24';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CASE WHEN T1.SubscribedToEmailList LIKE 'TRUE' THEN 'YES' ELSE 'NO' END AS result FROM customers AS T1 INNER JOIN rootbeerreview AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN rootbeerbrand AS T3 ON T2.BrandID = T3.BrandID WHERE T2.StarRating = 3 AND T3.BrandName = 'Frostie' AND T2.ReviewDate = '2014-04-24'"}],"db_id":"beer_factory"}635{"prompt":"Among the most popular professors, how many are females?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the most popular professors, how many are females?\n        \n        ### Context:\n        #### Query Context:\n        the most popular professors refers to prof_id where MAX(popularity); female refers to gender;\n        \n        #### Database Schema:\n        {'db_id': 'cs_semester', 'table_names_original': ['course', 'prof', 'RA', 'registration', 'student'], 'table_names': ['course', 'prof', 'RA', 'registration', 'student'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'name'], [0, 'credit'], [0, 'diff'], [1, 'prof_id'], [1, 'gender'], [1, 'first_name'], [1, 'last_name'], [1, 'email'], [1, 'popularity'], [1, 'teachingability'], [1, 'graduate_from'], [2, 'student_id'], [2, 'capability'], [2, 'prof_id'], [2, 'salary'], [3, 'course_id'], [3, 'student_id'], [3, 'grade'], [3, 'sat'], [4, 'student_id'], [4, 'f_name'], [4, 'l_name'], [4, 'phone_number'], [4, 'email'], [4, 'intelligence'], [4, 'gpa'], [4, 'type']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'name'], [0, 'credit'], [0, 'difficulty'], [1, 'professor id'], [1, 'gender'], [1, 'first name'], [1, 'last name'], [1, 'email'], [1, 'popularity'], [1, 'teaching ability'], [1, 'graduate from'], [2, 'student id'], [2, 'capability'], [2, 'professor id'], [2, 'salary'], [3, 'course id'], [3, 'student id'], [3, 'grade'], [3, 'satisfying degree'], [4, 'student id'], [4, 'first name'], [4, 'last name'], [4, 'phone number'], [4, 'email'], [4, 'intelligence'], [4, 'graduate point average'], [4, 'type']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'real', 'text'], 'primary_keys': [1, 5, [13, 15], [17, 18], 21], 'foreign_keys': [[13, 21], [15, 5], [18, 21], [17, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(prof_id) FROM prof WHERE gender = 'Female' AND popularity = ( SELECT MAX(popularity) FROM prof )"}],"db_id":"cs_semester"}636{"prompt":"Among the coaches who won the 'NBA coach of the year' award from 1971 - 1975, how many of them were in 'POR' team?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the coaches who won the 'NBA coach of the year' award from 1971 - 1975, how many of them were in 'POR' team?\n        \n        ### Context:\n        #### Query Context:\n        the 'NBA coach of the year' award refers to award = 'NBA coach of the year'; from 1971 - 1975 refers to year between 1971 and 1975; 'POR' team refers to tmID = 'POR'\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.id) FROM awards_coaches AS T1 INNER JOIN teams AS T2 ON T1.year = T2.year WHERE T1.year BETWEEN 1971 AND 1975 AND T1.award = 'NBA Coach of the Year' AND T2.tmID = 'POR'"}],"db_id":"professional_basketball"}637{"prompt":"How many businesses have the category named food? List those businesses and find the percentage of businesses with less than 2 stars.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many businesses have the category named food? List those businesses and find the percentage of businesses with less than 2 stars.\n        \n        ### Context:\n        #### Query Context:\n        businesses have the category named food refer to business_id where category_name = 'Food'; DIVIDE(COUNT(business_id where category_name = 'Food' and stars < 2), COUNT(business_id where category_name = 'Food')) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.business_id, CAST((( SELECT COUNT(business_id) FROM Business WHERE stars < 2 ) - ( SELECT COUNT(business_id) FROM Business WHERE stars > 2 )) AS REAL) * 100 \/ ( SELECT COUNT(stars) FROM Business ) FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T1.business_id = T3.business_id WHERE T2.category_name = 'Food'"}],"db_id":"public_review_platform"}638{"prompt":"What is the word id for title \"Sometent\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the word id for title \"Sometent\"?\n        \n        ### Context:\n        #### Query Context:\n        word id refers to wid\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.wid FROM pages AS T1 INNER JOIN pages_words AS T2 ON T1.pid = T2.pid WHERE T1.title = 'Sometent'"}],"db_id":"language_corpus"}639{"prompt":"Name the publisher who published the most books.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the publisher who published the most books.\n        \n        ### Context:\n        #### Query Context:\n        published the most books refers to Max(Count(book_id)); publisher refers to publisher_name\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.publisher_name FROM book AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.publisher_id GROUP BY T2.publisher_name ORDER BY COUNT(T2.publisher_id) DESC LIMIT 1"}],"db_id":"books"}640{"prompt":"Which two nations are separated from one another by the longest border? Please include the entire names of the nations in your answer.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which two nations are separated from one another by the longest border? Please include the entire names of the nations in your answer.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Country1, Country2 FROM borders ORDER BY Length DESC LIMIT 1"}],"db_id":"mondial_geo"}641{"prompt":"What percentage of authors of the paper about Charged particle multiplicity are affiliated with INFN?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of authors of the paper about Charged particle multiplicity are affiliated with INFN?\n        \n        ### Context:\n        #### Query Context:\n        about Charged particle multiplicity refers to Title which contains 'Charged particle multiplicity'; affiliated with INFN refers to Affiliation LIKE '%INFN%' ; percentage refers to DIVIDE(COUNT(Affiliation LIKE '%INFN%' ), COUNT(AuthorId)) * 100%\n        \n        #### Database Schema:\n        {'db_id': 'authors', 'table_names_original': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'table_names': ['Author', 'Conference', 'Journal', 'Paper', 'PaperAuthor'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Affiliation'], [1, 'Id'], [1, 'ShortName'], [1, 'FullName'], [1, 'HomePage'], [2, 'Id'], [2, 'ShortName'], [2, 'FullName'], [2, 'HomePage'], [3, 'Id'], [3, 'Title'], [3, 'Year'], [3, 'ConferenceId'], [3, 'JournalId'], [3, 'Keyword'], [4, 'PaperId'], [4, 'AuthorId'], [4, 'Name'], [4, 'Affiliation']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 8, 12], 'foreign_keys': [[16, 8], [15, 4], [19, 1], [18, 12]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST((SUM(CASE WHEN T1.Affiliation LIKE '%INFN%' THEN 1 ELSE 0 END)) AS REAL) * 100 \/ COUNT(T2.Id) FROM PaperAuthor AS T1 INNER JOIN Paper AS T2 ON T1.PaperId = T2.Id WHERE T2.Title LIKE '%Charged particle multiplicity%'"}],"db_id":"authors"}642{"prompt":"What is the name of the customer number 93697 with the total order price of 191918.92?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the customer number 93697 with the total order price of 191918.92?\n        \n        ### Context:\n        #### Query Context:\n        customer name refers to c_name; number 93697 refers to o_custkey = 93697; total order price of 191918.92 refers to o_totalprice = 191918.92\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.c_name FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey WHERE T1.o_totalprice = 191918.92 AND T1.o_custkey = 93697"}],"db_id":"retails"}643{"prompt":"What is the average age of the people who participated in the winter season?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average age of the people who participated in the winter season?\n        \n        ### Context:\n        #### Query Context:\n        AVG(age) where season = 'Winter';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.age) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.season = 'Winter'"}],"db_id":"olympics"}644{"prompt":"What was the maximum weight of the shipment carried to Boston? Name the customer of that shipment.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What was the maximum weight of the shipment carried to Boston? Name the customer of that shipment.\n        \n        ### Context:\n        #### Query Context:\n        \"Boston\" is the city_name; maximum weight refers to Max(weight); customer refers to cust_name\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.weight, T2.cust_name FROM shipment AS T1 INNER JOIN customer AS T2 ON T1.cust_id = T2.cust_id INNER JOIN city AS T3 ON T3.city_id = T1.city_id WHERE T3.city_name = 'Boston' ORDER BY T1.weight DESC LIMIT 1"}],"db_id":"shipping"}645{"prompt":"Give the station pressure status recorded by the weather station which contained no.12 store on 2012\/5\/15.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the station pressure status recorded by the weather station which contained no.12 store on 2012\/5\/15.\n        \n        ### Context:\n        #### Query Context:\n        no.12 store refers to store_nbr = 12; on 2012\/5\/15 refers to date = '2012-05-15'; station pressure status refers to stnpressure\n        \n        #### Database Schema:\n        {'db_id': 'sales_in_weather', 'table_names_original': ['sales_in_weather', 'weather', 'relation'], 'table_names': ['sales_in_weather', 'weather', 'relation'], 'column_names_original': [[-1, '*'], [0, 'date'], [0, 'store_nbr'], [0, 'item_nbr'], [0, 'units'], [1, 'station_nbr'], [1, 'date'], [1, 'tmax'], [1, 'tmin'], [1, 'tavg'], [1, 'depart'], [1, 'dewpoint'], [1, 'wetbulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'codesum'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'stnpressure'], [1, 'sealevel'], [1, 'resultspeed'], [1, 'resultdir'], [1, 'avgspeed'], [2, 'store_nbr'], [2, 'station_nbr']], 'column_names': [[-1, '*'], [0, 'date'], [0, 'store number'], [0, 'item number'], [0, 'units'], [1, 'station number'], [1, 'date'], [1, 'temperature max'], [1, 'temperature min'], [1, 'temperature average'], [1, 'departure from normal'], [1, 'dew point'], [1, 'wet bulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'code summarization'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'station pressure'], [1, 'sea level'], [1, 'resultant speed'], [1, 'resultant direction'], [1, 'average speed'], [2, 'store number'], [2, 'station number']], 'column_types': ['text', 'date', 'integer', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [5, 6], 25], 'foreign_keys': [[26, 5], [25, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.stnpressure FROM weather AS T1 INNER JOIN relation AS T2 ON T1.station_nbr = T2.station_nbr WHERE T1.`date` = '2012-05-15' AND T2.store_nbr = 12"}],"db_id":"sales_in_weather"}646{"prompt":"Please list the names of the institutes with the percentage of male White students graduating in 2011 within 150 percent of normal\/expected time over 20.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the names of the institutes with the percentage of male White students graduating in 2011 within 150 percent of normal\/expected time over 20.\n        \n        ### Context:\n        #### Query Context:\n        names of the institutes refers to chronname; male refers to gender = 'M'; white refers to race = 'w'; in 2011 refers to year = 2011; percentage of students graduating within 150 percent of normal\/expected time over 20 refers to grad_150>20;\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T FROM ( SELECT DISTINCT CASE WHEN T2.grad_150 > 20 THEN T1.chronname ELSE NULL END AS T FROM institution_details T1 INNER JOIN institution_grads T2 ON T2.unitid = T1.unitid WHERE T2.year = 2011 AND T2.gender = 'M' AND T2.race = 'W' ) WHERE T IS NOT NULL"}],"db_id":"college_completion"}647{"prompt":"How many movie lists were still updated 10 years after it was created?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many movie lists were still updated 10 years after it was created?\n        \n        ### Context:\n        #### Query Context:\n        updated 10 years after it was created refers to list_update_timestamp_utc > (list_creation_timestamp_utc+10);\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM lists WHERE SUBSTR(list_update_timestamp_utc, 1, 4) - SUBSTR(list_creation_timestamp_utc, 1, 4) > 10"}],"db_id":"movie_platform"}648{"prompt":"How many awards did simpson 20 won in 2009?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many awards did simpson 20 won in 2009?\n        \n        ### Context:\n        #### Query Context:\n        won refers to result = 'Winner'; in 2009 refers to year = 2009\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(award_id) FROM Award WHERE SUBSTR(year, 1, 4) = '2009' AND result = 'Winner';"}],"db_id":"simpson_episodes"}649{"prompt":"What is the proportion of white males and females in the police force?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the proportion of white males and females in the police force?\n        \n        ### Context:\n        #### Query Context:\n        white refers to race = 'W'; male refers to gender = 'M'; female refers to gender = 'F'; proportion of white males = divide(count(officers where race = 'W' and gender = 'M'), count(officers)) * 100%; proportion of white females = divide(count(officers where race = 'W' and gender = 'F'), count(officers)) * 100%\n        \n        #### Database Schema:\n        {'db_id': 'shooting', 'table_names_original': ['incidents', 'officers', 'subjects'], 'table_names': ['Incidents', 'officers', 'subjects'], 'column_names_original': [[-1, '*'], [0, 'case_number'], [0, 'date'], [0, 'location'], [0, 'subject_statuses'], [0, 'subject_weapon'], [0, 'subjects'], [0, 'subject_count'], [0, 'officers'], [1, 'case_number'], [1, 'race'], [1, 'gender'], [1, 'last_name'], [1, 'first_name'], [1, 'full_name'], [2, 'case_number'], [2, 'race'], [2, 'gender'], [2, 'last_name'], [2, 'first_name'], [2, 'full_name']], 'column_names': [[-1, '*'], [0, 'case number'], [0, 'date'], [0, 'location'], [0, 'subject statuses'], [0, 'subject weapon'], [0, 'subjects'], [0, 'subject_count'], [0, 'officers'], [1, 'case number'], [1, 'race'], [1, 'gender'], [1, 'last name'], [1, 'first name'], [1, 'full name'], [2, 'case number'], [2, 'race'], [2, 'gender'], [2, 'last name'], [2, 'first name'], [2, 'full name']], 'column_types': ['text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[9, 1], [15, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(gender = 'M') AS REAL) \/ SUM(gender = 'F') FROM officers WHERE race = 'W'"}],"db_id":"shooting"}650{"prompt":"What percentage of all scenes are tragic scenes in Shakespeare's work in 1594?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of all scenes are tragic scenes in Shakespeare's work in 1594?\n        \n        ### Context:\n        #### Query Context:\n        tragic scenes refers to GenreType = 'Tragedy'; work in 1594 refers to Date = '1594'; percentage = divide((sum(Scene) when GenreType = 'Tragedy'), count(Scene))as percentage\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T2.GenreType = 'Tragedy', 1, 0)) AS REAL) * 100 \/ COUNT(T1.Scene) FROM chapters AS T1 INNER JOIN works AS T2 ON T1.work_id = T2.id WHERE T2.Date = '1594'"}],"db_id":"shakespeare"}651{"prompt":"What is the list ID that was first created by user 85981819?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the list ID that was first created by user 85981819?\n        \n        ### Context:\n        #### Query Context:\n        first created list refers to oldest list_creation_date_utc;\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT list_id FROM lists_users WHERE user_id = 85981819 ORDER BY list_creation_date_utc ASC LIMIT 1"}],"db_id":"movie_platform"}652{"prompt":"What is the role of Mark Hammel?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the role of Mark Hammel?\n        \n        ### Context:\n        #### Query Context:\n        role refers to job\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.job FROM person AS T1 INNER JOIN movie_crew AS T2 ON T1.person_id = T2.person_id WHERE T1.person_name = 'Mark Hammel'"}],"db_id":"movies_4"}653{"prompt":"What is the relationship between object sample no. 25 and object sample no. 2 on image no. 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the relationship between object sample no. 25 and object sample no. 2 on image no. 1?\n        \n        ### Context:\n        #### Query Context:\n        relationship refers to PRED_CLASS; object sample no. 25 and object sample no. 2 refers to OBJ1_SAMPLE_ID = 25 and OBJ2_SAMPLE_ID = 2; image no. 1 refers to IMG_ID = 1\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.PRED_CLASS FROM IMG_REL AS T1 INNER JOIN PRED_CLASSES AS T2 ON T1.PRED_CLASS_ID = T2.PRED_CLASS_ID WHERE T1.IMG_ID = 1 AND T1.OBJ1_SAMPLE_ID = 25 AND T1.OBJ2_SAMPLE_ID = 2"}],"db_id":"image_and_language"}654{"prompt":"Among the languages used in Baltic Countries, provide the languages which are used by over 80%.\n\n","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the languages used in Baltic Countries, provide the languages which are used by over 80%.\n\n\n        \n        ### Context:\n        #### Query Context:\n        Baltic Countries refers to Region = 'Baltic Countries'; languages which are used by over 80% refers to Percentage > 80;\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Language FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode WHERE T1.Region = 'Baltic Countries' AND T2.Percentage > 80"}],"db_id":"world"}655{"prompt":"Give the genre and popularity of movies whose revenue is at least 120,000,000 between 2012 and 2015.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the genre and popularity of movies whose revenue is at least 120,000,000 between 2012 and 2015.\n        \n        ### Context:\n        #### Query Context:\n        genre refers to genre_name; revenue is at least 120,000,000 refers to revenue > = 120000000; between 2012 and 2015 refers to release_date BETWEEN '2012-01-01' AND '2015-12-31'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.genre_name, T1.popularity FROM movie AS T1 INNER JOIN movie_genres AS T2 ON T1.movie_id = T2.movie_id INNER JOIN genre AS T3 ON T2.genre_id = T3.genre_id WHERE T1.revenue > 120000000 AND T1.release_date BETWEEN '2012-01-01' AND '2015-12-31'"}],"db_id":"movies_4"}656{"prompt":"Among the users who received low compliments from other users, which users joined Yelp in 2012?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the users who received low compliments from other users, which users joined Yelp in 2012?\n        \n        ### Context:\n        #### Query Context:\n        low compliments refers to number_of_compliments = 'Low'; joined Yelp in 2012 refers to user_yelping_since_year = 2012\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.user_id FROM Users AS T1 INNER JOIN Users_Compliments AS T2 ON T1.user_id = T2.user_id WHERE T1.user_yelping_since_year = 2012 AND T2.number_of_compliments = 'Low'"}],"db_id":"public_review_platform"}657{"prompt":"What is the name of the person that has the highest number of nominated award but didn't win?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the person that has the highest number of nominated award but didn't win?\n        \n        ### Context:\n        #### Query Context:\n        nominated refers to result = 'Nominee'; highest number of nominated award refers to Max(Count(person))\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT person FROM Award WHERE result = 'Nominee' GROUP BY person ORDER BY COUNT(person) DESC LIMIT 1;"}],"db_id":"simpson_episodes"}658{"prompt":"Please state the district name where incident number JB106545 took place.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please state the district name where incident number JB106545 took place.\n        \n        ### Context:\n        #### Query Context:\n        incident number JB106545 refers to case_number = 'JB106545'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.case_number FROM Crime AS T1 INNER JOIN FBI_Code AS T2 ON T1.fbi_code_no = T2.fbi_code_no WHERE T2.title = 'Criminal Sexual Assault' AND T2.crime_against = 'Persons' AND T1.arrest = 'TRUE' LIMIT 3"}],"db_id":"chicago_crime"}659{"prompt":"What is the name of the subcategory to which the gray product with the lowest safety stock level belongs?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the subcategory to which the gray product with the lowest safety stock level belongs?\n        \n        ### Context:\n        #### Query Context:\n        gray is color of product\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 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'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the proportion of English-speaking citizens in the countries that rely on the United States compared to the total number of citizens in those countries?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 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'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Percentage * T1.Population FROM country AS T1 INNER JOIN language AS T2 ON T1.Code = T2.Country INNER JOIN politics AS T3 ON T3.Country = T2.Country WHERE T3.Dependent = 'USA' AND T2.Name = 'English'"}],"db_id":"mondial_geo"}661{"prompt":"What is the average price of cars with 8 cylinders?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average price of cars with 8 cylinders?\n        \n        ### Context:\n        #### Query Context:\n        with 8 cylinders refers to cylinders = 8; average price = avg(price) where cylinders = 8\n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.price) FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID WHERE T1.cylinders = 8"}],"db_id":"cars"}662{"prompt":"List the official full names of all the legislators who have facebook, instagram, twitter and youtube accounts.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the official full names of all the legislators who have facebook, instagram, twitter and youtube accounts.\n        \n        ### Context:\n        #### Query Context:\n        have facebook, instagram, twitter and youtube accounts refers to 'facebook' is not null AND 'instagram' is not null AND 'twitter' is not null AND 'youtube' is not null;\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.official_full_name FROM current AS T1 INNER JOIN `social-media` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.facebook IS NOT NULL AND T2.instagram IS NOT NULL AND T2.twitter IS NOT NULL AND T2.youtube IS NOT NULL"}],"db_id":"legislator"}663{"prompt":"From which college was the player who won the most award in 1970.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        From which college was the player who won the most award in 1970.\n        \n        ### Context:\n        #### Query Context:\n        college refers to highSchool; won the most award refers to max(count(award)); in 1970 refers to year = 1970\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT college FROM players WHERE playerID = ( SELECT playerID FROM awards_players WHERE year = 1970 GROUP BY playerID ORDER BY COUNT(award) DESC LIMIT 1 )"}],"db_id":"professional_basketball"}664{"prompt":"Which publisher published Overwatch?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which publisher published Overwatch?\n        \n        ### Context:\n        #### Query Context:\n        which publisher refers to publisher_name; Overwatch refers to game_name = 'Overwatch';\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.publisher_name FROM game AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.game_id INNER JOIN publisher AS T3 ON T2.publisher_id = T3.id WHERE T1.game_name = 'Overwatch'"}],"db_id":"video_games"}665{"prompt":"Among the repositories with over 200 likes, how many of them have files contained by solutions with a processed time of under 636439500080712000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the repositories with over 200 likes, how many of them have files contained by solutions with a processed time of under 636439500080712000?\n        \n        ### Context:\n        #### Query Context:\n        over 200 likes refers to Stars > 200; ProcessedTime<636439500080712000;\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.RepoId) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T2.ProcessedTime < 636439500080712000 AND T1.Stars > 200"}],"db_id":"codebase_comments"}666{"prompt":"Where is the place of birth of the actor with the number nm0007064 who has not been credited for playing the role of a \"Narrator\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Where is the place of birth of the actor with the number nm0007064 who has not been credited for playing the role of a \"Narrator\"?\n        \n        ### Context:\n        #### Query Context:\n        place of birth refers to birth_place; actor with the number nm0007064 refers to person_id = 'nm007064'; has not been credited refers to credited = ''; the role of a \"Narrator\" refers to role = 'narrator'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.birth_place FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.person_id = T2.person_id WHERE T1.person_id = 'nm0007064' AND T2.role = 'Narrator' AND T2.credited = 'false'"}],"db_id":"law_episode"}667{"prompt":"Give the title of the film starred by Liza Bergman with the highest replacement cost.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the title of the film starred by Liza Bergman with the highest replacement cost.\n        \n        ### Context:\n        #### Query Context:\n        Liza Bergman' is a full name; full name refers to first_name, last_name; highest replacement cost refers to MAX(replacement_cost)\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.title FROM actor AS T1 INNER JOIN film_actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T2.film_id = T3.film_id WHERE T1.first_name = 'Liza' AND T1.last_name = 'Bergman' ORDER BY replacement_cost DESC LIMIT 1"}],"db_id":"movie_3"}668{"prompt":"What is the longest assembly item duration for bicycles?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the longest assembly item duration for bicycles?\n        \n        ### Context:\n        #### Query Context:\n        longest assembly item duration = max(subtract(EndDate,StartDate))\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 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'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT JULIANDAY(EndDate) - JULIANDAY(StartDate) FROM BillOfMaterials ORDER BY JULIANDAY(EndDate) - JULIANDAY(StartDate) DESC LIMIT 1"}],"db_id":"works_cycles"}669{"prompt":"Among all the solution of the 'zh-cn' methods, which path is most often used?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the solution of the 'zh-cn' methods, which path is most often used?\n        \n        ### Context:\n        #### Query Context:\n        solution refers to SolutionId; zh-cn refers to Lang = 'zh-cn'; path that is most often used refers to MAX(COUNT(Path));\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Path FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.Lang = 'zh-cn' GROUP BY T1.Path ORDER BY COUNT(T1.Path) DESC LIMIT 1"}],"db_id":"codebase_comments"}670{"prompt":"What is the position of the employee with the 10th highest salary? Indicate his\/her salary amount and his\/her full name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the position of the employee with the 10th highest salary? Indicate his\/her salary amount and his\/her full name.\n        \n        ### Context:\n        #### Query Context:\n        salary and Rate are synonyms; full name = FirstName+MiddleName+LastName;\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified 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'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.JobTitle, T1.Rate, T3.FirstName, T3.MiddleName, T3.LastName FROM EmployeePayHistory AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN Person AS T3 ON T2.BusinessEntityID = T3.BusinessEntityID ORDER BY T1.Rate DESC LIMIT 9, 1"}],"db_id":"works_cycles"}671{"prompt":"Please list the titles of all the episodes in which Park Dietz was credited.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the titles of all the episodes in which Park Dietz was credited.\n        \n        ### Context:\n        #### Query Context:\n        credited refers to credited = 'true'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T3.person_id = T2.person_id WHERE T2.credited = 'true' AND T3.name = 'Park Dietz'"}],"db_id":"law_episode"}672{"prompt":"Among all players drafted by the Toronto Maple Leafs in 2008, identify the player with the highest prospects for the draft.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all players drafted by the Toronto Maple Leafs in 2008, identify the player with the highest prospects for the draft.\n        \n        ### Context:\n        #### Query Context:\n        players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; highest prospects for the draft refers to MAX(CSS_rank);\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT PlayerName FROM PlayerInfo WHERE overallby = 'Toronto Maple Leafs' AND draftyear = '2008' ORDER BY CSS_rank DESC LIMIT 1"}],"db_id":"ice_hockey_draft"}673{"prompt":"Please list all release titles whose tag is jazz in 2005.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list all release titles whose tag is jazz in 2005.\n        \n        ### Context:\n        #### Query Context:\n        release titles refer to groupName; groupYear = 2005;\n        \n        #### Database Schema:\n        {'db_id': 'music_tracker', 'table_names_original': ['torrents', 'tags'], 'table_names': ['torrents', 'tags'], 'column_names_original': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_names': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_types': ['text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [7, 8], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.groupName FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T1.groupYear = 2005 AND T2.tag LIKE 'jazz'"}],"db_id":"music_tracker"}674{"prompt":"What category do Road Frames fall into?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What category do Road Frames fall into?\n        \n        ### Context:\n        #### Query Context:\n        Road Frames is a name of product subcategory\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], 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'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 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You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of project that have the most expensive funds?\n        \n        ### Context:\n        #### Query Context:\n        the most expensive funds refers to max(multiply(item_unit_price, item_quantity))\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.projectid = ( SELECT projectid FROM resources ORDER BY item_unit_price * item_quantity DESC LIMIT 1 )"}],"db_id":"donor"}676{"prompt":"List the closing time and day of week of active businesses in Scottsdale with stars greater than the 60% of average age of star rating.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the closing time and day of week of active businesses in Scottsdale with stars greater than the 60% of average age of star rating.\n        \n        ### Context:\n        #### Query Context:\n        active business refers to active = 'true';\u00a0 'Scottsdale' is the name of city; stars greater than the 60% of average age of star rating refers to stars\u00a0 > avg(stars) * 60%\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.closing_time, T3.day_of_week FROM Business AS T1 INNER JOIN Business_Hours AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T1.city LIKE 'Scottsdale' AND T1.active LIKE 'TRUE' AND T1.stars > 0.6 * ( SELECT AVG(T1.stars) FROM Business AS T1 INNER JOIN Business_Hours AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T1.city LIKE 'Scottsdale' AND T1.active LIKE 'TRUE' )"}],"db_id":"public_review_platform"}677{"prompt":"Which historical legislators are members of the National Greenbacker party? Write their first and last names.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which historical legislators are members of the National Greenbacker party? Write their first and last names.\n        \n        ### Context:\n        #### Query Context:\n        members of the National Greenbacker party refers to party = 'National Greenbacker'; first and last names refers to first_name, last_name\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.first_name, T2.last_name FROM `historical-terms` AS T1 INNER JOIN historical AS T2 ON T2.bioguide_id = T1.bioguide WHERE T1.party = 'National Greenbacker'"}],"db_id":"legislator"}678{"prompt":"On what street in Tuolumne County is Good Heavens restaurant located?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        On what street in Tuolumne County is Good Heavens restaurant located?\n        \n        ### Context:\n        #### Query Context:\n        street refers to street_name; Good Heavens restaurant refers to label = 'good heavens'\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.street_name FROM location AS T1 INNER JOIN generalinfo AS T2 ON T1.id_restaurant = T2.id_restaurant INNER JOIN geographic AS T3 ON T2.city = T3.city WHERE T2.label = 'good heavens' AND T3.county = 'tuolumne county'"}],"db_id":"restaurant"}679{"prompt":"List the names and menu page IDs of the dishes that first appeared in 1861.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the names and menu page IDs of the dishes that first appeared in 1861.\n        \n        ### Context:\n        #### Query Context:\n        first appeared in 1861 refers to first_appeared = 1861;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.name, T1.dish_id FROM MenuItem AS T1 INNER JOIN Dish AS T2 ON T2.id = T1.dish_id WHERE T2.first_appeared = 1861"}],"db_id":"menu"}680{"prompt":"List the active business ID and its stars of the businesses fall under the category of Fashion.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the active business ID and its stars of the businesses fall under the category of Fashion.\n        \n        ### Context:\n        #### Query Context:\n        active business refers to active = 'true'; category of Fashion refers to category = 'Fashion'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.business_id, T1.stars FROM Business AS T1 INNER JOIN Business_Categories ON T1.business_id = Business_Categories.business_id INNER JOIN Categories AS T3 ON Business_Categories.category_id = T3.category_id WHERE T1.active LIKE 'TRUE' AND T3.category_name LIKE 'Fashion'"}],"db_id":"public_review_platform"}681{"prompt":"List all the method name of the solution path \"graffen_NLog.Targets.Syslog\\src\\NLog.Targets.Syslog.sln\n\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the method name of the solution path \"graffen_NLog.Targets.Syslog\\src\\NLog.Targets.Syslog.sln\n\".\n        \n        ### Context:\n        #### Query Context:\n        method name refers to Name; solution path refers to Path; Path = 'graffen_NLog.Targets.Syslog\\src\\NLog.Targets.Syslog.sln';\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.Name FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T1.Path = 'graffen_NLog.Targets.SyslogsrcNLog.Targets.Syslog.sln'"}],"db_id":"codebase_comments"}682{"prompt":"How many care plans has Mrs. Norman Berge taken?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many care plans has Mrs. Norman Berge taken?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.PATIENT) FROM patients AS T1 INNER JOIN careplans AS T2 ON T1.patient = T2.PATIENT WHERE T1.prefix = 'Mrs.' AND T1.first = 'Norman' AND T1.last = 'Berge'"}],"db_id":"synthea"}683{"prompt":"Of the first 60,000 customers' responses to the incentive mailing sent by the marketing department, how many of them are considered a true response?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Of the first 60,000 customers' responses to the incentive mailing sent by the marketing department, how many of them are considered a true response?\n        \n        ### Context:\n        #### Query Context:\n        RESPONSE = 'true';\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(REFID) custmoer_number FROM Mailings1_2 WHERE RESPONSE = 'true'"}],"db_id":"software_company"}684{"prompt":"What is the character that won the award in Primetime Emmy 2009?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the character that won the award in Primetime Emmy 2009?\n        \n        ### Context:\n        #### Query Context:\n        won the award in Primetime Emmy 2009 refers to award_category = 'Primetime Emmy' and year = 2009\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.character FROM Award AS T1 INNER JOIN Character_Award AS T2 ON T1.award_id = T2.award_id WHERE T1.award_category = 'Primetime Emmy' AND T1.year = 2009 AND T1.result = 'Winner';"}],"db_id":"simpson_episodes"}685{"prompt":"Point out the greater one between the number of actual responding and not responding to mailing.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Point out the greater one between the number of actual responding and not responding to mailing.\n        \n        ### Context:\n        #### Query Context:\n        COUNT(REFID where RESPONSE = 'true')>or<COUNT(REFID where RESPONSE = 'false');\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT RESPONSE FROM Mailings1_2 GROUP BY RESPONSE ORDER BY COUNT(RESPONSE) DESC LIMIT 1"}],"db_id":"software_company"}686{"prompt":"How many games did Electronic Arts publish?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many games did Electronic Arts publish?\n        \n        ### Context:\n        #### Query Context:\n        Electronic Arts refers to publisher_name = 'Electronic Arts'\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.game_id) FROM publisher AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.publisher_id WHERE T1.publisher_name = 'Electronic Arts'"}],"db_id":"video_games"}687{"prompt":"What is the difference in population between the two nations where the tallest peak is located?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the difference in population between the two nations where the tallest peak is located?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT * FROM mountain AS T1 INNER JOIN geo_mountain AS T2 ON T1.Name = T2.Mountain INNER JOIN province AS T3 ON T3.Country = T2.Country INNER JOIN country AS T4 ON T4.Code = T3.Country WHERE T1.Name = ( SELECT Name FROM mountain ORDER BY Height DESC LIMIT 1 )"}],"db_id":"mondial_geo"}688{"prompt":"List down the customer's geographic identifier who are handlers or cleaners.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the customer's geographic identifier who are handlers or cleaners.\n        \n        ### Context:\n        #### Query Context:\n        geographic identifier refers to GEOID; OCCUPATION = 'Handlers-cleaners';\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT GEOID FROM Customers WHERE OCCUPATION = 'Handlers-cleaners'"}],"db_id":"software_company"}689{"prompt":"How much higher is James Johnson's salary from the minimum salary of his title?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much higher is James Johnson's salary from the minimum salary of his title?\n        \n        ### Context:\n        #### Query Context:\n        James Johnson is the fullname of an employee; full name = firstname, lastname; minimum salary refers to minsalary; calculation = SUBTRACT(salary, minsalary)\n        \n        #### Database Schema:\n        {'db_id': 'human_resources', 'table_names_original': ['location', 'position', 'employee'], 'table_names': ['location', 'position', 'employee'], 'column_names_original': [[-1, '*'], [0, 'locationID'], [0, 'locationcity'], [0, 'address'], [0, 'state'], [0, 'zipcode'], [0, 'officephone'], [1, 'positionID'], [1, 'positiontitle'], [1, 'educationrequired'], [1, 'minsalary'], [1, 'maxsalary'], [2, 'ssn'], [2, 'lastname'], [2, 'firstname'], [2, 'hiredate'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'positionID'], [2, 'locationID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'location city'], [0, 'address'], [0, 'state'], [0, 'zip code'], [0, 'office phone'], [1, 'position id'], [1, 'position title'], [1, 'education required'], [1, 'minimum salary'], [1, 'maximum salary'], [2, 'social security number'], [2, 'last name'], [2, 'first name'], [2, 'hire date'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'position id'], [2, 'location id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 7, 12], 'foreign_keys': [[19, 7], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL) - CAST(REPLACE(SUBSTR(T2.minsalary, 4), ',', '') AS REAL) AS diff FROM employee AS T1  INNER JOIN position AS T2  ON T1.positionID = T2.positionID WHERE T1.lastname = 'Johnson' AND T1.firstname = 'James'"}],"db_id":"human_resources"}690{"prompt":"Which negatively correlated, genetically interacting genes are non-essential? What percentage do they represent with respect to those that are essential?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which negatively correlated, genetically interacting genes are non-essential? What percentage do they represent with respect to those that are essential?\n        \n        ### Context:\n        #### Query Context:\n        If the Expression_Corr value is negative then it's negatively correlated; Percentage of Essensity = [count(negatively correlated, genetical interaction, non-essential) \/ count(negatively correlated, genetical interaction, non-essential+negatively correlated, genetical interaction, essential)] * 100%\n        \n        #### Database Schema:\n        {'db_id': 'genes', 'table_names_original': ['Classification', 'Genes', 'Interactions'], 'table_names': ['Classification', 'Genes', 'Interactions'], 'column_names_original': [[-1, '*'], [0, 'GeneID'], [0, 'Localization'], [1, 'GeneID'], [1, 'Essential'], [1, 'Class'], [1, 'Complex'], [1, 'Phenotype'], [1, 'Motif'], [1, 'Chromosome'], [1, 'Function'], [1, 'Localization'], [2, 'GeneID1'], [2, 'GeneID2'], [2, 'Type'], [2, 'Expression_Corr']], 'column_names': [[-1, '*'], [0, 'gene id'], [0, 'Localization'], [1, 'gene id'], [1, 'Essential'], [1, 'Class'], [1, 'Complex'], [1, 'Phenotype'], [1, 'Motif'], [1, 'Chromosome'], [1, 'Function'], [1, 'Localization'], [2, 'gene id 1'], [2, 'gene id 2'], [2, 'Type'], [2, 'Expression correlation score']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [12, 13]], 'foreign_keys': [[3, 1], [13, 1], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(T1.GeneID) AS REAL) * 100 \/ ( SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 ) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 AND T1.Essential = 'Non-Essential'"}],"db_id":"genes"}691{"prompt":"List down the actor IDs of film titled \"BOUND CHEAPER\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the actor IDs of film titled \"BOUND CHEAPER\".\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.actor_id FROM film AS T1 INNER JOIN film_actor AS T2 ON T1.film_id = T2.film_id WHERE T1.title = 'BOUND CHEAPER'"}],"db_id":"movie_3"}692{"prompt":"State the number of actively running Yelp businesses in \"Tolleson\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the number of actively running Yelp businesses in \"Tolleson\".\n        \n        ### Context:\n        #### Query Context:\n        actively running refers to active = 'TRUE'; Tolleson refers to city = 'Tolleson';\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(business_id) FROM Business WHERE city LIKE 'Tolleson' AND active LIKE 'TRUE'"}],"db_id":"public_review_platform"}693{"prompt":"How many strategy games are there?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many strategy games are there?\n        \n        ### Context:\n        #### Query Context:\n        strategy games refers game_name WHERE genre_name = 'Strategy';\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(CASE WHEN T1.genre_name = 'Strategy' THEN T2.id ELSE NULL END) FROM genre AS T1 INNER JOIN game AS T2 ON T1.id = T2.genre_id"}],"db_id":"video_games"}694{"prompt":"Name the goalies who played for more than two teams from Year 2000 to 2005.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the goalies who played for more than two teams from Year 2000 to 2005.\n        \n        ### Context:\n        #### Query Context:\n        goalie who played for more than 2 teams refers to COUNT(DISTINCT(T2.tmID))>2\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 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### Response:\n        "},{"role":"assistant","content":"SELECT T1.firstName, T1.lastName FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID WHERE T2.year >= 2000 AND T2.year <= 2005 GROUP BY T2.playerID HAVING COUNT(DISTINCT T2.tmID) > 2"}],"db_id":"hockey"}695{"prompt":"How many employees are under Gregory Cardenas?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many employees are under Gregory Cardenas?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.employee_id) FROM employee AS T1 WHERE T1.supervisor = ( SELECT employee_id FROM employee WHERE first_name = 'Gregory' AND last_name = 'Cardenas' )"}],"db_id":"food_inspection_2"}696{"prompt":"How many businesses accept insurance?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. 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time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.business_id) FROM Business_Attributes AS T1 INNER JOIN Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T2.attribute_name = 'Accepts Insurance' AND T1.attribute_value = 'true'"}],"db_id":"public_review_platform"}697{"prompt":"How many users rated the movie \"The Magnificent Ambersons\" gave a rating score of no more than 2? List all the URL to the rating on Mubi.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many users rated the movie \"The Magnificent Ambersons\" gave a rating score of no more than 2? List all the URL to the rating on Mubi.\n        \n        ### Context:\n        #### Query Context:\n        The Magnificent Ambersons refers to movie_title; rating score of no more than 2 refers to rating_score<2; URL to rating refers to rating_url\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.user_id), T2.rating_url FROM movies AS T1 INNER JOIN ratings AS T2 ON T1.movie_id = T2.movie_id WHERE T1.movie_title = 'The Magnificent Ambersons' AND T2.rating_score <= 2"}],"db_id":"movie_platform"}698{"prompt":"Which country has had the highest proportion of CO2 emissions from transport?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country has had the highest proportion of CO2 emissions from transport?\n        \n        ### Context:\n        #### Query Context:\n        the highest proportion of CO2 emissions from transport refers to max(value where indicatorname = 'CO2 emissions from transport (% of total fuel combustion)')\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CountryName FROM Indicators WHERE IndicatorName LIKE 'CO2 emissions FROM transport%' ORDER BY Value DESC LIMIT 1"}],"db_id":"world_development_indicators"}699{"prompt":"Please name any three root beer brands that have the highest market evaluation and acceptance.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please name any three root beer brands that have the highest market evaluation and acceptance.\n        \n        ### Context:\n        #### Query Context:\n        root beer brands refers to BrandName; highest market evaluation and acceptance refers to MAX(COUNT(StarRating = 5));\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.BrandName FROM rootbeerbrand AS T1 INNER JOIN rootbeerreview AS T2 ON T1.BrandID = T2.BrandID WHERE T2.StarRating = 5 LIMIT 3"}],"db_id":"beer_factory"}700{"prompt":"Who is the newest hired employee? Give the full name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the newest hired employee? Give the full name.\n        \n        ### Context:\n        #### Query Context:\n        newest hired refers to latest HireDate; full name = FirstName, LastName;\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT FirstName, LastName FROM Employees WHERE HireDate = ( SELECT MAX(HireDate) FROM Employees )"}],"db_id":"retail_world"}701{"prompt":"How many units of item 7 have been sold by store 7 when the snow is less than 5 inches?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many units of item 7 have been sold by store 7 when the snow is less than 5 inches?\n        \n        ### Context:\n        #### Query Context:\n        item 7 refers to item_nbr = 7; store 7 refers to store_nbr = 7; snow is less than 5 inches refers to snowfall < 5\n        \n        #### Database Schema:\n        {'db_id': 'sales_in_weather', 'table_names_original': ['sales_in_weather', 'weather', 'relation'], 'table_names': ['sales_in_weather', 'weather', 'relation'], 'column_names_original': [[-1, '*'], [0, 'date'], [0, 'store_nbr'], [0, 'item_nbr'], [0, 'units'], [1, 'station_nbr'], [1, 'date'], [1, 'tmax'], [1, 'tmin'], [1, 'tavg'], [1, 'depart'], [1, 'dewpoint'], [1, 'wetbulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'codesum'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'stnpressure'], [1, 'sealevel'], [1, 'resultspeed'], [1, 'resultdir'], [1, 'avgspeed'], [2, 'store_nbr'], [2, 'station_nbr']], 'column_names': [[-1, '*'], [0, 'date'], [0, 'store number'], [0, 'item number'], [0, 'units'], [1, 'station number'], [1, 'date'], [1, 'temperature max'], [1, 'temperature min'], [1, 'temperature average'], [1, 'departure from normal'], [1, 'dew point'], [1, 'wet bulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'code summarization'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'station pressure'], [1, 'sea level'], [1, 'resultant speed'], [1, 'resultant direction'], [1, 'average speed'], [2, 'store number'], [2, 'station number']], 'column_types': ['text', 'date', 'integer', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [5, 6], 25], 'foreign_keys': [[26, 5], [25, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(units) FROM weather AS T1 INNER JOIN relation AS T2 ON T1.station_nbr = T2.station_nbr INNER JOIN sales_in_weather AS T3 ON T2.store_nbr = T3.store_nbr WHERE T2.store_nbr = 7 AND T3.item_nbr = 7 AND T1.snowfall < 5"}],"db_id":"sales_in_weather"}702{"prompt":"Give the full name of the actor who acted the most in drama movies?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the full name of the actor who acted the most in drama movies?\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name; drama is a category of a film; acted the most in a movies refers to MAX(COUNT(film_id))\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.first_name, T.last_name FROM ( SELECT T1.first_name, T1.last_name, COUNT(T2.film_id) AS num FROM actor AS T1 INNER JOIN film_actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film_category AS T3 ON T2.film_id = T3.film_id WHERE T3.category_id = 7 GROUP BY T1.first_name, T1.last_name ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"movie_3"}703{"prompt":"Provide the ID and ISO code of Belgium.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the ID and ISO code of Belgium.\n        \n        ### Context:\n        #### Query Context:\n        ID refers to country_id; ISO code refers to country_iso_code; Belgium refers to country_name = 'Belgium'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNTry_id, COUNTry_iso_code FROM COUNTry WHERE COUNTry_name = 'Belgium'"}],"db_id":"movies_4"}704{"prompt":"List the scene numbers involving the character named Sir Toby Belch in the Twelfth Night.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the scene numbers involving the character named Sir Toby Belch in the Twelfth Night.\n        \n        ### Context:\n        #### Query Context:\n        scene numbers refers to Scene; character named Sir Toby Belch refers to CharName = 'Sir Toby Belch'; in the Twelfth Night refers to Title = 'Twelfth Night'\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.Scene FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id INNER JOIN paragraphs AS T3 ON T2.id = T3.chapter_id INNER JOIN characters AS T4 ON T3.character_id = T4.id WHERE T1.Title = 'Twelfth Night' AND T4.CharName = 'Sir Toby Belch'"}],"db_id":"shakespeare"}705{"prompt":"How old was Mrs. Laronda Bernier at the time of her most recent medical encounter?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How old was Mrs. Laronda Bernier at the time of her most recent medical encounter?\n        \n        ### Context:\n        #### Query Context:\n        how old = SUBTRACT(MIN(encounters.DATE), patients.birthdate); most recent medical encounter refers to MIN(encounters.DATE);\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.DATE - T1.birthdate AS age FROM patients AS T1 INNER JOIN encounters AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Laronda' AND T1.last = 'Bernier' ORDER BY T2.DATE DESC LIMIT 1"}],"db_id":"synthea"}706{"prompt":"Which ingredient appeared the least in recipes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which ingredient appeared the least in recipes?\n        \n        ### Context:\n        #### Query Context:\n        ingredient appeared the least in recipes refers to MIN(ingredient_id)\n        \n        #### Database Schema:\n        {'db_id': 'cookbook', 'table_names_original': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'table_names': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'column_names_original': [[-1, '*'], [0, 'ingredient_id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe_id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield_unit'], [1, 'prep_min'], [1, 'cook_min'], [1, 'stnd_min'], [1, 'source'], [1, 'intro'], [1, 'directions'], [2, 'recipe_id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total_fat'], [2, 'sat_fat'], [2, 'cholestrl'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin_c'], [2, 'vitamin_a'], [2, 'fiber'], [2, 'pcnt_cal_carb'], [2, 'pcnt_cal_fat'], [2, 'pcnt_cal_prot'], [2, 'calories'], [3, 'quantity_id'], [3, 'recipe_id'], [3, 'ingredient_id'], [3, 'max_qty'], [3, 'min_qty'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_names': [[-1, '*'], [0, 'ingredient id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield unit'], [1, 'preparation minute'], [1, 'cooked minute'], [1, 'stand minute'], [1, 'source'], [1, 'introduction'], [1, 'directions'], [2, 'recipe id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total fat'], [2, 'saturated fat'], [2, 'cholesterol'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin c'], [2, 'vitamin a'], [2, 'fiber'], [2, 'percentage calculation carbo'], [2, 'percentage calculation fat'], [2, 'percentage calculation protein'], [2, 'calories'], [3, 'quantity id'], [3, 'recipe id'], [3, 'ingredient id'], [3, 'maximum quantity'], [3, 'minimum quantity'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text'], 'primary_keys': [1, 5, 16, 32], 'foreign_keys': [[16, 5], [33, 16], [34, 1], [33, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.name FROM Ingredient AS T1 INNER JOIN Quantity AS T2 ON T1.ingredient_id = T2.ingredient_id GROUP BY T2.ingredient_id ORDER BY COUNT(T2.ingredient_id) ASC LIMIT 1"}],"db_id":"cookbook"}707{"prompt":"State the address of Amy Klobuchar at the term of 4th of January 2001.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the address of Amy Klobuchar at the term of 4th of January 2001.\n        \n        ### Context:\n        #### Query Context:\n        at the term of 4th of January 2001 refers to start = '2001-04-01';\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.address FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.first_name = 'Amy' AND T1.last_name = 'Klobuchar' AND T2.start = '2001-04-01'"}],"db_id":"legislator"}708{"prompt":"Locate all events on devices of women under 30 years old.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Locate all events on devices of women under 30 years old.\n        \n        ### Context:\n        #### Query Context:\n        locate = longitude, latitude; women refers to gender = 'F'; under 30 years old refers to age < 30;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.device_id FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE T1.gender = 'F' AND T1.age < 30"}],"db_id":"talkingdata"}709{"prompt":"Please provide the ID of the user with the most followers on the list.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please provide the ID of the user with the most followers on the list.\n        \n        ### Context:\n        #### Query Context:\n        most followers refers to Max(list_followers);\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT user_id FROM lists ORDER BY list_followers DESC LIMIT 1"}],"db_id":"movie_platform"}710{"prompt":"Among the movies starring Tom Cruise, which one of them has the best quality?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the movies starring Tom Cruise, which one of them has the best quality?\n        \n        ### Context:\n        #### Query Context:\n        starring Tom Cruise refers to name = 'Tom Cruise'; best quality refers to max(rating)\n        \n        #### Database Schema:\n        {'db_id': 'movie', 'table_names_original': ['actor', 'movie', 'characters'], 'table_names': ['actor', 'movie', 'characters'], 'column_names_original': [[-1, '*'], [0, 'ActorID'], [0, 'Name'], [0, 'Date of Birth'], [0, 'Birth City'], [0, 'Birth Country'], [0, 'Height (Inches)'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'MovieID'], [1, 'Title'], [1, 'MPAA Rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'Rating Count'], [1, 'Summary'], [2, 'MovieID'], [2, 'ActorID'], [2, 'Character Name'], [2, 'creditOrder'], [2, 'pay'], [2, 'screentime']], 'column_names': [[-1, '*'], [0, 'actor id'], [0, 'Name'], [0, 'date of birth'], [0, 'birth city'], [0, 'birth country'], [0, 'height inches'], [0, 'Biography'], [0, 'Gender'], [0, 'Ethnicity'], [0, 'NetWorth'], [1, 'movie id'], [1, 'Title'], [1, 'motion picture association of america rating'], [1, 'Budget'], [1, 'Gross'], [1, 'Release Date'], [1, 'Genre'], [1, 'Runtime'], [1, 'Rating'], [1, 'rating count'], [1, 'Summary'], [2, 'movie id'], [2, 'actor id'], [2, 'character name'], [2, 'credit order'], [2, 'pay'], [2, 'screentime']], 'column_types': ['text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text'], 'primary_keys': [1, 11, [22, 23]], 'foreign_keys': [[22, 11], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Title FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID INNER JOIN actor AS T3 ON T3.ActorID = T2.ActorID WHERE T3.Name = 'Tom Cruise' ORDER BY T1.Rating DESC LIMIT 1"}],"db_id":"movie"}711{"prompt":"Among the English comedy movies produced in the UK, how many movies with a running time of 3 was rated the highest by users between the age 45-50? Indicate the movie names.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the English comedy movies produced in the UK, how many movies with a running time of 3 was rated the highest by users between the age 45-50? Indicate the movie names.\n        \n        ### Context:\n        #### Query Context:\n        UK is a country\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.movieid FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid INNER JOIN u2base AS T3 ON T1.movieid = T3.movieid INNER JOIN users AS T4 ON T3.userid = T4.userid WHERE T1.country = 'UK' AND T2.genre = 'Comedy' AND T1.runningtime = 3 AND T3.rating = 5 AND T4.age BETWEEN 45 AND 50 AND T1.isEnglish = 'T'"}],"db_id":"movielens"}712{"prompt":"Among the male customers in Sacramento, what percentage bought Dominion root beer in 2013?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the male customers in Sacramento, what percentage bought Dominion root beer in 2013?\n        \n        ### Context:\n        #### Query Context:\n        male customers refers to Gender = 'M'; Sacramento refers to City = 'Sacramento'; percentage = MULTIPLY(DIVIDE(SUM(BrandID WHERE BrandName = 'Dominion'), COUNT(BrandID) WHERE City = 'Sacramento'), 1.0); Dominion refers to BrandName = 'Dominion'; in 2013 refers to TransactionDate > = 2013-01-01 AND TransactionDate < 2014-01-01;\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T4.BrandName = 'Dominion' THEN T1.CustomerID ELSE NULL END) AS REAL) * 100 \/ COUNT(T1.CustomerID) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN rootbeer AS T3 ON T2.RootBeerID = T3.RootBeerID INNER JOIN rootbeerbrand AS T4 ON T3.BrandID = T4.BrandID WHERE T1.City = 'Sacramento' AND T1.Gender = 'M' AND T2.TransactionDate LIKE '2014%'"}],"db_id":"beer_factory"}713{"prompt":"How many crime against property are there?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many crime against property are there?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) AS cnt FROM FBI_Code WHERE crime_against = 'Property'"}],"db_id":"chicago_crime"}714{"prompt":"How many Away Victories happened on 2016\/3\/27 in the LaLiga 2 division?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Away Victories happened on 2016\/3\/27 in the LaLiga 2 division?\n        \n        ### Context:\n        #### Query Context:\n        Away victories refer to FTR = 'A'; LaLiga 2 is the name of division; Date = '2016-03-27';\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.FTR) FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.name = 'LaLiga 2' AND T1.Date = '2016-03-27' AND T1.FTR = 'A'"}],"db_id":"european_football_1"}715{"prompt":"Which language is the rarest among all the books?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which language is the rarest among all the books?\n        \n        ### Context:\n        #### Query Context:\n        language written in refers to language_name; rarest refers to Min(Count(book_id))\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.language_name FROM book AS T1 INNER JOIN book_language AS T2 ON T1.language_id = T2.language_id GROUP BY T2.language_name ORDER BY COUNT(T2.language_name) ASC LIMIT 1"}],"db_id":"books"}716{"prompt":"Among the flights with air carrier described as Asap Air Inc.: ASP, what is the tail number of the flight with the longest departure delay?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the flights with air carrier described as Asap Air Inc.: ASP, what is the tail number of the flight with the longest departure delay?\n        \n        ### Context:\n        #### Query Context:\n        Asap Air Inc.: ASP refers to Description = 'Asap Air Inc.: ASP'; tail number refers to TAIL_NUM; longest departure delay refers to MAX(DEP_DELAY);\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Asap Air Inc.: ASP' ORDER BY T2.DEP_DELAY DESC LIMIT 1"}],"db_id":"airline"}717{"prompt":"List the languages used in the USA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the languages used in the USA.\n        \n        ### Context:\n        #### Query Context:\n        USA refers to CountryCode = 'USA';\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Language FROM CountryLanguage WHERE CountryCode = 'USA'"}],"db_id":"world"}718{"prompt":"How many dishes have appeared on the menu in less than 5 years?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many dishes have appeared on the menu in less than 5 years?\n        \n        ### Context:\n        #### Query Context:\n        appeared on the menu in less than 5 years = SUBTRACT(last_appeared, first_appeared) < 5;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM Dish AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.dish_id WHERE T1.last_appeared - T1.first_appeared < 5"}],"db_id":"menu"}719{"prompt":"Determine Disney's total box office gross between 2010 and 2016.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Determine Disney's total box office gross between 2010 and 2016.\n        \n        ### Context:\n        #### Query Context:\n        between 2010 and 2016 refers to Year between 2010 and 2016;\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(Total) FROM revenue WHERE `Year` BETWEEN 2010 AND 2016"}],"db_id":"disney"}720{"prompt":"What are the descriptions of the short chapters?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the descriptions of the short chapters?\n        \n        ### Context:\n        #### Query Context:\n        short chapters refers to ParagraphNum < 150\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.Description FROM paragraphs AS T1 INNER JOIN chapters AS T2 ON T1.chapter_id = T2.id WHERE T1.ParagraphNum < 150"}],"db_id":"shakespeare"}721{"prompt":"Among all the orders, which products sold for the most amount?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the orders, which products sold for the most amount?\n        \n        ### Context:\n        #### Query Context:\n        the most amount refers to max(sum(Quantity))\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT ProductID FROM `Order Details` GROUP BY ProductID ORDER BY SUM(Quantity) DESC LIMIT 1"}],"db_id":"retail_world"}722{"prompt":"What are the cities for country called \"\u00c2\u00b4Uman\" in local name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the cities for country called \"\u00c2\u00b4Uman\" in local name.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T2.LocalName = '\u00c2\u00b4Uman'"}],"db_id":"world"}723{"prompt":"How many of the items are instructed to be delivered in person?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the items are instructed to be delivered in person?\n        \n        ### Context:\n        #### Query Context:\n        instructed to be delivered in person refers to l_shipinstruct = 'DELIVER IN PERSON'\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(l_linenumber) FROM lineitem WHERE l_shipinstruct = 'DELIVER IN PERSON'"}],"db_id":"retails"}724{"prompt":"What is the location id for Debur and Polish?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the location id for Debur and Polish?\n        \n        ### Context:\n        #### Query Context:\n        Debur and Polish is name of manufacturing location\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 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'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT LocationID FROM Location WHERE Name = 'Debur and Polish'"}],"db_id":"works_cycles"}725{"prompt":"List down the page numbers for menu with dishes on the right upper corner.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the page numbers for menu with dishes on the right upper corner.\n        \n        ### Context:\n        #### Query Context:\n        on the right upper corner refers to xpos > 0.75 AND ypos < 0.25;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.page_number FROM Menu AS T1 INNER JOIN MenuPage AS T2 ON T1.id = T2.menu_id INNER JOIN MenuItem AS T3 ON T2.id = T3.menu_page_id WHERE T3.xpos > 0.75 AND T3.ypos < 0.25"}],"db_id":"menu"}726{"prompt":"When John Aalberg took part in the 1994 Winter Olympic Game, how old was he?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        When John Aalberg took part in the 1994 Winter Olympic Game, how old was he?\n        \n        ### Context:\n        #### Query Context:\n        how old was he refers to age; 1994 Winter refers to games_name = '1994 Winter';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.age FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T3.full_name = 'John Aalberg' AND T1.games_name = '1994 Winter'"}],"db_id":"olympics"}727{"prompt":"List the ranking criteria under the Shanghai Ranking system.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the ranking criteria under the Shanghai Ranking system.\n        \n        ### Context:\n        #### Query Context:\n        Shanghai Ranking system refers to system_name = 'Shanghai Ranking'; ranking criteria refers to criteria_name\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.criteria_name FROM ranking_system AS T1 INNER JOIN ranking_criteria AS T2 ON T1.id = T2.ranking_system_id WHERE T1.system_name = 'Shanghai Ranking'"}],"db_id":"university"}728{"prompt":"How many businesses were founded after 1960 in a nation that wasn't independent?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many businesses were founded after 1960 in a nation that wasn't independent?\n        \n        ### Context:\n        #### Query Context:\n        Established means founded; Country means nation; Organization means businesses\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 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'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T3.Name) FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country INNER JOIN organization AS T3 ON T3.Country = T2.Country WHERE T2.Independence = NULL AND STRFTIME('%Y', T3.Established) > '1960'"}],"db_id":"mondial_geo"}729{"prompt":"Find the number of customers handled by each of the sales people.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find the number of customers handled by each of the sales people.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(CustomerID) FROM Sales GROUP BY SalesPersonID"}],"db_id":"sales"}730{"prompt":"Calculate the average price of cars from Europe.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average price of cars from Europe.\n        \n        ### Context:\n        #### Query Context:\n        from Europe refers to country = 'Europe'; average price = avg(price) where country = 'Europe'\n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.price) FROM price AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID INNER JOIN country AS T3 ON T3.origin = T2.country WHERE T3.country = 'Europe'"}],"db_id":"cars"}731{"prompt":"How many cities are there in Monterey?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cities are there in Monterey?\n        \n        ### Context:\n        #### Query Context:\n        Monterey refers to region = 'monterey'\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT city) FROM geographic WHERE region = 'monterey'"}],"db_id":"restaurant"}732{"prompt":"What is the name of the state that the client with the email \"skylar.ramirez@gmail.com\" lives in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the state that the client with the email \"skylar.ramirez@gmail.com\" lives in?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.state FROM state AS T1 INNER JOIN district AS T2 ON T1.StateCode = T2.state_abbrev INNER JOIN client AS T3 ON T2.district_id = T3.district_id WHERE T3.email = 'skylar.ramirez@gmail.com'"}],"db_id":"retail_complains"}733{"prompt":"Calculate the total rental rate for animation film titles.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the total rental rate for animation film titles.\n        \n        ### Context:\n        #### Query Context:\n        animation film refers to category.name = 'Animation'; total rental rate = sum(rental_rate)\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T1.rental_rate) FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T2.category_id = T3.category_id WHERE T3.`name` = 'Animation'"}],"db_id":"movie_3"}734{"prompt":"What is the weekly average number of fraud incidents that were reported in January 2018? Provide the description of the location where the majority of fraud incidents occurred in the said month.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the weekly average number of fraud incidents that were reported in January 2018? Provide the description of the location where the majority of fraud incidents occurred in the said month.\n        \n        ### Context:\n        #### Query Context:\n        fraud incident refers to title = 'Fraud'; reported in January 2018 refers to Substr(date, 1, 1) = '1' AND Substr(date, 5, 4) = '2018'; description of location refers to location_description; weekly average refers to Divide (Count(report_no), 4); majority of incidents occurred refers to Max(Count(location_description))\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(T1.fbi_code_no) AS REAL) \/ 4 FROM FBI_Code AS T1 INNER JOIN Crime AS T2 ON T1.fbi_code_no = T2.fbi_code_no WHERE SUBSTR(T2.date, 1, 1) = '1' AND SUBSTR(T2.date, 5, 4) = '2018'"}],"db_id":"chicago_crime"}735{"prompt":"What is the projected sales quota amount in 2013 and sales YTD amount for sales person with business entity ID 275?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the projected sales quota amount in 2013 and sales YTD amount for sales person with business entity ID 275?\n        \n        ### Context:\n        #### Query Context:\n        projected sales quota refers to SalesQuota; projected sales quota in 2013 refers to year(QuotaDate) = 2013; projected sales quota for 2013 = SUM(SalesQuota where year(QuotaDate) = 2013);\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country 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'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'blob', 'text', 'blob', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'text', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'text', 'datetime', 'integer', 'datetime', 'real', 'text', 'datetime', 'integer', 'integer', 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'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T1.SalesQuota) FROM SalesPerson AS T1 INNER JOIN SalesPersonQuotaHistory AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.BusinessEntityID = 275 AND STRFTIME('%Y', QuotaDate) = '2013'"}],"db_id":"works_cycles"}736{"prompt":"How many students graduated from Central Alabama Community College in 2011 in total?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students graduated from Central Alabama Community College in 2011 in total?\n        \n        ### Context:\n        #### Query Context:\n        Central Alabama Community College refers to chronname = 'Central Alabama Community College'; in 2011 refers to year = 2011;\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.grad_cohort FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T1.unitid = T2.unitid WHERE T1.chronname = 'Central Alabama Community College' AND T2.year = 2011"}],"db_id":"college_completion"}737{"prompt":"List the cities where the product 'Mishi Kobe Niku' were shipped to.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the cities where the product 'Mishi Kobe Niku' were shipped to.\n        \n        ### Context:\n        #### Query Context:\n        cities refers to ShipCity; 'Mishi Kobe Niku' is a ProductName\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ShipCity FROM Orders AS T1 INNER JOIN `Order Details` AS T2 ON T1.OrderID = T2.OrderID INNER JOIN Products AS T3 ON T2.ProductID = T3.ProductID WHERE T3.ProductName = 'Mishi Kobe Niku'"}],"db_id":"retail_world"}738{"prompt":"Among the businesses within the postal code 94117, what is total number of businesses with a high risk category?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the businesses within the postal code 94117, what is total number of businesses with a high risk category?\n        \n        ### Context:\n        #### Query Context:\n        businesses with a high risk category refer to business_id where risk_category = 'High Risk';\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection', 'table_names_original': ['businesses', 'inspections', 'violations'], 'table_names': ['businesses', 'inspections', 'violations'], 'column_names_original': [[-1, '*'], [0, 'business_id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal_code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone_number'], [0, 'tax_code'], [0, 'business_certificate'], [0, 'application_date'], [0, 'owner_name'], [0, 'owner_address'], [0, 'owner_city'], [0, 'owner_state'], [0, 'owner_zip'], [1, 'business_id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business_id'], [2, 'date'], [2, 'violation_type_id'], [2, 'risk_category'], [2, 'description']], 'column_names': [[-1, '*'], [0, 'business id'], [0, 'name'], [0, 'address'], [0, 'city'], [0, 'postal code'], [0, 'latitude'], [0, 'longitude'], [0, 'phone number'], [0, 'tax code'], [0, 'business certificate'], [0, 'application date'], [0, 'owner name'], [0, 'owner address'], [0, 'owner city'], [0, 'owner state'], [0, 'owner zip'], [1, 'business id'], [1, 'score'], [1, 'date'], [1, 'type'], [2, 'business id'], [2, 'date'], [2, 'violation type id'], [2, 'risk category'], [2, 'description']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'text', 'integer', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'date', 'text', 'integer', 'date', 'text', 'text', 'text'], 'primary_keys': [1], 'foreign_keys': [[17, 1], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.business_id) FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T2.postal_code = 94117 AND T1.risk_category = 'High Risk'"}],"db_id":"food_inspection"}739{"prompt":"What is the percentage of people who gave the \"True North\" episode a 1-star rating?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage of people who gave the \"True North\" episode a 1-star rating?\n        \n        ### Context:\n        #### Query Context:\n        the \"True North\" episode refers to title = 'True North'; 1-star refers to stars = 1; percentage = divide(count(episode_id where stars = 1), count(episode_id)) * 100% where title = 'True North'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.stars = 1 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.episode_id) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'True North' AND T1.episode_id = 'tt0629477'"}],"db_id":"law_episode"}740{"prompt":"State the podcast title, content review and rating for all reviews with titled 'Love it!'","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the podcast title, content review and rating for all reviews with titled 'Love it!'\n        \n        ### Context:\n        #### Query Context:\n        \"Love it!\" is the title of review; content reviewed refers to content\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.title, T2.content, T2.rating FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.title = 'Love it!'"}],"db_id":"music_platform_2"}741{"prompt":"In which country is the city of Grozny? Give the full name of the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which country is the city of Grozny? Give the full name of the country.\n        \n        ### Context:\n        #### Query Context:\n        Grozny is a province\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM country AS T1 INNER JOIN province AS T2 ON T1.Code = T2.Country INNER JOIN city AS T3 ON T3.Province = T2.Name WHERE T3.Name = 'Grozny'"}],"db_id":"mondial_geo"}742{"prompt":"How many territory fall into region 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many territory fall into region 1?\n        \n        ### Context:\n        #### Query Context:\n        region 1 refers to RegionID = 1\n\n\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(TerritoryID) FROM Territories WHERE RegionID = 1"}],"db_id":"retail_world"}743{"prompt":"What is the contact name and phone number of the customer who has made the most total payment on the order to date?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the contact name and phone number of the customer who has made the most total payment on the order to date?\n        \n        ### Context:\n        #### Query Context:\n        most total payment = Max(Multiply(Quantity, UnitPrice, Subtract(1, Discount)))\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ContactName, T1.Phone FROM Customers AS T1 INNER JOIN Orders AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN `Order Details` AS T3 ON T2.OrderID = T3.OrderID GROUP BY T2.OrderID, T1.ContactName, T1.Phone ORDER BY SUM(T3.UnitPrice * T3.Quantity * (1 - T3.Discount)) DESC LIMIT 1"}],"db_id":"retail_world"}744{"prompt":"What are the titles of the top 5 Catalan language Wikipedia page with the least number of different words? Indicate each title's word id that has appeared the most in the said pages.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the titles of the top 5 Catalan language Wikipedia page with the least number of different words? Indicate each title's word id that has appeared the most in the said pages.\n        \n        ### Context:\n        #### Query Context:\n        least number of different words refers to min(words); word id refers to wid; appeared the most refers to max(occurrences)\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM pages AS T1 INNER JOIN pages_words AS T2 ON T1.pid = T2.pid ORDER BY T1.words LIMIT 5"}],"db_id":"language_corpus"}745{"prompt":"For all ratings which are rated in year 2020, name the movies which has the rating scored 4 and above.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For all ratings which are rated in year 2020, name the movies which has the rating scored 4 and above.\n        \n        ### Context:\n        #### Query Context:\n        ratings in year 2020 refers to rating_timestamp_utc like '%2020%'; rating_score > = 4;\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.movie_title FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE CAST(SUBSTR(T1.rating_timestamp_utc, 1, 4) AS INTEGER) = 2020 AND CAST(SUBSTR(T1.rating_timestamp_utc, 6, 2) AS INTEGER) > 4"}],"db_id":"movie_platform"}746{"prompt":"List all players' given name who are good at both left and right hand and playing the forward position.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all players' given name who are good at both left and right hand and playing the forward position.\n        \n        ### Context:\n        #### Query Context:\n        good at both left and right hand refers to shootCatch IS NULL;\u00a0 playing the forward position refers to pos = 'F'\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 'T'], [9, 'SHO'], [9, 'GA'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'tmID'], [10, 'W'], [10, 'L'], [10, 'SA'], [10, 'GA'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'tmID'], [11, 'lgID'], [11, 'pos'], [11, 'GP'], [11, 'G'], [11, 'A'], [11, 'Pts'], [11, 'PIM'], [11, '+\/-'], [11, 'PPG'], [11, 'PPA'], [11, 'SHG'], [11, 'SHA'], [11, 'GWG'], [11, 'GTG'], [11, 'SOG'], [11, 'PostGP'], [11, 'PostG'], [11, 'PostA'], [11, 'PostPts'], [11, 'PostPIM'], [11, 'Post+\/-'], [11, 'PostPPG'], [11, 'PostPPA'], [11, 'PostSHG'], [11, 'PostSHA'], [11, 'PostGWG'], [11, 'PostSOG'], [12, 'playerID'], [12, 'year'], [12, 'tmID'], [12, 'lgID'], [12, 'pos'], [12, 'GP'], [12, 'G'], [12, 'A'], [12, 'Pts'], [12, 'PIM'], [13, 'playerID'], [13, 'year'], [13, 'stint'], [13, 'tmID'], [13, 'S'], [13, 'G'], [13, 'GDG'], [14, 'playerID'], [14, 'year'], [14, 'PPA'], [14, 'SHA'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'tmIDWinner'], [15, 'lgIDWinner'], [15, 'tmIDLoser'], [15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], [12, 'league ID'], [12, 'position'], [12, 'Games played'], [12, 'Goals'], [12, 'assists'], [12, 'points'], [12, 'Penalty minutes'], [13, 'player id'], [13, 'year'], [13, 'stint'], [13, 'team ID'], [13, 'shots'], [13, 'goals'], [13, 'game deciding goals'], [14, 'player id'], [14, 'year'], [14, 'Power play assists'], [14, 'Shorthanded assists'], [15, 'year'], [15, 'round'], [15, 'series'], [15, 'Team ID of winner'], [15, 'League ID of winner'], [15, 'Team ID of loser'], [15, 'league id of loser'], [15, 'wins'], [15, 'loses'], [15, 'ties'], [15, 'goals for winner'], [15, 'goals for loser'], [15, 'note'], [16, 'year'], [16, 'league ID'], [16, 'team ID'], [16, 'home wins'], [16, 'home loses'], [16, 'home ties'], [16, 'Home overtime losses'], [16, 'Road wins'], [16, 'Road loses'], [16, 'Road ties'], [16, 'road overtime loses'], [16, 'September wins'], [16, 'September loses'], [16, 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'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 'Games'], [19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 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'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT nameGiven FROM Master WHERE shootCatch IS NULL AND pos = 'F'"}],"db_id":"hockey"}747{"prompt":"List all the unspecified gender characters.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the unspecified gender characters.\n        \n        ### Context:\n        #### Query Context:\n        characters refers to character_name; gender = 'Unspecified'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.character_name FROM movie_cast AS T1 INNER JOIN gender AS T2 ON T1.gender_id = T2.gender_id WHERE T2.gender = 'Unspecified'"}],"db_id":"movies_4"}748{"prompt":"What is the name of the supplier that supplies the most products to the company?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the supplier that supplies the most products to the company?\n        \n        ### Context:\n        #### Query Context:\n        name of the supplier refers to SupplierID; the most product refers to max(count(ProductID))\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.SupplierID FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID GROUP BY T1.SupplierID ORDER BY COUNT(*) DESC LIMIT 1"}],"db_id":"retail_world"}749{"prompt":"How many different football events are there?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many different football events are there?\n        \n        ### Context:\n        #### Query Context:\n        events refer to event_name; football refers to sport_name = 'Football';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.event_name) FROM sport AS T1 INNER JOIN event AS T2 ON T1.id = T2.sport_id WHERE T1.sport_name = 'Football'"}],"db_id":"olympics"}750{"prompt":"What is the average duration time of the films starring PENELOPE GUINESS?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average duration time of the films starring PENELOPE GUINESS?\n        \n        ### Context:\n        #### Query Context:\n        average duration time = AVG(length)\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T3.length) FROM actor AS T1 INNER JOIN film_actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T2.film_id = T3.film_id WHERE T1.first_name = 'PENELOPE' AND T1.last_name = 'GUINESS'"}],"db_id":"movie_3"}751{"prompt":"What is the topic of the series when the Total reserves minus gold (current US$) indicator of Haiti hit the value of 3,000,000 in 1961? Please include its series code and license type.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the topic of the series when the Total reserves minus gold (current US$) indicator of Haiti hit the value of 3,000,000 in 1961? Please include its series code and license type.\n        \n        ### Context:\n        #### Query Context:\n        Total reserves minus gold (current US$) is the IndicatorName; Haiti is the CountryName; Year = 1961\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Topic, T2.Seriescode, T2.LicenseType FROM Indicators AS T1 INNER JOIN Series AS T2 ON T1.IndicatorName = T2.IndicatorName WHERE T1.Year = 1961 AND T1.CountryName = 'Haiti' AND T1.IndicatorName = 'Total reserves minus gold (current US$)' AND T1.Value = 3000000"}],"db_id":"world_development_indicators"}752{"prompt":"What are the inspection results for Xando Coffee & Bar \/ Cosi Sandwich Bar?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the inspection results for Xando Coffee & Bar \/ Cosi Sandwich Bar?\n        \n        ### Context:\n        #### Query Context:\n        Xando Coffee & Bar \/ Cosi Sandwich Bar refers to dba_name = 'XANDO COFFEE & BAR \/ COSI SANDWICH BAR'\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.results FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T1.dba_name = 'XANDO COFFEE & BAR \/ COSI SANDWICH BAR'"}],"db_id":"food_inspection_2"}753{"prompt":"Please list the mountains in the country with the lowest inflation rate.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the mountains in the country with the lowest inflation rate.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Mountain FROM geo_mountain WHERE Country = ( SELECT Country FROM economy ORDER BY Inflation ASC LIMIT 1 )"}],"db_id":"mondial_geo"}754{"prompt":"Name the city of the station that trip ID 585842 borrowed a bike and indicate when that station was first installed.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the city of the station that trip ID 585842 borrowed a bike and indicate when that station was first installed.\n        \n        ### Context:\n        #### Query Context:\n        when the station was first installed refers to installation_date;\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.city, T2.installation_date FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T1.id = 585842"}],"db_id":"bike_share_1"}755{"prompt":"Who played Captain Jack Sparrow in all of the Pirates of the Caribbean movies?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who played Captain Jack Sparrow in all of the Pirates of the Caribbean movies?\n        \n        ### Context:\n        #### Query Context:\n        Captain Jack Sparrow refers to character_name = 'Captain Jack Sparrow'; Pirates of the Caribbean movies refers to title LIKE 'Pirates of the Carribbean%'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.person_name FROM movie AS T1 INNER JOIN movie_cast AS T2 ON T1.movie_id = T2.movie_id INNER JOIN person AS T3 ON T2.person_id = T3.person_id WHERE T2.character_name = 'Captain Jack Sparrow' AND T1.title LIKE 'Pirates of the Caribbean%'"}],"db_id":"movies_4"}756{"prompt":"In which nation is Harvard University located?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which nation is Harvard University located?\n        \n        ### Context:\n        #### Query Context:\n        Harvard University refers to university_name = 'Harvard University'; nation refers to country_name\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.country_name FROM university AS T1 INNER JOIN country AS T2 ON T1.country_id = T2.id WHERE T1.university_name = 'Harvard University'"}],"db_id":"university"}757{"prompt":"What are the sales reasons for order 43718?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the sales reasons for order 43718?\n        \n        ### Context:\n        #### Query Context:\n        order refers to SalesOrderID\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 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'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], 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'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name FROM SalesOrderHeaderSalesReason AS T1 INNER JOIN SalesReason AS T2 ON T1.SalesReasonID = T2.SalesReasonID WHERE T1.SalesOrderID = 43718"}],"db_id":"works_cycles"}758{"prompt":"Count the total number of tweet IDs in `en`.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Count the total number of tweet IDs in `en`.\n        \n        ### Context:\n        #### Query Context:\n        \"en\" is the language and refers to Lang = 'en'\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT TweetID) FROM twitter WHERE Lang = 'en'"}],"db_id":"social_media"}759{"prompt":"How many dishes appear in the right upper corner of the menu page?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many dishes appear in the right upper corner of the menu page?\n        \n        ### Context:\n        #### Query Context:\n        appear in the right upper corner of the menu page refers to xpos > 0.75 and ypos < 0.25;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM MenuItem AS T1 INNER JOIN Dish AS T2 ON T1.dish_id = T2.id WHERE T1.xpos > 0.75 AND T1.ypos < 0.25"}],"db_id":"menu"}760{"prompt":"What year did the Simpsons receive its first ever award for Favorite Animated Comedy in People's Choice Award?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What year did the Simpsons receive its first ever award for Favorite Animated Comedy in People's Choice Award?\n        \n        ### Context:\n        #### Query Context:\n        \"Favorite Animated Comedy\" is the award; 'People's Choice Award' is the award_category; received award refers to result = 'Winner'; first ever award refers to Min(year)\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT year FROM Award WHERE result = 'Winner' AND award = 'Favorite Animated Comedy' ORDER BY year DESC LIMIT 1;"}],"db_id":"simpson_episodes"}761{"prompt":"What are the names of games that were released in 2007?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the names of games that were released in 2007?\n        \n        ### Context:\n        #### Query Context:\n        names of games refers to game_name; released in 2007 refers to release_year = 2007;\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.game_name FROM game_platform AS T1 INNER JOIN game_publisher AS T2 ON T1.game_publisher_id = T2.id INNER JOIN game AS T3 ON T2.game_id = T3.id WHERE T1.release_year = 2007"}],"db_id":"video_games"}762{"prompt":"What are the genres of Sky Captain and the World of Tomorrow?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the genres of Sky Captain and the World of Tomorrow?\n        \n        ### Context:\n        #### Query Context:\n        genres refers to genre_name; Sky Captain and the World of Tomorrow refers to title = 'Sky Captain and the World of Tomorrow'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.genre_name FROM movie AS T1 INNER JOIN movie_genres AS T2 ON T1.movie_id = T2.movie_id INNER JOIN genre AS T3 ON T2.genre_id = T3.genre_id WHERE T1.title = 'Sky Captain and the World of Tomorrow'"}],"db_id":"movies_4"}763{"prompt":"When is the modified date of the phone number \"1500 555-0143\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        When is the modified date of the phone number \"1500 555-0143\"?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 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369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT ModifiedDate FROM PersonPhone WHERE PhoneNumber = '1 (11) 500 555-0143'"}],"db_id":"works_cycles"}764{"prompt":"For the movie \"Land of the Dead\", who is its director?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For the movie \"Land of the Dead\", who is its director?\n        \n        ### Context:\n        #### Query Context:\n        \"Land of the Dead\" refers to title = 'Land of the Dead'; director refers to person_name where job = 'Director'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.person_name FROM movie AS T1 INNER JOIN movie_crew AS T2 ON T1.movie_id = T2.movie_id INNER JOIN person AS T3 ON T2.person_id = T3.person_id WHERE T1.title = 'Land of the Dead' AND T2.job = 'Director'"}],"db_id":"movies_4"}765{"prompt":"Please calculate the average of Arms imports (SIPRI trend indicator values) of the European & Central Asian countries.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please calculate the average of Arms imports (SIPRI trend indicator values) of the European & Central Asian countries.\n        \n        ### Context:\n        #### Query Context:\n        European & Central Asian is the name of the region; IndicatorName = 'Arms imports (SIPRI trend indicator values)'; countries refer to CountryCode; DIVIDE(Sum(Value), Count(CountryCode));\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T2.Value) AS REAL) \/ COUNT(T1.CountryCode) FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.Region = 'Europe & Central Asia' AND T2.IndicatorName = 'Arms imports (SIPRI trend indicator values)'"}],"db_id":"world_development_indicators"}766{"prompt":"How many \"Mountain-500 Black 42\" were sold in total?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many \"Mountain-500 Black 42\" were sold in total?\n        \n        ### Context:\n        #### Query Context:\n        Mountain-500 Black 42' is name of product; sold in total = SUM(Quantity);\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.Quantity) FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Name = 'Mountain-500 Black, 42'"}],"db_id":"sales"}767{"prompt":"Who is the staff manager in store id 2?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the staff manager in store id 2?\n        \n        ### Context:\n        #### Query Context:\n        staff manager refers to manager_staff_id\n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT manager_staff_id FROM store WHERE store_id = 2"}],"db_id":"movie_3"}768{"prompt":"How many events did the device ID \"3915082290673130000\" join?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many events did the device ID \"3915082290673130000\" join?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(event_id) FROM events WHERE device_id = 3915082290673130000"}],"db_id":"talkingdata"}769{"prompt":"List the days in 2013 when rain and fog occurred together and find the id of bikes borrowed on these days.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the days in 2013 when rain and fog occurred together and find the id of bikes borrowed on these days.\n        \n        ### Context:\n        #### Query Context:\n        in 2013 refers to year(date) = 2013; rain and fog ocurred together refers to events = 'Fog-Rain'; id of bikes refers to biked_id;\n        \n        #### Database Schema:\n        {'db_id': 'bike_share_1', 'table_names_original': ['station', 'status', 'trip', 'weather'], 'table_names': ['station', 'status', 'trip', 'weather'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'lat'], [0, 'long'], [0, 'dock_count'], [0, 'city'], [0, 'installation_date'], [1, 'station_id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start_date'], [2, 'start_station_name'], [2, 'start_station_id'], [2, 'end_date'], [2, 'end_station_name'], [2, 'end_station_id'], [2, 'bike_id'], [2, 'subscription_type'], [2, 'zip_code'], [3, 'date'], [3, 'max_temperature_f'], [3, 'mean_temperature_f'], [3, 'min_temperature_f'], [3, 'max_dew_point_f'], [3, 'mean_dew_point_f'], [3, 'min_dew_point_f'], [3, 'max_humidity'], [3, 'mean_humidity'], [3, 'min_humidity'], [3, 'max_sea_level_pressure_inches'], [3, 'mean_sea_level_pressure_inches'], [3, 'min_sea_level_pressure_inches'], [3, 'max_visibility_miles'], [3, 'mean_visibility_miles'], [3, 'min_visibility_miles'], [3, 'max_wind_Speed_mph'], [3, 'mean_wind_speed_mph'], [3, 'max_gust_speed_mph'], [3, 'precipitation_inches'], [3, 'cloud_cover'], [3, 'events'], [3, 'wind_dir_degrees'], [3, 'zip_code']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'latitude'], [0, 'longitude'], [0, 'dock count'], [0, 'city'], [0, 'installation date'], [1, 'station id'], [1, 'bikes_available'], [1, 'docks_available'], [1, 'time'], [2, 'id'], [2, 'duration'], [2, 'start date'], [2, 'start station name'], [2, 'start station id'], [2, 'end date'], [2, 'end station name'], [2, 'end station id'], [2, 'bike id'], [2, 'subscription type'], [2, 'zip code'], [3, 'date'], [3, 'max temperature in Fahrenheit degree'], [3, 'mean temperature in Fahrenheit degree'], [3, 'min temperature in Fahrenheit degree'], [3, 'max dew point in Fahrenheit degree'], [3, 'mean dew point in Fahrenheit degree'], [3, 'min dew point in Fahrenheit degree'], [3, 'max humidity'], [3, 'mean humidity'], [3, 'min humidity'], [3, 'max sea level pressure in inches'], [3, 'mean sea level pressure in inches'], [3, 'min sea level pressure in inches'], [3, 'max visibility in miles'], [3, 'mean visibility in miles'], [3, 'min visibility in miles'], [3, 'max wind Speed in mph'], [3, 'mean wind Speed in mph'], [3, 'max gust Speed in mph'], [3, 'precipitation in inches'], [3, 'cloud cover'], [3, 'events'], [3, 'wind direction degrees'], [3, 'zip code']], 'column_types': ['text', 'integer', 'text', 'real', 'real', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text'], 'primary_keys': [1, 12], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.date, T1.bike_id FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code WHERE SUBSTR(CAST(T2.date AS TEXT), -4) = '2013' AND T2.events = 'Fog-Rain'"}],"db_id":"bike_share_1"}770{"prompt":"How many scenes are there on average in one act in Twelfth Night?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many scenes are there on average in one act in Twelfth Night?\n        \n        ### Context:\n        #### Query Context:\n        Twelfth Night refers to Title = 'Twelfth Night'; average scene = divide(sum(Scene), count(Act))\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.Scene) \/ COUNT(T2.Act) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Twelfth Night'"}],"db_id":"shakespeare"}771{"prompt":"What is the tail number of the flight with air carrier named Iscargo Hf: ICQ and arrival time of 1000 and below?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the tail number of the flight with air carrier named Iscargo Hf: ICQ and arrival time of 1000 and below?\n        \n        ### Context:\n        #### Query Context:\n        tail number refers to TAIL_NUM; Iscargo Hf: ICQ refers to Description = 'Iscargo Hf: ICQ'; arrival time of 1000 and below refers to ARR_TIME < = 1000;\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.TAIL_NUM FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T2.ARR_TIME <= 1000 AND T1.Description = 'Iscargo Hf: ICQ'"}],"db_id":"airline"}772{"prompt":"List down all the person IDs who taught course ID of 18.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down all the person IDs who taught course ID of 18.\n        \n        ### Context:\n        #### Query Context:\n        person IDs refers to taughtBy.p_id; course ID of 18\u00a0 refers to taughtBy.course_id = 18\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT p_id FROM taughtBy WHERE course_id = 18"}],"db_id":"computer_student"}773{"prompt":"How many solutions contain files found within the repository most people like?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many solutions contain files found within the repository most people like?\n        \n        ### Context:\n        #### Query Context:\n        more stars mean more people like this repository; most people like refers to max(Stars);\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.RepoId) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T1.Stars = ( SELECT MAX(Stars) FROM Repo )"}],"db_id":"codebase_comments"}774{"prompt":"Which region is Joshua Bennet located in?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which region is Joshua Bennet located in?\n        \n        ### Context:\n        #### Query Context:\n        \"Joshua Bennett\" is the name of Sales Team\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T FROM ( SELECT DISTINCT CASE  WHEN `Sales Team` = 'Joshua Bennett' THEN Region ELSE NULL END AS T FROM `Sales Team` ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}775{"prompt":"Calculate the total salary for employees from UK.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the total salary for employees from UK.\n        \n        ### Context:\n        #### Query Context:\n        \"UK\" is the Country; total salary refers to Sum(Salary)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(Salary) FROM Employees WHERE Country = 'UK'"}],"db_id":"retail_world"}776{"prompt":"Who is the Pro-Administration senator that runs from March 4, 1789 to December 31, 1791?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the Pro-Administration senator that runs from March 4, 1789 to December 31, 1791?\n        \n        ### Context:\n        #### Query Context:\n        Pro-Administration refers to party = 'Pro-Administration'; senator refers to type = 'sen'; runs from March 4, 1789 refers to start = '1789-03-04'; runs to refers to end = '1791-12-31';\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first_name, T1.last_name FROM historical AS T1 INNER JOIN `historical-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.party = 'Pro-Administration' AND T2.start = '1789-03-04' AND T2.end = '1791-12-31'"}],"db_id":"legislator"}777{"prompt":"Give the percentage of \"Automotive\" businesses among all the Yelp businesses.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the percentage of \"Automotive\" businesses among all the Yelp businesses.\n        \n        ### Context:\n        #### Query Context:\n        automotive businesses refers to category_name = 'Automotive'; percentage = MULTIPLY(DIVIDE(SUM(category_name = 'Automotive'), COUNT(business_id)), 1.0);\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.category_name LIKE 'Automotive' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.business_id) AS \"percentage\" FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id"}],"db_id":"public_review_platform"}778{"prompt":"How many events in total have happened on all the vivo devices in the year 2016?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many events in total have happened on all the vivo devices in the year 2016?\n        \n        ### Context:\n        #### Query Context:\n        vivo devices refers to phone_brand = 'vivo'; in the year 2016 refers to year(timestamp) = 2016;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.event_id) FROM events AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.event_id = T2.device_id WHERE STRFTIME('%Y', T1.timestamp) = '2016' AND T2.phone_brand = 'vivo'"}],"db_id":"talkingdata"}779{"prompt":"What is the highest average rating for action movies made in the USA?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the highest average rating for action movies made in the USA?\n        \n        ### Context:\n        #### Query Context:\n        USA is a country\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.rating) FROM movies AS T1 INNER JOIN u2base AS T2 ON T1.movieid = T2.movieid INNER JOIN movies2directors AS T3 ON T1.movieid = T3.movieid WHERE T1.country = 'USA' AND T3.genre = 'Action' GROUP BY T1.movieid ORDER BY AVG(T2.rating) DESC LIMIT 1"}],"db_id":"movielens"}780{"prompt":"How many papers were cited by schmidt99advanced cited word3555?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many papers were cited by schmidt99advanced cited word3555?\n        \n        ### Context:\n        #### Query Context:\n        paper cited by refers to citing_paper_id; citing_paper_id = 'schmidt99advanced';\n        \n        #### Database Schema:\n        {'db_id': 'citeseer', 'table_names_original': ['cites', 'paper', 'content'], 'table_names': ['cites', 'paper', 'content'], 'column_names_original': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper_id'], [1, 'class_label'], [2, 'paper_id'], [2, 'word_cited_id']], 'column_names': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper id'], [1, 'class label'], [2, 'paper id'], [2, 'word cited id']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [[1, 2], 3, [5, 6]], 'foreign_keys': [[5, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.paper_id) FROM cites AS T1 INNER JOIN content AS T2 ON T1.cited_paper_id = T2.paper_id WHERE T1.citing_paper_id = 'schmidt99advanced' AND T2.word_cited_id = 'word3555'"}],"db_id":"citeseer"}781{"prompt":"How many employees are there in the \"Miami\" office?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many employees are there in the \"Miami\" office?\n        \n        ### Context:\n        #### Query Context:\n        Miami office refers to locationcity = 'Miami'\n        \n        #### Database Schema:\n        {'db_id': 'human_resources', 'table_names_original': ['location', 'position', 'employee'], 'table_names': ['location', 'position', 'employee'], 'column_names_original': [[-1, '*'], [0, 'locationID'], [0, 'locationcity'], [0, 'address'], [0, 'state'], [0, 'zipcode'], [0, 'officephone'], [1, 'positionID'], [1, 'positiontitle'], [1, 'educationrequired'], [1, 'minsalary'], [1, 'maxsalary'], [2, 'ssn'], [2, 'lastname'], [2, 'firstname'], [2, 'hiredate'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'positionID'], [2, 'locationID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'location city'], [0, 'address'], [0, 'state'], [0, 'zip code'], [0, 'office phone'], [1, 'position id'], [1, 'position title'], [1, 'education required'], [1, 'minimum salary'], [1, 'maximum salary'], [2, 'social security number'], [2, 'last name'], [2, 'first name'], [2, 'hire date'], [2, 'salary'], [2, 'gender'], [2, 'performance'], [2, 'position id'], [2, 'location id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 7, 12], 'foreign_keys': [[19, 7], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM employee AS T1  INNER JOIN location AS T2  ON T1.locationID = T2.locationID WHERE T2.locationcity = 'Miami'"}],"db_id":"human_resources"}782{"prompt":"How many historical legislators were born in 1973?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many historical legislators were born in 1973?\n        \n        ### Context:\n        #### Query Context:\n        born in 1973 refers to birthday_bio = 1973\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM historical WHERE CAST(birthday_bio AS date) = 1973"}],"db_id":"legislator"}783{"prompt":"How many married male employees were born before the year 1960?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many married male employees were born before the year 1960?\n        \n        ### Context:\n        #### Query Context:\n        married refers to MaritalStatus = 'M'; male refers to Gender = 'M'; BirthDate < = '1959-12-31';\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 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Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the names of universities with number of students ranges from 400 to 1000.\n        \n        ### Context:\n        #### Query Context:\n        number of students ranges from 400 to 1000 refers to num_students BETWEEN 400 AND 1000; name of university refers to university_name\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.university_name FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id WHERE T1.num_students BETWEEN 400 AND 1000"}],"db_id":"university"}785{"prompt":"What is the full name of the Vice President of Sales. Give me the URL of his\/her photo.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of the Vice President of Sales. Give me the URL of his\/her photo.\n        \n        ### Context:\n        #### Query Context:\n        \"Vice Present of Sales\" refers to Title = 'Vice President, Sales'; full name refers to FirstName, LastName; url of photo refers to PhotoPath\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT FirstName, LastName FROM Employees WHERE Title = 'Vice President, Sales'"}],"db_id":"retail_world"}786{"prompt":"How many students for both genders graduated from a 2-year institute in Alabama in 2011?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many students for both genders graduated from a 2-year institute in Alabama in 2011?\n        \n        ### Context:\n        #### Query Context:\n        2-year institute refers to cohort = '2y all'; Alabama refers to state = 'Alabama'; in 2011 refers to year = 2011; T2.gender = 'B' means both genders;\n        \n        #### Database Schema:\n        {'db_id': 'college_completion', 'table_names_original': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'table_names': ['institution_details', 'institution_grads', 'state_sector_grads', 'state_sector_details'], 'column_names_original': [[-1, '*'], [0, 'unitid'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'hbcu'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student_count'], [0, 'awards_per_value'], [0, 'awards_per_state_value'], [0, 'awards_per_natl_value'], [0, 'exp_award_value'], [0, 'exp_award_state_value'], [0, 'exp_award_natl_value'], [0, 'exp_award_percentile'], [0, 'ft_pct'], [0, 'fte_value'], [0, 'fte_percentile'], [0, 'med_sat_value'], [0, 'med_sat_percentile'], [0, 'aid_value'], [0, 'aid_percentile'], [0, 'endow_value'], [0, 'endow_percentile'], [0, 'grad_100_value'], [0, 'grad_100_percentile'], [0, 'grad_150_value'], [0, 'grad_150_percentile'], [0, 'pell_value'], [0, 'pell_percentile'], [0, 'retain_value'], [0, 'retain_percentile'], [0, 'ft_fac_value'], [0, 'ft_fac_percentile'], [0, 'vsa_year'], [0, 'vsa_grad_after4_first'], [0, 'vsa_grad_elsewhere_after4_first'], [0, 'vsa_enroll_after4_first'], [0, 'vsa_enroll_elsewhere_after4_first'], [0, 'vsa_grad_after6_first'], [0, 'vsa_grad_elsewhere_after6_first'], [0, 'vsa_enroll_after6_first'], [0, 'vsa_enroll_elsewhere_after6_first'], [0, 'vsa_grad_after4_transfer'], [0, 'vsa_grad_elsewhere_after4_transfer'], [0, 'vsa_enroll_after4_transfer'], [0, 'vsa_enroll_elsewhere_after4_transfer'], [0, 'vsa_grad_after6_transfer'], [0, 'vsa_grad_elsewhere_after6_transfer'], [0, 'vsa_enroll_after6_transfer'], [0, 'vsa_enroll_elsewhere_after6_transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unitid'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'grad_cohort'], [1, 'grad_100'], [1, 'grad_150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'stateid'], [2, 'state'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'grad_cohort'], [2, 'grad_100'], [2, 'grad_150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'grad_cohort_ct'], [3, 'stateid'], [3, 'state'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted_pct'], [3, 'awards_per_state_value'], [3, 'awards_per_natl_value'], [3, 'exp_award_state_value'], [3, 'exp_award_natl_value'], [3, 'state_appr_value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_names': [[-1, '*'], [0, 'Unit ID number'], [0, 'chronname'], [0, 'city'], [0, 'state'], [0, 'level'], [0, 'control'], [0, 'basic'], [0, 'Historically Black College and Universities'], [0, 'flagship'], [0, 'long_x'], [0, 'lat_y'], [0, 'site'], [0, 'student count'], [0, 'awards per value'], [0, 'awards per state value'], [0, 'awards per national value'], [0, 'expected award value'], [0, 'expected award state value'], [0, 'expected award national value'], [0, 'exp_award_percentile'], [0, 'Full-time percentage'], [0, 'Full-time percentage'], [0, 'fte_percentile'], [0, 'median SAT value'], [0, 'median SAT percentile'], [0, 'aid value'], [0, 'aid percentile'], [0, 'endow value'], [0, 'endow percentile'], [0, 'graduation 100 value'], [0, 'graduation 100 percentile'], [0, 'graduation 150 value'], [0, 'graduation 150 percentile'], [0, 'pell value'], [0, 'pell percentile'], [0, 'retain value'], [0, 'retain percentile'], [0, 'full time faculty value'], [0, 'full time faculty percentile'], [0, 'voluntary system of accountability year'], [0, 'voluntary system of accountability after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year first time'], [0, 'voluntary system of accountability enrollment after 4 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 4 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 6 year first time'], [0, 'voluntary system of accountability enrollment after 6 year first time'], [0, 'voluntary system of accountability enrollment elsewhere after 6 year first time'], [0, 'voluntary system of accountability transfer after 6 year first time'], [0, 'voluntary system of accountability graduation elsewhere after 4 year'], [0, 'voluntary system of accountability enrollment after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 4 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'voluntary system of accountability graduation elsewhere after 6 years transfer'], [0, 'voluntary system of accountability enrollment after 6 years transfer'], [0, 'voluntary system of accountability enrollment elsewhere after 6 years transfer'], [0, 'similar'], [0, 'state_sector_ct'], [0, 'carnegie_ct'], [0, 'counted_pct'], [0, 'nicknames'], [0, 'cohort_size'], [1, 'unit id'], [1, 'year'], [1, 'gender'], [1, 'race'], [1, 'cohort'], [1, 'graduation cohort'], [1, 'graduation 100'], [1, 'graduation 150'], [1, 'grad_100_rate'], [1, 'grad_150_rate'], [2, 'state id'], [2, 'state name'], [2, 'state_abbr'], [2, 'control'], [2, 'level'], [2, 'year'], [2, 'gender'], [2, 'race'], [2, 'cohort'], [2, 'graduation cohort'], [2, 'graduation 100'], [2, 'graduation 150'], [2, 'grad_100_rate'], [2, 'grad_150_rate'], [2, 'graduation cohort count'], [3, 'state id'], [3, 'state name'], [3, 'state_post'], [3, 'level'], [3, 'control'], [3, 'schools_count'], [3, 'counted percentage'], [3, 'awards per state value'], [3, 'awards per national value'], [3, 'expected award state value'], [3, 'expected award national value'], [3, 'state appropriation value'], [3, 'state_appr_rank'], [3, 'grad_rate_rank'], [3, 'awards_per_rank']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, [88, 91, 92]], 'foreign_keys': [[63, 1], [73, 88], [74, 4], [89, 4]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.grad_cohort) FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE  T2.cohort = '2y all' AND T2.year = 2011 AND T1.state = 'Alabama'"}],"db_id":"college_completion"}787{"prompt":"What is the percentage difference of English and non-English-language crime movies in other countries in year 3?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the percentage difference of English and non-English-language crime movies in other countries in year 3?\n        \n        ### Context:\n        #### Query Context:\n        non-English-language refers to isEnglish = 'F'; The percentage difference can be computed by [count(isEnglish = 'T' in movies) - count(isEnglish = 'F' in movies) \/ (all movies)] * 100%\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(T1.isEnglish = 'T', 1, 0)) - SUM(IIF(T1.isEnglish = 'F', 1, 0)) AS REAL) * 100 \/ COUNT(T1.movieid) FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid WHERE T1.country = 'other' AND T1.year = 3"}],"db_id":"movielens"}788{"prompt":"In \"Sex, Pies and Idiot Scrapes\", how many percentage of votes did the 9 star score has?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In \"Sex, Pies and Idiot Scrapes\", how many percentage of votes did the 9 star score has?\n        \n        ### Context:\n        #### Query Context:\n        \"Sex, Pies and Idiot Scrapes\" is the title of episode; 9 star score refers to stars = 9; percentage refers to percent\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.percent FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE T1.title = 'Sex, Pies and Idiot Scrapes' AND T2.stars = 9;"}],"db_id":"simpson_episodes"}789{"prompt":"What is the average winning rate of the Montreal Canadiens in the Stanley Cup finals?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average winning rate of the Montreal Canadiens in the Stanley Cup finals?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(SUM(DIVIDE(W,G), COUNT(oppID);\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 'W'], [9, 'L'], [9, 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[19, 'wins'], [19, 'loses'], [19, 'ties'], [19, 'goals for'], [19, 'goals against'], [19, 'penalty minutes'], [19, 'Bench minors (minutes)'], [19, 'Power play goals'], [19, 'Power play chances'], [19, 'Shorthanded goals against'], [19, 'Power play goals against'], [19, 'Penalty kill chances'], [19, 'Shorthanded goals for'], [20, 'year'], [20, 'league ID'], [20, 'team ID'], [20, 'Games'], [20, 'wins'], [20, 'loses'], [20, 'ties'], [20, 'goals for'], [20, 'goals against'], [20, 'penalty minutes'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 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'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T2.W \/ T2.G) \/ SUM(T2.G + T2.W) FROM Teams AS T1 INNER JOIN TeamsSC AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.name = 'Montreal Canadiens'"}],"db_id":"hockey"}790{"prompt":"What is the book with the most orders?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the book with the most orders?\n        \n        ### Context:\n        #### Query Context:\n        books refers to title; the most orders refers to Max(Count(order_id))\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.title FROM order_line AS T1 INNER JOIN book AS T2 ON T1.book_id = T2.book_id GROUP BY T2.title ORDER BY COUNT(T1.book_id) DESC LIMIT 1"}],"db_id":"books"}791{"prompt":"Provide the destination city of the shipment shipped by January 16, 2017.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the destination city of the shipment shipped by January 16, 2017.\n        \n        ### Context:\n        #### Query Context:\n        January 16, 2017 refers to ship_date = '2017-01-16'; city refers to city_name\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.city_name FROM shipment AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.city_id WHERE T1.ship_date = '2017-01-16'"}],"db_id":"shipping"}792{"prompt":"Among the menus sponsored by Pacific Mail Steamship Company, how many menus have no more than 2 pages?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the menus sponsored by Pacific Mail Steamship Company, how many menus have no more than 2 pages?\n        \n        ### Context:\n        #### Query Context:\n        sponsored by Pacific Mail Steamship Company refers to sponsor = 'PACIFIC MAIL STEAMSHIP COMPANY'; no more than 2 pages refers to COUNT(page_number) < = 2;\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM Menu AS T1 INNER JOIN MenuPage AS T2 ON T1.id = T2.menu_id WHERE T1.sponsor = 'PACIFIC MAIL STEAMSHIP COMPANY' GROUP BY T2.menu_id HAVING COUNT(T2.page_number) <= 2"}],"db_id":"menu"}793{"prompt":"How many images have at least 25 attributes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many images have at least 25 attributes?\n        \n        ### Context:\n        #### Query Context:\n        images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM ( SELECT IMG_ID FROM IMG_OBJ_att GROUP BY IMG_ID HAVING COUNT(ATT_CLASS_ID) > 25 ) T1"}],"db_id":"image_and_language"}794{"prompt":"What is the full name of ABEDA and when was it established?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of ABEDA and when was it established?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Name, Established FROM organization WHERE Abbreviation = 'ABEDA'"}],"db_id":"mondial_geo"}795{"prompt":"Among the orders shipped in November, 1998 by air, how many orders were urgent?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the orders shipped in November, 1998 by air, how many orders were urgent?\n        \n        ### Context:\n        #### Query Context:\n        orders shipped in November, 1998 refer to o_orderkey where l_shipdate LIKE '1998-11-%'; by air refers to l_shipmode = 'AIR'; the order is urgent if o_orderpriority = '1-URGENT' ;\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.o_orderkey) FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T2.l_shipmode = 'AIR' AND T1.o_orderpriority = '1-URGENT' AND SUBSTR(T2.l_shipdate, 1, 7) = '1998-11'"}],"db_id":"retails"}796{"prompt":"How many cities have a salt lake located in it?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cities have a salt lake located in it?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.City) FROM located AS T1 INNER JOIN lake AS T2 ON T1.Lake = T2.Name WHERE T2.Type = 'salt'"}],"db_id":"mondial_geo"}797{"prompt":"Please name any three comedic works.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please name any three comedic works.\n        \n        ### Context:\n        #### Query Context:\n        comedic works refers to GenreType = 'comedy'\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Title FROM works WHERE GenreType = 'comedy' LIMIT 3"}],"db_id":"shakespeare"}798{"prompt":"List by name all customers who provided consent for the tag Older American.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List by name all customers who provided consent for the tag Older American.\n        \n        ### Context:\n        #### Query Context:\n        name refers to first; provided consent refers to \"Consumer consent provided?\" not in ('N\/A', null, 'empty');\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.Tags = 'Older American' AND T2.`Consumer consent provided?` != 'N\/A' AND T2.`Consumer consent provided?` IS NOT NULL AND T2.`Consumer consent provided?` != ''"}],"db_id":"retail_complains"}799{"prompt":"Among the users who received high compliments from other users, which users joined Yelp earliest?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the users who received high compliments from other users, which users joined Yelp earliest?\n        \n        ### Context:\n        #### Query Context:\n        high compliments refers to number_of_compliments = ' High'; joined Yelp earliest refers to min(user_yelping_since_year)\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.user_id FROM Users AS T1 INNER JOIN Users_Compliments AS T2 ON T1.user_id = T2.user_id WHERE T2.number_of_compliments = 'High' AND T1.user_yelping_since_year = ( SELECT MIN(user_yelping_since_year) FROM Users )"}],"db_id":"public_review_platform"}800{"prompt":"How many crimes were handled by Brendan Reilly on 7th October 2018?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many crimes were handled by Brendan Reilly on 7th October 2018?\n        \n        ### Context:\n        #### Query Context:\n        7th October 2018 refers to date like '10\/7\/2018%'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.alderman_last_name = 'Reilly' THEN 1 ELSE 0 END) FROM Crime AS T1 INNER JOIN Ward AS T2 ON T1.ward_no = T2.ward_no WHERE T2.alderman_name_suffix IS NULL AND T2.alderman_first_name = 'Brendan' AND date LIKE '10\/7\/2018%'"}],"db_id":"chicago_crime"}801{"prompt":"State description of medication taken by Joe Homenick.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State description of medication taken by Joe Homenick.\n        \n        ### Context:\n        #### Query Context:\n        description of medication refers to medications.DESCRIPTION;\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.DESCRIPTION FROM medications AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T2.first = 'Joye' AND T2.last = 'Homenick'"}],"db_id":"synthea"}802{"prompt":"What is the average price of products with more than fifty units in stock?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average price of products with more than fifty units in stock?\n        \n        ### Context:\n        #### Query Context:\n        more than fifty units in stock refers to UnitsInStock > 50; average price = avg(UnitPrice where UnitsInStock > 50)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(UnitPrice) \/ COUNT(UnitPrice) FROM Products WHERE UnitsInStock > 50"}],"db_id":"retail_world"}803{"prompt":"Provide the alias of the city with the highest population in year 2020.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the alias of the city with the highest population in year 2020.\n        \n        ### Context:\n        #### Query Context:\n        the highest population in year 2020 refers to MAX(population_2020);\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.alias FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.population_2020 = ( SELECT MAX(population_2020) FROM zip_data )"}],"db_id":"address"}804{"prompt":"How many orders which expected profits greater than 100?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many orders which expected profits greater than 100?\n        \n        ### Context:\n        #### Query Context:\n        expected profits greater than 100 = (SUBTRACT(msrp, buyPrice))>100;\n        \n        #### Database Schema:\n        {'db_id': 'car_retails', 'table_names_original': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'table_names': ['offices', 'employees', 'customers', 'orders', 'payments', 'productlines', 'products', 'orderdetails'], 'column_names_original': [[-1, '*'], [0, 'officeCode'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'employeeNumber'], [1, 'lastName'], [1, 'firstName'], [1, 'extension'], [1, 'email'], [1, 'officeCode'], [1, 'reportsTo'], [1, 'jobTitle'], [2, 'customerNumber'], [2, 'customerName'], [2, 'contactLastName'], [2, 'contactFirstName'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'salesRepEmployeeNumber'], [2, 'creditLimit'], [3, 'orderNumber'], [3, 'orderDate'], [3, 'requiredDate'], [3, 'shippedDate'], [3, 'status'], [3, 'comments'], [3, 'customerNumber'], [4, 'customerNumber'], [4, 'checkNumber'], [4, 'paymentDate'], [4, 'amount'], [5, 'productLine'], [5, 'textDescription'], [5, 'htmlDescription'], [5, 'image'], [6, 'productCode'], [6, 'productName'], [6, 'productLine'], [6, 'productScale'], [6, 'productVendor'], [6, 'productDescription'], [6, 'quantityInStock'], [6, 'buyPrice'], [6, 'MSRP'], [7, 'orderNumber'], [7, 'productCode'], [7, 'quantityOrdered'], [7, 'priceEach'], [7, 'orderLineNumber']], 'column_names': [[-1, '*'], [0, 'office code'], [0, 'city'], [0, 'phone'], [0, 'addressLine1'], [0, 'addressLine2'], [0, 'state'], [0, 'country'], [0, 'postalCode'], [0, 'territory'], [1, 'Employee Number'], [1, 'last name'], [1, 'first name'], [1, 'extension'], [1, 'email'], [1, 'office code'], [1, 'reports to'], [1, 'job title'], [2, 'customer number'], [2, 'customer name'], [2, 'contact last name'], [2, 'contact first name'], [2, 'phone'], [2, 'addressLine1'], [2, 'addressLine2'], [2, 'city'], [2, 'state'], [2, 'postalCode'], [2, 'country'], [2, 'sales representative employee number'], [2, 'credit limit'], [3, 'order number'], [3, 'order date'], [3, 'required Date'], [3, 'shipped date'], [3, 'status'], [3, 'comments'], [3, 'customer number'], [4, 'customer number'], [4, 'check Number'], [4, 'payment Date'], [4, 'amount'], [5, 'product line'], [5, 'text description'], [5, 'html description'], [5, 'image'], [6, 'product code'], [6, 'product name'], [6, 'product line'], [6, 'product scale'], [6, 'product vendor'], [6, 'product description'], [6, 'quantity in stock'], [6, 'buy price'], [6, 'Manufacturer Suggested Retail Price'], [7, 'order number'], [7, 'product code'], [7, 'quantity ordered'], [7, 'price for each'], [7, 'order Line Number']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'date', 'date', 'date', 'text', 'text', 'integer', 'integer', 'text', 'date', 'real', 'text', 'text', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'integer', 'real', 'integer'], 'primary_keys': [1, 10, 18, 31, [38, 39], 42, 46, [55, 56]], 'foreign_keys': [[16, 10], [15, 1], [29, 10], [37, 18], [38, 18], [48, 42]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.productCode) FROM orderdetails AS T1 INNER JOIN products AS T2 ON T1.productCode = T2.productCode WHERE T2.MSRP - T2.buyPrice > 100"}],"db_id":"car_retails"}805{"prompt":"How many tweets are in English?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many tweets are in English?\n        \n        ### Context:\n        #### Query Context:\n        english is the language and refers to Lang = 'en'\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(TweetID) AS tweet_number FROM twitter WHERE Lang = 'en'"}],"db_id":"social_media"}806{"prompt":"What is the company name of the supplier who supplies the product with the highest unit price?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the company name of the supplier who supplies the product with the highest unit price?\n        \n        ### Context:\n        #### Query Context:\n        the highest unit price refers to MAX(UnitPrice);\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.CompanyName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T1.UnitPrice = ( SELECT MAX(UnitPrice) FROM Products )"}],"db_id":"retail_world"}807{"prompt":"Find and list the id and geographic ID of the elderly customers with an education level below 3.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find and list the id and geographic ID of the elderly customers with an education level below 3.\n        \n        ### Context:\n        #### Query Context:\n        elderly customers with an education level below 3 refer to age > 65 where EDUCATIONNUM < 3; geographic ID refers to GEOID;\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT ID, GEOID FROM Customers WHERE EDUCATIONNUM < 3 AND age > 65"}],"db_id":"software_company"}808{"prompt":"How many cars on a train that runs in the east direction have a flat roof?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cars on a train that runs in the east direction have a flat roof?\n        \n        ### Context:\n        #### Query Context:\n        flat roof refers to roof = 'flat'\n        \n        #### Database Schema:\n        {'db_id': 'trains', 'table_names_original': ['cars', 'trains'], 'table_names': ['cars', 'trains'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'train_id'], [0, 'position'], [0, 'shape'], [0, 'len'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load_num'], [1, 'id'], [1, 'direction']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'train id'], [0, 'position'], [0, 'shape'], [0, 'length'], [0, 'sides'], [0, 'roof'], [0, 'wheels'], [0, 'load_shape'], [0, 'load number'], [1, 'id'], [1, 'direction']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 11], 'foreign_keys': [[2, 11]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.roof = 'flat' THEN 1 ELSE 0 END)as count FROM cars AS T1 INNER JOIN trains AS T2 ON T1.train_id = T2.id WHERE T2.direction = 'east'"}],"db_id":"trains"}809{"prompt":"How many of the crew members who are taller than 1.70m were born in Canada?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the crew members who are taller than 1.70m were born in Canada?\n        \n        ### Context:\n        #### Query Context:\n        taller than 1.70m refers to height_meters > 1.70; born in Canada refers to birth_country = 'Canada'\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(name) FROM Person WHERE height_meters > 1.70 AND birth_country = 'Canada';"}],"db_id":"simpson_episodes"}810{"prompt":"In customers with the first name of Erica, how many of them bought a quantity below 200?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In customers with the first name of Erica, how many of them bought a quantity below 200?\n        \n        ### Context:\n        #### Query Context:\n        quantity below 200 refers to quantity < 200;\n        \n        #### Database Schema:\n        {'db_id': 'sales', 'table_names_original': ['Customers', 'Employees', 'Products', 'Sales'], 'table_names': ['Customers', 'Employees', 'Products', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'FirstName'], [0, 'MiddleInitial'], [0, 'LastName'], [1, 'EmployeeID'], [1, 'FirstName'], [1, 'MiddleInitial'], [1, 'LastName'], [2, 'ProductID'], [2, 'Name'], [2, 'Price'], [3, 'SalesID'], [3, 'SalesPersonID'], [3, 'CustomerID'], [3, 'ProductID'], [3, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'First Name'], [0, 'Middle Initial'], [0, 'Last Name'], [1, 'Employee ID'], [1, 'First Name'], [1, 'Middle Initial'], [1, 'Last Name'], [2, 'Product ID'], [2, 'Name'], [2, 'Price'], [3, 'Sales ID'], [3, 'SalesPerson ID'], [3, 'Customer ID'], [3, 'Product ID'], [3, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 5, 9, 12], 'foreign_keys': [[15, 9], [14, 1], [13, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.ProductID) FROM Sales AS T1 INNER JOIN Customers AS T2 ON T1.CustomerID = T2.CustomerID WHERE T2.FirstName = 'Erica' AND T1.Quantity < 200"}],"db_id":"sales"}811{"prompt":"Calculate the average score per university under Alumni criteria in 2008.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average score per university under Alumni criteria in 2008.\n        \n        ### Context:\n        #### Query Context:\n        under Alumni criteria refers to criteria_name = 'Alumni'; in 2008 refers to year = 2008; average score refers to DIVIDE(SUM(score), COUNT(university_id))\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.score) FROM ranking_criteria AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.ranking_criteria_id WHERE T1.criteria_name = 'Alumni' AND T2.year = 2008"}],"db_id":"university"}812{"prompt":"Which company has the lowest unit price? Please give the company name and the product name.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which company has the lowest unit price? Please give the company name and the product name.\n        \n        ### Context:\n        #### Query Context:\n        lowest unit price refers to Min(UnitPrice)\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.CompanyName, T1.ProductName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T1.UnitPrice = ( SELECT MIN(UnitPrice) FROM Products )"}],"db_id":"retail_world"}813{"prompt":"Which team won by wickets in match ID 335993?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which team won by wickets in match ID 335993?\n        \n        ### Context:\n        #### Query Context:\n        team refers to Team_Name\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Team_Name FROM Team AS T1 INNER JOIN Match AS T2 ON T1.team_id = T2.match_winner INNER JOIN Player_Match AS T3 ON T1.Team_Id = T3.Team_Id INNER JOIN Win_By AS T4 ON T2.Win_Type = T4.Win_Id WHERE T2.Match_Id = '335993' GROUP BY T1.Team_Name"}],"db_id":"soccer_2016"}814{"prompt":"Among the active businesses located at Mesa, AZ, list the category and attributes of business with a low review count.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the active businesses located at Mesa, AZ, list the category and attributes of business with a low review count.\n        \n        ### Context:\n        #### Query Context:\n        active business refers to active = 'true': 'Mesa' is the name of city; 'AZ' is the state; low review count refers to review_count = 'Low'; category refers to category_name\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.category_name FROM Business AS T1 INNER JOIN Business_Categories AS T2 ON T1.business_id = T2.business_id INNER JOIN Categories AS T3 ON T2.category_id = T3.category_id WHERE T1.review_count = 'Low' AND T1.city = 'Mesa' AND T1.active = 'true' AND T1.state = 'AZ'"}],"db_id":"public_review_platform"}815{"prompt":"List all words cited in the AI class label.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all words cited in the AI class label.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'citeseer', 'table_names_original': ['cites', 'paper', 'content'], 'table_names': ['cites', 'paper', 'content'], 'column_names_original': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper_id'], [1, 'class_label'], [2, 'paper_id'], [2, 'word_cited_id']], 'column_names': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper id'], [1, 'class label'], [2, 'paper id'], [2, 'word cited id']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [[1, 2], 3, [5, 6]], 'foreign_keys': [[5, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.word_cited_id FROM paper AS T1 INNER JOIN content AS T2 ON T1.paper_id = T2.paper_id WHERE T1.class_label = 'AI'"}],"db_id":"citeseer"}816{"prompt":"Which team won the match in the EC division on January 20, 2008 at home?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which team won the match in the EC division on January 20, 2008 at home?\n        \n        ### Context:\n        #### Query Context:\n        won at home refers to FTR = 'H'; January 20, 2008 refers to Date = '2008-01-20'; EC division refers to Div = 'EC';\n        \n        #### Database Schema:\n        {'db_id': 'european_football_1', 'table_names_original': ['divisions', 'matchs'], 'table_names': ['divisions', 'matchs'], 'column_names_original': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Div'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'FTHG'], [1, 'FTAG'], [1, 'FTR'], [1, 'season']], 'column_names': [[-1, '*'], [0, 'division'], [0, 'name'], [0, 'country'], [1, 'Division'], [1, 'Date'], [1, 'HomeTeam'], [1, 'AwayTeam'], [1, 'Final-time Home-team Goals'], [1, 'Final-time Away-team Goals'], [1, 'Final-time Results'], [1, 'season']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'integer', 'integer', 'text', 'integer'], 'primary_keys': [1], 'foreign_keys': [[4, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT HomeTeam FROM matchs WHERE Div = 'EC' AND Date = '2008-01-20' AND FTR = 'H'"}],"db_id":"european_football_1"}817{"prompt":"To the products which could make the profit as 21.9037, what were their list price after October of 2012?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        To the products which could make the profit as 21.9037, what were their list price after October of 2012?\n        \n        ### Context:\n        #### Query Context:\n        Profit as 82.41 = SUTRACT(ListPrice, StandardCost); May of 2012 refers to StartDate = '2012-05'\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 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[60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], 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Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], 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'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.ListPrice FROM Product AS T1 INNER JOIN ProductListPriceHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ListPrice - T1.StandardCost > 21.9037 AND STRFTIME('%Y-%m-%d', T2.StartDate) >= '2012-10-01'"}],"db_id":"works_cycles"}818{"prompt":"Find out which hotel and travel business having the most review? Calculate the standard deviation of the review star for this business.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Find out which hotel and travel business having the most review? Calculate the standard deviation of the review star for this business.\n        \n        ### Context:\n        #### Query Context:\n        \"Hotel & Travel\" is the category_name; most review refers to Max(Count(category_id)); Average star per user = Divide (Sum (review_stars), Count(user_id))\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.category_id FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Reviews AS T3 ON T3.business_id = T1.business_id WHERE T2.category_name = 'Hotels & Travel' GROUP BY T2.category_id ORDER BY COUNT(T2.category_id) DESC LIMIT 1"}],"db_id":"public_review_platform"}819{"prompt":"List all paper ID and its class type with more than 20 cited words.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all paper ID and its class type with more than 20 cited words.\n        \n        ### Context:\n        #### Query Context:\n        class type refers to class_label; count(word_cited_id)>20\n        \n        #### Database Schema:\n        {'db_id': 'citeseer', 'table_names_original': ['cites', 'paper', 'content'], 'table_names': ['cites', 'paper', 'content'], 'column_names_original': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper_id'], [1, 'class_label'], [2, 'paper_id'], [2, 'word_cited_id']], 'column_names': [[-1, '*'], [0, 'cited_paper_id'], [0, 'citing_paper_id'], [1, 'paper id'], [1, 'class label'], [2, 'paper id'], [2, 'word cited id']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [[1, 2], 3, [5, 6]], 'foreign_keys': [[5, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.paper_id, T1.class_label FROM paper AS T1 INNER JOIN content AS T2 ON T1.paper_id = T2.paper_id GROUP BY T2.paper_id, T1.class_label HAVING COUNT(T2.word_cited_id) > 20"}],"db_id":"citeseer"}820{"prompt":"Who was the nominee playing the role of Katrina Ludlow in the Law & Order series?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who was the nominee playing the role of Katrina Ludlow in the Law & Order series?\n        \n        ### Context:\n        #### Query Context:\n        nominee refers to result = 'Nominee'; the role of Katrina Ludlow refers to role = 'Katrina Ludlow'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.name FROM Award AS T1 INNER JOIN Person AS T2 ON T1.person_id = T2.person_id WHERE T1.result = 'Nominee' AND T1.role = 'Katrina Ludlow' AND T1.series = 'Law and Order'"}],"db_id":"law_episode"}821{"prompt":"Provide the page IDs and name of the menu which had the highest page count.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the page IDs and name of the menu which had the highest page count.\n        \n        ### Context:\n        #### Query Context:\n        page IDs refers to page_number; highest page count refers to MAX(page_count);\n        \n        #### Database Schema:\n        {'db_id': 'menu', 'table_names_original': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'table_names': ['Dish', 'Menu', 'MenuPage', 'MenuItem'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus_appeared'], [0, 'times_appeared'], [0, 'first_appeared'], [0, 'last_appeared'], [0, 'lowest_price'], [0, 'highest_price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical_description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page_count'], [1, 'dish_count'], [2, 'id'], [2, 'menu_id'], [2, 'page_number'], [2, 'image_id'], [2, 'full_height'], [2, 'full_width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high_price'], [3, 'dish_id'], [3, 'created_at'], [3, 'updated_at'], [3, 'xpos'], [3, 'ypos']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'description'], [0, 'menus appeared'], [0, 'times appeared'], [0, 'first appeared'], [0, 'last appeared'], [0, 'lowest price'], [0, 'highest price'], [1, 'id'], [1, 'name'], [1, 'sponsor'], [1, 'event'], [1, 'venue'], [1, 'place'], [1, 'physical description'], [1, 'occasion'], [1, 'notes'], [1, 'call_number'], [1, 'keywords'], [1, 'language'], [1, 'date'], [1, 'location'], [1, 'location_type'], [1, 'currency'], [1, 'currency_symbol'], [1, 'status'], [1, 'page count'], [1, 'dish count'], [2, 'id'], [2, 'menu id'], [2, 'page number'], [2, 'image id'], [2, 'full height'], [2, 'full width'], [2, 'uuid'], [3, 'id'], [3, 'menu_page_id'], [3, 'price'], [3, 'high price'], [3, 'dish_id'], [3, 'created at'], [3, 'updated at'], [3, 'x position'], [3, 'y position']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'integer', 'text', 'text', 'real', 'real'], 'primary_keys': [1, 10, 30, 37], 'foreign_keys': [[31, 10], [38, 30], [41, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.page_number, T2.name FROM MenuPage AS T1 INNER JOIN Menu AS T2 ON T2.id = T1.menu_id ORDER BY T2.page_count DESC LIMIT 1"}],"db_id":"menu"}822{"prompt":"What are the most recent three independent nations?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the most recent three independent nations?\n        \n        ### Context:\n        #### Query Context:\n        Larger date of indepedence refers to more recent indepdence; Nation refers to country\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country ORDER BY T2.Independence DESC LIMIT 3"}],"db_id":"mondial_geo"}823{"prompt":"On which platform was Panzer Tactics released in 2007?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        On which platform was Panzer Tactics released in 2007?\n        \n        ### Context:\n        #### Query Context:\n        platform refers to platform_name; Panzer Tactics refers to game_name = 'Panzer Tactics'; released in 2007 refers to release_year = 2007\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T5.platform_name FROM game_publisher AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id INNER JOIN game AS T3 ON T1.game_id = T3.id INNER JOIN game_platform AS T4 ON T1.id = T4.game_publisher_id INNER JOIN platform AS T5 ON T4.platform_id = T5.id WHERE T3.game_name = 'Panzer Tactics' AND T4.release_year = 2007"}],"db_id":"video_games"}824{"prompt":"What is the detailed position of the store which has order SO - 000115?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the detailed position of the store which has order SO - 000115?\n        \n        ### Context:\n        #### Query Context:\n        Latitude and Longitude coordinates can be used to identify the detailed position of stores; store refers to StoreID WHERE OrderNumber = 'SO - 000115';\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Latitude, T2.Longitude FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID WHERE T1.OrderNumber = 'SO - 000115'"}],"db_id":"regional_sales"}825{"prompt":"Who is the head of the state where the most crowded city belongs?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the head of the state where the most crowded city belongs?\n        \n        ### Context:\n        #### Query Context:\n        head of the state refers to HeadOfState; most crowded city refers to MAX(Population);\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.HeadOfState FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode ORDER BY T2.Population DESC LIMIT 1"}],"db_id":"world"}826{"prompt":"Calculate the difference between the number of cars that has a horsepower of 130 with the model year 1970 and model year 1976","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the difference between the number of cars that has a horsepower of 130 with the model year 1970 and model year 1976\n        \n        ### Context:\n        #### Query Context:\n        a horsepower of 130 refers to horsepower = 130; difference = subtract(count(ID where model_year = 1970), count(ID where model_year = 1976)) where horsepower = 130\n        \n        #### Database Schema:\n        {'db_id': 'cars', 'table_names_original': ['country', 'price', 'data', 'production'], 'table_names': ['country', 'price', 'data', 'production'], 'column_names_original': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mpg'], [2, 'cylinders'], [2, 'displacement'], [2, 'horsepower'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car_name'], [3, 'ID'], [3, 'model_year'], [3, 'country']], 'column_names': [[-1, '*'], [0, 'origin'], [0, 'country'], [1, 'ID'], [1, 'price'], [2, 'ID'], [2, 'mileage per gallon'], [2, 'number of cylinders'], [2, 'displacement'], [2, 'horse power'], [2, 'weight'], [2, 'acceleration'], [2, 'model'], [2, 'car name'], [3, 'ID'], [3, 'model year'], [3, 'country']], 'column_types': ['text', 'integer', 'text', 'integer', 'real', 'integer', 'real', 'integer', 'real', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, [14, 15]], 'foreign_keys': [[5, 3], [14, 3], [14, 5], [16, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.model_year = 1970 THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.model_year = 1976 THEN 1 ELSE 0 END) FROM data AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID WHERE T1.horsepower = 130"}],"db_id":"cars"}827{"prompt":"Calculate the ratio between the number of representatives in Alabama and the number of representatives in Illinois.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the ratio between the number of representatives in Alabama and the number of representatives in Illinois.\n        \n        ### Context:\n        #### Query Context:\n        \"Alabama\" and \"Illinois\" are both state; Ratio = Divide (Count(state = 'Alabama'), Count(state = 'Illinois'))\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) \/ COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress"}],"db_id":"address"}828{"prompt":"What is the average number of root beers of the brand A&W sold in a day in August, 2014?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average number of root beers of the brand A&W sold in a day in August, 2014?\n        \n        ### Context:\n        #### Query Context:\n        average = DIVIDE(SUM(COUNT(RootBeerID WHERE BrandName = 'A&W' AND SUBSTR(TransactionDate, 1, 4) = '2014' AND SUBSTR(TransactionDate, 6, 2) = '08')), 31); A&W refers to BrandName = 'A&W'; in August, 2014 refers to SUBSTR(TransactionDate, 1, 4) = '2014' AND SUBSTR(TransactionDate, 6, 2) = '08';\n        \n        #### Database Schema:\n        {'db_id': 'beer_factory', 'table_names_original': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'table_names': ['customers', 'geolocation', 'location', 'rootbeerbrand', 'rootbeer', 'rootbeerreview', 'transaction'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'First'], [0, 'Last'], [0, 'StreetAddress'], [0, 'City'], [0, 'State'], [0, 'ZipCode'], [0, 'Email'], [0, 'PhoneNumber'], [0, 'FirstPurchaseDate'], [0, 'SubscribedToEmailList'], [0, 'Gender'], [1, 'LocationID'], [1, 'Latitude'], [1, 'Longitude'], [2, 'LocationID'], [2, 'LocationName'], [2, 'StreetAddress'], [2, 'City'], [2, 'State'], [2, 'ZipCode'], [3, 'BrandID'], [3, 'BrandName'], [3, 'FirstBrewedYear'], [3, 'BreweryName'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'CaneSugar'], [3, 'CornSyrup'], [3, 'Honey'], [3, 'ArtificialSweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'AvailableInCans'], [3, 'AvailableInBottles'], [3, 'AvailableInKegs'], [3, 'Website'], [3, 'FacebookPage'], [3, 'Twitter'], [3, 'WholesaleCost'], [3, 'CurrentRetailPrice'], [4, 'RootBeerID'], [4, 'BrandID'], [4, 'ContainerType'], [4, 'LocationID'], [4, 'PurchaseDate'], [5, 'CustomerID'], [5, 'BrandID'], [5, 'StarRating'], [5, 'ReviewDate'], [5, 'Review'], [6, 'TransactionID'], [6, 'CreditCardNumber'], [6, 'CustomerID'], [6, 'TransactionDate'], [6, 'CreditCardType'], [6, 'LocationID'], [6, 'RootBeerID'], [6, 'PurchasePrice']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'first name'], [0, 'last name'], [0, 'street address'], [0, 'City'], [0, 'State'], [0, 'zip code'], [0, 'Email'], [0, 'phone number'], [0, 'first purchase date'], [0, 'subscribed to email list'], [0, 'Gender'], [1, 'location id'], [1, 'Latitude'], [1, 'Longitude'], [2, 'location id'], [2, 'location name'], [2, 'street address'], [2, 'City'], [2, 'State'], [2, 'zip code'], [3, 'brand id'], [3, 'brand name'], [3, 'first brewed year'], [3, 'brewery name'], [3, 'City'], [3, 'State'], [3, 'Country'], [3, 'Description'], [3, 'cane sugar'], [3, 'corn syrup'], [3, 'Honey'], [3, 'artificial sweetener'], [3, 'Caffeinated'], [3, 'Alcoholic'], [3, 'available in cans'], [3, 'available in bottles'], [3, 'available in kegs'], [3, 'Website'], [3, 'facebook page'], [3, 'Twitter'], [3, 'wholesale cost'], [3, 'current retail price'], [4, 'root beer id'], [4, 'brandid'], [4, 'container type'], [4, 'location id'], [4, 'purchase date'], [5, 'customer id'], [5, 'brand id'], [5, 'star rating'], [5, 'review date'], [5, 'Review'], [6, 'transaction id'], [6, 'credit card number'], [6, 'customer id'], [6, 'transaction date'], [6, 'credit card type'], [6, 'location id'], [6, 'root beer id'], [6, 'purchase price']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'real', 'real', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'date', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'integer', 'date', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 13, 16, 22, 44, [49, 50], 54], 'foreign_keys': [[13, 16], [16, 13], [45, 22], [47, 16], [47, 13], [50, 22], [49, 1], [60, 44], [59, 16], [56, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(T1.BrandID) AS REAL) \/ 31 FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID WHERE T2.TransactionDate LIKE '2014-08%' AND T3.BrandName = 'A&W'"}],"db_id":"beer_factory"}829{"prompt":"What is the name of the university with the most international students in 2011?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the university with the most international students in 2011?\n        \n        ### Context:\n        #### Query Context:\n        in 2011 refers to year 2011; the most international students refers to MAX(DIVIDE(MULTIPLY(num_students,\u00a0 pct_international_students), 100)); name of university refers to university_id\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.university_name FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id WHERE T1.year = 2011 ORDER BY T1.pct_international_students DESC LIMIT 1"}],"db_id":"university"}830{"prompt":"Which artists have released singles with the tag 1970s?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which artists have released singles with the tag 1970s?\n        \n        ### Context:\n        #### Query Context:\n        releaseType = 'single';\n        \n        #### Database Schema:\n        {'db_id': 'music_tracker', 'table_names_original': ['torrents', 'tags'], 'table_names': ['torrents', 'tags'], 'column_names_original': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_names': [[-1, '*'], [0, 'groupName'], [0, 'totalSnatched'], [0, 'artist'], [0, 'groupYear'], [0, 'releaseType'], [0, 'groupId'], [0, 'id'], [1, 'index'], [1, 'id'], [1, 'tag']], 'column_types': ['text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [7, 8], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.artist FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T1.releaseType = 'single' AND T2.tag LIKE '1970s'"}],"db_id":"music_tracker"}831{"prompt":"Give the name of the youngest player.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the name of the youngest player.\n        \n        ### Context:\n        #### Query Context:\n        name of player refers to Player_Name; the youngest refers to max(DOB)\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT Player_Name FROM Player ORDER BY DOB DESC LIMIT 1"}],"db_id":"soccer_2016"}832{"prompt":"Among the Production Technicians who are single, how many of them are vendor contact?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the Production Technicians who are single, how many of them are vendor contact?\n        \n        ### Context:\n        #### Query Context:\n        Production Technicians refer to the\u00a0 JobTitle = 'Production Technician%'; single refers to MaritalStatus = 'S'; Vendor contact refers to PersonType = 'VC'\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], 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players?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In 2010, how many loses made by team 'BOS' and how many assists were made by the players?\n        \n        ### Context:\n        #### Query Context:\n        year = 2010; BOS refers to tmID; loses refer to L; assists refer to A;\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 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### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T1.L), SUM(T2.A) FROM Teams AS T1 INNER JOIN Scoring AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.tmID = 'BOS' AND T1.year = 2010"}],"db_id":"hockey"}834{"prompt":"Which recipe in the database contains the most total fat? Give its title.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which recipe in the database contains the most total fat? Give its title.\n        \n        ### Context:\n        #### Query Context:\n        the most total fat refers to MAX(total_fat)\n        \n        #### Database Schema:\n        {'db_id': 'cookbook', 'table_names_original': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'table_names': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'column_names_original': [[-1, '*'], [0, 'ingredient_id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe_id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield_unit'], [1, 'prep_min'], [1, 'cook_min'], [1, 'stnd_min'], [1, 'source'], [1, 'intro'], [1, 'directions'], [2, 'recipe_id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total_fat'], [2, 'sat_fat'], [2, 'cholestrl'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin_c'], [2, 'vitamin_a'], [2, 'fiber'], [2, 'pcnt_cal_carb'], [2, 'pcnt_cal_fat'], [2, 'pcnt_cal_prot'], [2, 'calories'], [3, 'quantity_id'], [3, 'recipe_id'], [3, 'ingredient_id'], [3, 'max_qty'], [3, 'min_qty'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_names': [[-1, '*'], [0, 'ingredient id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield unit'], [1, 'preparation minute'], [1, 'cooked minute'], [1, 'stand minute'], [1, 'source'], [1, 'introduction'], [1, 'directions'], [2, 'recipe id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total fat'], [2, 'saturated fat'], [2, 'cholesterol'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin c'], [2, 'vitamin a'], [2, 'fiber'], [2, 'percentage calculation carbo'], [2, 'percentage calculation fat'], [2, 'percentage calculation protein'], [2, 'calories'], [3, 'quantity id'], [3, 'recipe id'], [3, 'ingredient id'], [3, 'maximum quantity'], [3, 'minimum quantity'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text'], 'primary_keys': [1, 5, 16, 32], 'foreign_keys': [[16, 5], [33, 16], [34, 1], [33, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id ORDER BY T2.total_fat DESC LIMIT 1"}],"db_id":"cookbook"}835{"prompt":"List the product's name bought by the customer named Bill Shonely from the Central region.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the product's name bought by the customer named Bill Shonely from the Central region.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'superstore', 'table_names_original': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'table_names': ['people', 'product', 'central_superstore', 'east_superstore', 'south_superstore', 'west_superstore'], 'column_names_original': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'Row ID'], [2, 'Order ID'], [2, 'Order Date'], [2, 'Ship Date'], [2, 'Ship Mode'], [2, 'Customer ID'], [2, 'Region'], [2, 'Product ID'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'Row ID'], [3, 'Order ID'], [3, 'Order Date'], [3, 'Ship Date'], [3, 'Ship Mode'], [3, 'Customer ID'], [3, 'Region'], [3, 'Product ID'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'Row ID'], [4, 'Order ID'], [4, 'Order Date'], [4, 'Ship Date'], [4, 'Ship Mode'], [4, 'Customer ID'], [4, 'Region'], [4, 'Product ID'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'Row ID'], [5, 'Order ID'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Ship Mode'], [5, 'Customer ID'], [5, 'Region'], [5, 'Product ID'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_names': [[-1, '*'], [0, 'Customer ID'], [0, 'Customer Name'], [0, 'Segment'], [0, 'Country'], [0, 'City'], [0, 'State'], [0, 'Postal Code'], [0, 'Region'], [1, 'Product ID'], [1, 'Product Name'], [1, 'Category'], [1, 'Sub-Category'], [1, 'Region'], [2, 'row id'], [2, 'order id'], [2, 'order date'], [2, 'ship date'], [2, 'ship mode'], [2, 'customer id'], [2, 'Region'], [2, 'product id'], [2, 'Sales'], [2, 'Quantity'], [2, 'Discount'], [2, 'Profit'], [3, 'row id'], [3, 'order id'], [3, 'order date'], [3, 'ship date'], [3, 'ship mode'], [3, 'customer id'], [3, 'Region'], [3, 'product id'], [3, 'Sales'], [3, 'Quantity'], [3, 'Discount'], [3, 'Profit'], [4, 'row id'], [4, 'order id'], [4, 'order date'], [4, 'ship date'], [4, 'ship mode'], [4, 'customer id'], [4, 'Region'], [4, 'product id'], [4, 'Sales'], [4, 'Quantity'], [4, 'Discount'], [4, 'Profit'], [5, 'row id'], [5, 'order id'], [5, 'order date'], [5, 'ship date'], [5, 'ship mode'], [5, 'customer id'], [5, 'Region'], [5, 'product id'], [5, 'Sales'], [5, 'Quantity'], [5, 'Discount'], [5, 'Profit']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real', 'integer', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'real', 'integer', 'real', 'real'], 'primary_keys': [[1, 8], [9, 13], 14, 26, 38, 50], 'foreign_keys': [[21, 9], [20, 13], [19, 1], [20, 8], [33, 9], [32, 13], [31, 1], [32, 8], [45, 9], [44, 13], [43, 1], [44, 8], [57, 9], [56, 13], [55, 1], [56, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T3.`Product Name` FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.`Customer Name` = 'Bill Shonely' AND T2.Region = 'Central'"}],"db_id":"superstore"}836{"prompt":"Which country is 41% Christian? Give the full name of the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which country is 41% Christian? Give the full name of the country.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name FROM country AS T1 INNER JOIN religion AS T2 ON T1.Code = T2.Country WHERE T2.Name = 'Christian' AND T2.Percentage = 41"}],"db_id":"mondial_geo"}837{"prompt":"What is the review length of user 35026 to business with business ID 2?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the review length of user 35026 to business with business ID 2?\n        \n        ### Context:\n        #### Query Context:\n        user 35026 refers to user_id = 35026\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT review_length FROM Reviews WHERE user_id = 35026 AND business_id = 2"}],"db_id":"public_review_platform"}838{"prompt":"Which station has the highest number of stores? Calculate the said station's average maximum temperature in February 2012.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which station has the highest number of stores? Calculate the said station's average maximum temperature in February 2012.\n        \n        ### Context:\n        #### Query Context:\n        station with highest number of stores refers to station_nbr where Max(Count(store_nbr)); station refers to station_nbr; in February 2012 refers to substring (date, 1, 7) = '2012-02'; average maximum temperature = Divide(Sum(tmax), 29)\n        \n        #### Database Schema:\n        {'db_id': 'sales_in_weather', 'table_names_original': ['sales_in_weather', 'weather', 'relation'], 'table_names': ['sales_in_weather', 'weather', 'relation'], 'column_names_original': [[-1, '*'], [0, 'date'], [0, 'store_nbr'], [0, 'item_nbr'], [0, 'units'], [1, 'station_nbr'], [1, 'date'], [1, 'tmax'], [1, 'tmin'], [1, 'tavg'], [1, 'depart'], [1, 'dewpoint'], [1, 'wetbulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'codesum'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'stnpressure'], [1, 'sealevel'], [1, 'resultspeed'], [1, 'resultdir'], [1, 'avgspeed'], [2, 'store_nbr'], [2, 'station_nbr']], 'column_names': [[-1, '*'], [0, 'date'], [0, 'store number'], [0, 'item number'], [0, 'units'], [1, 'station number'], [1, 'date'], [1, 'temperature max'], [1, 'temperature min'], [1, 'temperature average'], [1, 'departure from normal'], [1, 'dew point'], [1, 'wet bulb'], [1, 'heat'], [1, 'cool'], [1, 'sunrise'], [1, 'sunset'], [1, 'code summarization'], [1, 'snowfall'], [1, 'preciptotal'], [1, 'station pressure'], [1, 'sea level'], [1, 'resultant speed'], [1, 'resultant direction'], [1, 'average speed'], [2, 'store number'], [2, 'station number']], 'column_types': ['text', 'date', 'integer', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [5, 6], 25], 'foreign_keys': [[26, 5], [25, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(T2.tmax) AS REAL) \/ 29 FROM ( SELECT station_nbr FROM relation GROUP BY station_nbr ORDER BY COUNT(store_nbr) DESC LIMIT 1 ) AS T1 INNER JOIN weather AS T2 ON T1.station_nbr = T2.station_nbr WHERE SUBSTR(T2.`date`, 1, 7) = '2012-02'"}],"db_id":"sales_in_weather"}839{"prompt":"How many transactions are there for product under the Mountain line?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many transactions are there for product under the Mountain line?\n        \n        ### Context:\n        #### Query Context:\n        The Mountain line refers to the product line, therefore ProductLine = 'M'\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 'AddressTypeID'], [25, 'rowguid'], [25, 'ModifiedDate'], [26, 'SalesTaxRateID'], [26, 'StateProvinceID'], [26, 'TaxType'], [26, 'TaxRate'], [26, 'Name'], [26, 'rowguid'], [26, 'ModifiedDate'], [27, 'BusinessEntityID'], [27, 'Name'], [27, 'SalesPersonID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'ModifiedDate'], [28, 'SalesOrderID'], [28, 'SalesReasonID'], [28, 'ModifiedDate'], [29, 'TransactionID'], [29, 'ProductID'], [29, 'ReferenceOrderID'], [29, 'ReferenceOrderLineID'], [29, 'TransactionDate'], [29, 'TransactionType'], [29, 'Quantity'], [29, 'ActualCost'], [29, 'ModifiedDate'], [30, 'UnitMeasureCode'], [30, 'Name'], [30, 'ModifiedDate'], [31, 'ProductID'], [31, 'StartDate'], [31, 'EndDate'], [31, 'StandardCost'], [31, 'ModifiedDate'], [32, 'ProductID'], [32, 'DocumentNode'], [32, 'ModifiedDate'], [33, 'ProductID'], [33, 'LocationID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'ModifiedDate'], [34, 'ProductID'], [34, 'ProductPhotoID'], [34, 'Primary'], [34, 'ModifiedDate'], [35, 'ProductReviewID'], [35, 'ProductID'], [35, 'ReviewerName'], [35, 'ReviewDate'], [35, 'EmailAddress'], [35, 'Rating'], [35, 'Comments'], [35, 'ModifiedDate'], [36, 'ShoppingCartItemID'], [36, 'ShoppingCartID'], [36, 'Quantity'], [36, 'ProductID'], [36, 'DateCreated'], [36, 'ModifiedDate'], [37, 'SpecialOfferID'], [37, 'ProductID'], [37, 'rowguid'], [37, 'ModifiedDate'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'TransactionID'], [39, 'ProductID'], [39, 'ReferenceOrderID'], [39, 'ReferenceOrderLineID'], [39, 'TransactionDate'], [39, 'TransactionType'], [39, 'Quantity'], [39, 'ActualCost'], [39, 'ModifiedDate'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'PreferredVendorStatus'], [40, 'ActiveFlag'], [40, 'PurchasingWebServiceURL'], [40, 'ModifiedDate'], [41, 'ProductID'], [41, 'BusinessEntityID'], [41, 'AverageLeadTime'], [41, 'StandardPrice'], [41, 'LastReceiptCost'], [41, 'LastReceiptDate'], [41, 'MinOrderQty'], [41, 'MaxOrderQty'], [41, 'OnOrderQty'], [41, 'UnitMeasureCode'], [41, 'ModifiedDate'], [42, 'PurchaseOrderID'], [42, 'RevisionNumber'], [42, 'Status'], [42, 'EmployeeID'], [42, 'VendorID'], [42, 'ShipMethodID'], [42, 'OrderDate'], [42, 'ShipDate'], [42, 'SubTotal'], [42, 'TaxAmt'], [42, 'Freight'], [42, 'TotalDue'], [42, 'ModifiedDate'], [43, 'PurchaseOrderID'], [43, 'PurchaseOrderDetailID'], [43, 'DueDate'], [43, 'OrderQty'], [43, 'ProductID'], [43, 'UnitPrice'], [43, 'LineTotal'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'WorkOrderID'], [44, 'ProductID'], [44, 'OrderQty'], [44, 'StockedQty'], [44, 'ScrappedQty'], [44, 'StartDate'], [44, 'EndDate'], [44, 'DueDate'], [44, 'ScrapReasonID'], [44, 'ModifiedDate'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'OperationSequence'], [45, 'LocationID'], [45, 'ScheduledStartDate'], [45, 'ScheduledEndDate'], [45, 'ActualStartDate'], [45, 'ActualEndDate'], [45, 'ActualResourceHrs'], [45, 'PlannedCost'], [45, 'ActualCost'], [45, 'ModifiedDate'], [46, 'CustomerID'], [46, 'PersonID'], [46, 'StoreID'], [46, 'TerritoryID'], [46, 'AccountNumber'], [46, 'rowguid'], [46, 'ModifiedDate'], [47, 'ProductID'], [47, 'StartDate'], [47, 'EndDate'], [47, 'ListPrice'], [47, 'ModifiedDate'], [48, 'AddressID'], [48, 'AddressLine1'], [48, 'AddressLine2'], [48, 'City'], [48, 'StateProvinceID'], [48, 'PostalCode'], [48, 'SpatialLocation'], [48, 'rowguid'], [48, 'ModifiedDate'], [49, 'AddressTypeID'], [49, 'Name'], [49, 'rowguid'], [49, 'ModifiedDate'], [50, 'BillOfMaterialsID'], [50, 'ProductAssemblyID'], [50, 'ComponentID'], [50, 'StartDate'], [50, 'EndDate'], [50, 'UnitMeasureCode'], [50, 'BOMLevel'], [50, 'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 'rowguid'], [11, 'modified date'], [12, 'Product Model ID'], [12, 'Name'], [12, 'Catalog Description'], [12, 'Instructions'], [12, 'rowguid'], [12, 'modified date'], [13, 'Product Model ID'], [13, 'Product Description ID'], [13, 'Culture ID'], [13, 'modified date'], [14, 'Product Photo ID'], [14, 'Thumb Nail Photo'], [14, 'Thumbnail Photo File Name'], [14, 'large photo'], [14, 'Large Photo File Name'], [14, 'Modified Date'], [15, 'Product Subcategory ID'], [15, 'Product Category ID'], [15, 'Name'], [15, 'rowguid'], [15, 'modified date'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'modified date'], [17, 'TerritoryID'], [17, 'Name'], [17, 'Country Region Code'], [17, 'Group'], [17, 'Sales Year to Date'], [17, 'Sales Last Year'], [17, 'Cost Year to Date'], [17, 'Cost Last Year'], [17, 'rowguid'], [17, 'modified date'], [18, 'Business Entity ID'], [18, 'Territory ID'], [18, 'Sales Quota'], [18, 'Bonus'], [18, 'Commission percentage'], [18, 'sales year to date'], [18, 'Sales Last Year'], [18, 'rowguid'], [18, 'modified date'], [19, 'Business Entity ID'], [19, 'Quota Date'], [19, 'Sales Quota'], [19, 'rowguid'], [19, 'modified date'], [20, 'Business Entity ID'], [20, 'Territory ID'], [20, 'Start Date'], [20, 'End Date'], [20, 'rowguid'], [20, 'modified date'], [21, 'Scrap Reason ID'], [21, 'Name'], [21, 'modified date'], [22, 'ShiftID'], [22, 'Name'], [22, 'Start Time'], [22, 'End Time'], [22, 'modified date'], [23, 'Ship Method ID'], [23, 'Name'], [23, 'Ship Base'], [23, 'Ship Rate'], [23, 'rowguid'], [23, 'modified date'], [24, 'Special Offer ID'], [24, 'Description'], [24, 'Discount precentage'], [24, 'Type'], [24, 'Category'], [24, 'Start Date'], [24, 'End Date'], [24, 'Min Quality'], [24, 'Max Quality'], [24, 'rowguid'], [24, 'modified date'], [25, 'Business Entity ID'], [25, 'Address ID'], [25, 'Address Type ID'], [25, 'rowguid'], [25, 'modified date'], [26, 'Sales Tax Rate ID'], [26, 'State Province ID'], [26, 'Tax Type'], [26, 'Tax Rate'], [26, 'Name'], [26, 'rowguid'], [26, 'modified date'], [27, 'Business Entity ID'], [27, 'Name'], [27, 'Sales Person ID'], [27, 'Demographics'], [27, 'rowguid'], [27, 'modified date'], [28, 'Sales Order ID'], [28, 'Sales Reason ID'], [28, 'modified date'], [29, 'Transaction ID'], [29, 'Product ID'], [29, 'Reference Order ID'], [29, 'Reference Order Line ID'], [29, 'Transaction Date'], [29, 'Transaction Type'], [29, 'Quantity'], [29, 'Actual Cost'], [29, 'modified date'], [30, 'Unit Measure Code'], [30, 'Name'], [30, 'modified date'], [31, 'Product ID'], [31, 'Start Date'], [31, 'End Date'], [31, 'Standard Cost'], [31, 'modified date'], [32, 'Product ID'], [32, 'Document Node'], [32, 'modified date'], [33, 'Product ID'], [33, 'Location ID'], [33, 'Shelf'], [33, 'Bin'], [33, 'Quantity'], [33, 'rowguid'], [33, 'modified date'], [34, 'Product ID'], [34, 'Product Photo ID'], [34, 'Primary'], [34, 'Modified Date'], [35, 'Product Review ID'], [35, 'Product ID'], [35, 'Reviewer Name'], [35, 'Review Date'], [35, 'Email Address'], [35, 'Rating'], [35, 'Comments'], [35, 'modified date'], [36, 'Shopping CartItem ID'], [36, 'Shopping Cart ID'], [36, 'Quantity'], [36, 'Product ID'], [36, 'Date Created'], [36, 'modified date'], [37, 'Special Offer ID'], [37, 'Product ID'], [37, 'rowguid'], [37, 'modified date'], [38, 'SalesOrderID'], [38, 'SalesOrderDetailID'], [38, 'CarrierTrackingNumber'], [38, 'OrderQty'], [38, 'ProductID'], [38, 'SpecialOfferID'], [38, 'UnitPrice'], [38, 'UnitPriceDiscount'], [38, 'LineTotal'], [38, 'rowguid'], [38, 'ModifiedDate'], [39, 'Transaction ID'], [39, 'Product ID'], [39, 'Reference Order ID'], [39, 'Reference Order Line ID'], [39, 'Transaction Date'], [39, 'Transaction Type'], [39, 'Quantity'], [39, 'Actual Cost'], [39, 'modified date'], [40, 'BusinessEntityID'], [40, 'AccountNumber'], [40, 'Name'], [40, 'CreditRating'], [40, 'Preferred Vendor Status'], [40, 'Active Flag'], [40, 'PurchasingWebServiceURL'], [40, 'modified date'], [41, 'Product ID'], [41, 'Business Entity ID'], [41, 'Average Lead Time'], [41, 'Standard Price'], [41, 'Last Receipt Cost'], [41, 'Last Receipt Date'], [41, 'Min Order Quantity'], [41, 'Max Order Quantity'], [41, 'On Order Quantity'], [41, 'Unit Measure Code'], [41, 'modified date'], [42, 'Purchase Order ID'], [42, 'Revision Number'], [42, 'Status'], [42, 'Employee ID'], [42, 'Vendor ID'], [42, 'Ship Method ID'], [42, 'Order Date'], [42, 'Ship Date'], [42, 'SubTotal'], [42, 'Tax Amount'], [42, 'Freight'], [42, 'Total Due'], [42, 'modified date'], [43, 'Purchase Order ID'], [43, 'Purchase Orde rDetail ID'], [43, 'Due Date'], [43, 'Order Quantity'], [43, 'Product ID'], [43, 'Unit Price'], [43, 'Line Total'], [43, 'ReceivedQty'], [43, 'RejectedQty'], [43, 'StockedQty'], [43, 'ModifiedDate'], [44, 'Work Order ID'], [44, 'Product ID'], [44, 'Order Quantity'], [44, 'Stocked Quantity'], [44, 'Scrapped Quantity'], [44, 'Start Date'], [44, 'End Date'], [44, 'Due Date'], [44, 'Scrap Reason ID'], [44, 'modified date'], [45, 'WorkOrderID'], [45, 'ProductID'], [45, 'Operation Sequence'], [45, 'Location ID'], [45, 'Scheduled Start Date'], [45, 'Scheduled End Date'], [45, 'Actual Start Date'], [45, 'ActualEndDate'], [45, 'Actual Resource Hours'], [45, 'Planned Cost'], [45, 'ActualCost'], [45, 'modified date'], [46, 'Customer ID'], [46, 'Person ID'], [46, 'Store ID'], [46, 'Territory ID'], [46, 'Account Number'], [46, 'rowguid'], [46, 'modified date'], [47, 'Product ID'], [47, 'Start Date'], [47, 'End Date'], [47, 'ListPrice'], [47, 'modified date'], [48, 'address id'], [48, 'address line 1'], [48, 'address line 2'], [48, 'City'], [48, 'state province id'], [48, 'postal code'], [48, 'spatial location'], [48, 'rowguid'], [48, 'modified date'], [49, 'address type id'], [49, 'Name'], [49, 'rowguid'], [49, 'modified date'], [50, 'bill of materials id'], [50, 'product assembly id'], [50, 'component ID'], [50, 'start date'], [50, 'end date'], [50, 'unit measure code'], [50, 'bill of materials level'], [50, 'per assembly quantity'], [50, 'modified date'], [51, 'business entity id'], [51, 'rowguid'], [51, 'modified date'], [52, 'Contact Type ID'], [52, 'Name'], [52, 'modified date'], [53, 'Currency Rate ID'], [53, 'Currency Rate Date'], [53, 'From Currency Code'], [53, 'ToCurrencyCode'], [53, 'Average Rate'], [53, 'End Of Day Rate'], [53, 'modified date'], [54, 'Department ID'], [54, 'Name'], [54, 'Group Name'], [54, 'modified date'], [55, 'Business Entity ID'], [55, 'DepartmentI D'], [55, 'Shift ID'], [55, 'Start Date'], [55, 'End Date'], [55, 'modified date'], [56, 'Business Entity ID'], [56, 'Rate Change Date'], [56, 'Rate'], [56, 'Pay Frequency'], [56, 'modified date'], [57, 'Job Candidate ID'], [57, 'Business Entity ID'], [57, 'Resume'], [57, 'modified date'], [58, 'Location ID'], [58, 'Name'], [58, 'Cost Rate'], [58, 'Availability'], [58, 'modified date'], [59, 'Phone Number Type ID'], [59, 'Phone Number'], [59, 'modified date'], [60, 'Product ID'], [60, 'Name'], [60, 'Product Number'], [60, 'Make Flag'], [60, 'Finished Goods Flag'], [60, 'Color'], [60, 'Safety Stock Level'], [60, 'Reorder Point'], [60, 'Standard Cost'], [60, 'List Price'], [60, 'Size'], [60, 'Size Unit Measure Code'], [60, 'Weight Unit Measure Code'], [60, 'Weight'], [60, 'Days To Manufacture'], [60, 'Product Line'], [60, 'Class'], [60, 'Style'], [60, 'Product Subcategory ID'], [60, 'Product Model ID'], [60, 'Sell Start Date'], [60, 'Sell End Date'], [60, 'Discontinued Date'], [60, 'rowguid'], [60, 'modified date'], [61, 'Document Node'], [61, 'Document Level'], [61, 'Title'], [61, 'Owner'], [61, 'Folder Flag'], [61, 'File Name'], [61, 'File Extension'], [61, 'Revision'], [61, 'Change Number'], [61, 'Status'], [61, 'Document Summary'], [61, 'Document'], [61, 'rowguid'], [61, 'modified date'], [62, 'State Province ID'], [62, 'State Province Code'], [62, 'Country Region Code'], [62, 'Is Only State Province Flag'], [62, 'Name'], [62, 'Territory ID'], [62, 'rowguid'], [62, 'modified date'], [63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 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'integer', 'integer', 'integer', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'datetime', 'real', 'real', 'real', 'datetime', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'date', 'date', 'real', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.TransactionID) FROM Product AS T1 INNER JOIN TransactionHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ProductLine = 'M'"}],"db_id":"works_cycles"}840{"prompt":"Among the matches held in 2015, who is the winning team in the match ID 829768?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the matches held in 2015, who is the winning team in the match ID 829768?\n        \n        ### Context:\n        #### Query Context:\n        in 2015 refers to Match_Date LIKE '2015%'; the winning team refers to Team_Id = Match_Winner; match ID 829768 refers to Match_Id = 829768\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Team_Name FROM Match AS T1 INNER JOIN Team AS T2 ON T2.Team_Id = T1.Match_Winner WHERE T1.Match_Date LIKE '2015%' AND T1.Match_Id = 829768"}],"db_id":"soccer_2016"}841{"prompt":"Among the employees who are store contacts, how many of them have a title of \"Mr.\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the employees who are store contacts, how many of them have a title of \"Mr.\"?\n        \n        ### Context:\n        #### Query Context:\n        store contact refers to PersonType = 'SC';\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 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89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(BusinessEntityID) FROM Person WHERE PersonType = 'SC' AND Title = 'Mr.'"}],"db_id":"works_cycles"}842{"prompt":"How many Asian female patients take oxaliplatin 5 MG\/ML [Eloxatin]?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Asian female patients take oxaliplatin 5 MG\/ML [Eloxatin]?\n        \n        ### Context:\n        #### Query Context:\n        female refers to gender = 'F'; oxaliplatin 5 MG\/ML [Eloxatin] refers to medications where DESCRIPTION = 'oxaliplatin 5 MG\/ML [Eloxatin]';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T2.patient) FROM medications AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T1.DESCRIPTION = 'oxaliplatin 5 MG\/ML [Eloxatin]' AND T2.race = 'asian' AND T2.gender = 'F'"}],"db_id":"synthea"}843{"prompt":"How many books were ordered by customer Kandy Adamec?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many books were ordered by customer Kandy Adamec?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM order_line AS T1 INNER JOIN cust_order AS T2 ON T2.order_id = T1.order_id INNER JOIN customer AS T3 ON T3.customer_id = T2.customer_id WHERE T3.first_name = 'Kandy' AND T3.last_name = 'Adamec'"}],"db_id":"books"}844{"prompt":"How many pounds did Sue Newell transport during her first shipment?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many pounds did Sue Newell transport during her first shipment?\n        \n        ### Context:\n        #### Query Context:\n        first shipment refers to Min(ship_date); pounds refers to weight\n        \n        #### Database Schema:\n        {'db_id': 'shipping', 'table_names_original': ['city', 'customer', 'driver', 'truck', 'shipment'], 'table_names': ['city', 'customer', 'driver', 'truck', 'shipment'], 'column_names_original': [[-1, '*'], [0, 'city_id'], [0, 'city_name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'cust_id'], [1, 'cust_name'], [1, 'annual_revenue'], [1, 'cust_type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver_id'], [2, 'first_name'], [2, 'last_name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip_code'], [2, 'phone'], [3, 'truck_id'], [3, 'make'], [3, 'model_year'], [4, 'ship_id'], [4, 'cust_id'], [4, 'weight'], [4, 'truck_id'], [4, 'driver_id'], [4, 'city_id'], [4, 'ship_date']], 'column_names': [[-1, '*'], [0, 'city id'], [0, 'city name'], [0, 'state'], [0, 'population'], [0, 'area'], [1, 'customer id'], [1, 'customer name'], [1, 'annual revenue'], [1, 'customer type'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'phone'], [2, 'driver id'], [2, 'first name'], [2, 'last name'], [2, 'address'], [2, 'city'], [2, 'state'], [2, 'zip code'], [2, 'phone'], [3, 'truck id'], [3, 'make'], [3, 'model year'], [4, 'ship id'], [4, 'customer id'], [4, 'weight'], [4, 'truck id'], [4, 'driver id'], [4, 'city id'], [4, 'ship date']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 6, 15, 23, 26], 'foreign_keys': [[29, 23], [30, 15], [31, 1], [27, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.weight FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id WHERE T2.first_name = 'Sue' AND T2.last_name = 'Newell' ORDER BY T1.ship_date ASC LIMIT 1"}],"db_id":"shipping"}845{"prompt":"Name the top five titles that sold more than average and list them in descending order of the number of sales in California stores?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name the top five titles that sold more than average and list them in descending order of the number of sales in California stores?\n        \n        ### Context:\n        #### Query Context:\n        qty is abbreviation for quantity; sold more than average refers to qty > AVG(qty); california refers to state = 'CA\"\n        \n        #### Database Schema:\n        {'db_id': 'book_publishing_company', 'table_names_original': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'table_names': ['authors', 'jobs', 'publishers', 'employee', 'pub_info', 'stores', 'discounts', 'titles', 'roysched', 'sales', 'titleauthor'], 'column_names_original': [[-1, '*'], [0, 'au_id'], [0, 'au_lname'], [0, 'au_fname'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job_id'], [1, 'job_desc'], [1, 'min_lvl'], [1, 'max_lvl'], [2, 'pub_id'], [2, 'pub_name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'emp_id'], [3, 'fname'], [3, 'minit'], [3, 'lname'], [3, 'job_id'], [3, 'job_lvl'], [3, 'pub_id'], [3, 'hire_date'], [4, 'pub_id'], [4, 'logo'], [4, 'pr_info'], [5, 'stor_id'], [5, 'stor_name'], [5, 'stor_address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discounttype'], [6, 'stor_id'], [6, 'lowqty'], [6, 'highqty'], [6, 'discount'], [7, 'title_id'], [7, 'title'], [7, 'type'], [7, 'pub_id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'ytd_sales'], [7, 'notes'], [7, 'pubdate'], [8, 'title_id'], [8, 'lorange'], [8, 'hirange'], [8, 'royalty'], [9, 'stor_id'], [9, 'ord_num'], [9, 'ord_date'], [9, 'qty'], [9, 'payterms'], [9, 'title_id'], [10, 'au_id'], [10, 'title_id'], [10, 'au_ord'], [10, 'royaltyper']], 'column_names': [[-1, '*'], [0, 'author id'], [0, 'author last name'], [0, 'author first name'], [0, 'phone'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'contract'], [1, 'job id'], [1, 'job description'], [1, 'min level'], [1, 'max level'], [2, 'publisher id'], [2, 'publisher name'], [2, 'city'], [2, 'state'], [2, 'country'], [3, 'employee id'], [3, 'first name'], [3, 'minit'], [3, 'last name'], [3, 'job id'], [3, 'job level'], [3, 'publisher id'], [3, 'hire_date'], [4, 'publication id'], [4, 'logo'], [4, \"publisher's information\"], [5, 'store id'], [5, 'store name'], [5, 'store address'], [5, 'city'], [5, 'state'], [5, 'zip'], [6, 'discount type'], [6, 'store id'], [6, 'low quantity'], [6, 'high quantity'], [6, 'discount'], [7, 'title id'], [7, 'title'], [7, 'type'], [7, 'publisher id'], [7, 'price'], [7, 'advance'], [7, 'royalty'], [7, 'year to date sales'], [7, 'notes'], [7, 'publication date'], [8, 'title_id'], [8, 'low range'], [8, 'high range'], [8, 'royalty'], [9, 'store id'], [9, 'order number'], [9, 'order date'], [9, 'quantity'], [9, 'payterms'], [9, 'title id'], [10, 'author id'], [10, 'title id'], [10, 'author ordering'], [10, 'royaltyper']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'datetime', 'text', 'blob', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'text', 'datetime', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [1, 10, 14, 19, 27, 30, 41, [55, 56, 60], [61, 62]], 'foreign_keys': [[25, 14], [23, 10], [27, 14], [37, 30], [44, 14], [51, 41], [60, 41], [55, 30], [62, 41], [61, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM titles AS T1 INNER JOIN sales AS T2 ON T1.title_id = T2.title_id INNER JOIN publishers AS T3 ON T1.pub_id = T3.pub_id WHERE T2.qty > ( SELECT CAST(SUM(qty) AS REAL) \/ COUNT(title_id) FROM sales ) AND T3.state = 'CA' ORDER BY T2.qty DESC LIMIT 5"}],"db_id":"book_publishing_company"}846{"prompt":"Please indicate the names of customers whose orders are eligible for 10% discount with order dates between 1\/1\/1994 and 1\/1\/1995.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please indicate the names of customers whose orders are eligible for 10% discount with order dates between 1\/1\/1994 and 1\/1\/1995.\n        \n        ### Context:\n        #### Query Context:\n        customer name refers to c_name; 10% discount refers to l_discount = 0.1; order dates between 1\/1\/1994 and 1\/1\/1995 refers to year(o_orderdate) = 1994 OR o_orderdate = '1995-01-01'\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.c_name FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey INNER JOIN customer AS T3 ON T1.o_custkey = T3.c_custkey WHERE T2.l_discount = 0.1 AND STRFTIME('%Y', T1.o_orderdate) BETWEEN 1994 AND 1995"}],"db_id":"retails"}847{"prompt":"List out which business category that are most likely to have average good review in Arizona?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List out which business category that are most likely to have average good review in Arizona?\n        \n        ### Context:\n        #### Query Context:\n        average good review refers to review_count > = 3; Arizona refers to state = 'AZ'; business category refers to category_name\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T4.category_name FROM Reviews AS T1 INNER JOIN Business AS T2 ON T1.business_id = T2.business_id INNER JOIN Business_Categories AS T3 ON T2.business_id = T3.business_id INNER JOIN Categories AS T4 ON T3.category_id = T4.category_id WHERE T2.state LIKE 'AZ' AND T1.review_stars >= 3"}],"db_id":"public_review_platform"}848{"prompt":"What is the revision ID for the page on Catalan titled \"Arqueologia\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the revision ID for the page on Catalan titled \"Arqueologia\"?\n        \n        ### Context:\n        #### Query Context:\n        lid = 1 means it's Catalan language; Arqueologia refers to title = 'Arqueologia'; revision ID refers to revision\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT revision FROM pages WHERE lid = 1 AND title = 'Arqueologia'"}],"db_id":"language_corpus"}849{"prompt":"What is the average height of the male Olympic competitors from Finland?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average height of the male Olympic competitors from Finland?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(SUM(height), COUNT(id)) where region_name = 'Finland' and gender = 'M';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T3.height) FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.region_name = 'Finland' AND T3.gender = 'M'"}],"db_id":"olympics"}850{"prompt":"How many ingredients are there in Apricot Yogurt Parfaits?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many ingredients are there in Apricot Yogurt Parfaits?\n        \n        ### Context:\n        #### Query Context:\n        Apricot Yogurt Parfaits refers to title\n        \n        #### Database Schema:\n        {'db_id': 'cookbook', 'table_names_original': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'table_names': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'column_names_original': [[-1, '*'], [0, 'ingredient_id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe_id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield_unit'], [1, 'prep_min'], [1, 'cook_min'], [1, 'stnd_min'], [1, 'source'], [1, 'intro'], [1, 'directions'], [2, 'recipe_id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total_fat'], [2, 'sat_fat'], [2, 'cholestrl'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin_c'], [2, 'vitamin_a'], [2, 'fiber'], [2, 'pcnt_cal_carb'], [2, 'pcnt_cal_fat'], [2, 'pcnt_cal_prot'], [2, 'calories'], [3, 'quantity_id'], [3, 'recipe_id'], [3, 'ingredient_id'], [3, 'max_qty'], [3, 'min_qty'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_names': [[-1, '*'], [0, 'ingredient id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield unit'], [1, 'preparation minute'], [1, 'cooked minute'], [1, 'stand minute'], [1, 'source'], [1, 'introduction'], [1, 'directions'], [2, 'recipe id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total fat'], [2, 'saturated fat'], [2, 'cholesterol'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin c'], [2, 'vitamin a'], [2, 'fiber'], [2, 'percentage calculation carbo'], [2, 'percentage calculation fat'], [2, 'percentage calculation protein'], [2, 'calories'], [3, 'quantity id'], [3, 'recipe id'], [3, 'ingredient id'], [3, 'maximum quantity'], [3, 'minimum quantity'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text'], 'primary_keys': [1, 5, 16, 32], 'foreign_keys': [[16, 5], [33, 16], [34, 1], [33, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM Recipe AS T1 INNER JOIN Quantity AS T2 ON T1.recipe_id = T2.recipe_id WHERE T1.title = 'Apricot Yogurt Parfaits'"}],"db_id":"cookbook"}851{"prompt":"What is the average score for the movie Versailles Rive-Gauche?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average score for the movie Versailles Rive-Gauche?\n        \n        ### Context:\n        #### Query Context:\n        Versailles Rive-Gauche' is movie_title; average score refers to Avg(rating_score);\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.rating_score) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_title LIKE 'Versailles Rive-Gauche'"}],"db_id":"movie_platform"}852{"prompt":"What is the level of the course with the most number of teachers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the level of the course with the most number of teachers?\n        \n        ### Context:\n        #### Query Context:\n        level of the course refers to courseLevel; course with most number of teachers refers to course_id = max(count(taughtBy.p_id))\n        \n        #### Database Schema:\n        {'db_id': 'computer_student', 'table_names_original': ['course', 'person', 'advisedBy', 'taughtBy'], 'table_names': ['course', 'person', 'advisedBy', 'taughtBy'], 'column_names_original': [[-1, '*'], [0, 'course_id'], [0, 'courseLevel'], [1, 'p_id'], [1, 'professor'], [1, 'student'], [1, 'hasPosition'], [1, 'inPhase'], [1, 'yearsInProgram'], [2, 'p_id'], [2, 'p_id_dummy'], [3, 'course_id'], [3, 'p_id']], 'column_names': [[-1, '*'], [0, 'course id'], [0, 'course level'], [1, 'person ID'], [1, 'professor'], [1, 'student'], [1, 'has position'], [1, 'in phase'], [1, 'years in program'], [2, 'person id'], [2, 'person id dummy'], [3, 'course ID'], [3, 'person ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [9, 10], [11, 12]], 'foreign_keys': [[9, 3], [10, 3], [11, 1], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.courseLevel FROM course AS T1 INNER JOIN taughtBy AS T2 ON T1.course_id = T2.course_id GROUP BY T2.course_id ORDER BY COUNT(T2.p_id) DESC LIMIT 1"}],"db_id":"computer_student"}853{"prompt":"What is the average ratio between female and male actors in a movie?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average ratio between female and male actors in a movie?\n        \n        ### Context:\n        #### Query Context:\n        female refers to gender = 'Female'\uff1bmale refers to gender = 'Male'; average ratio = divide(divide(sum(gender_id) when gender = 'Female', sum(gender_id) when gender = 'Male'), count(movie_id)) as percentage\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(COUNT(CASE WHEN T2.gender = 'Female' THEN T1.person_id ELSE NULL END) AS REAL) \/ COUNT(CASE WHEN T2.gender = 'Male' THEN T1.person_id ELSE NULL END) FROM movie_cast AS T1 INNER JOIN gender AS T2 ON T1.gender_id = T2.gender_id"}],"db_id":"movies_4"}854{"prompt":"Please list the full names of any three countries that have their series code with a description of UN Energy Statistics (2014).","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the full names of any three countries that have their series code with a description of UN Energy Statistics (2014).\n        \n        ### Context:\n        #### Query Context:\n        full name refers to longname\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.LongName FROM CountryNotes AS T1 INNER JOIN Country AS T2 ON T1.Countrycode = T2.CountryCode WHERE T1.Description = 'Sources: UN Energy Statistics (2014)' LIMIT 3"}],"db_id":"world_development_indicators"}855{"prompt":"What are the official languages used in Belgium?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What are the official languages used in Belgium?\n        \n        ### Context:\n        #### Query Context:\n        official languages refers to IsOfficial = 'T'; Belgium is a name of country;\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Language FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode WHERE T1.Name = 'Belgium' AND T2.IsOfficial = 'T'"}],"db_id":"world"}856{"prompt":"For the Yelp business in \"Tempe\" city which got \"3.5\" stars and review count as \"Uber\", how many \"long\" reviews did it get?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For the Yelp business in \"Tempe\" city which got \"3.5\" stars and review count as \"Uber\", how many \"long\" reviews did it get?\n        \n        ### Context:\n        #### Query Context:\n        \"Tempe\" is the name of city; long review refers to review_length = 'Long'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.review_length) FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id WHERE T1.city = 'Tempe' AND T1.stars = '3.5' AND T1.review_count = 'Uber' AND T2.review_length = 'Long'"}],"db_id":"public_review_platform"}857{"prompt":"Describe the ingredients in the recipe with the highest vitamin that helps vision in dim light.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Describe the ingredients in the recipe with the highest vitamin that helps vision in dim light.\n        \n        ### Context:\n        #### Query Context:\n        the highest vitamin that helps vision in dim light refers to MAX(vitamin_a)\n        \n        #### Database Schema:\n        {'db_id': 'cookbook', 'table_names_original': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'table_names': ['Ingredient', 'Recipe', 'Nutrition', 'Quantity'], 'column_names_original': [[-1, '*'], [0, 'ingredient_id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe_id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield_unit'], [1, 'prep_min'], [1, 'cook_min'], [1, 'stnd_min'], [1, 'source'], [1, 'intro'], [1, 'directions'], [2, 'recipe_id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total_fat'], [2, 'sat_fat'], [2, 'cholestrl'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin_c'], [2, 'vitamin_a'], [2, 'fiber'], [2, 'pcnt_cal_carb'], [2, 'pcnt_cal_fat'], [2, 'pcnt_cal_prot'], [2, 'calories'], [3, 'quantity_id'], [3, 'recipe_id'], [3, 'ingredient_id'], [3, 'max_qty'], [3, 'min_qty'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_names': [[-1, '*'], [0, 'ingredient id'], [0, 'category'], [0, 'name'], [0, 'plural'], [1, 'recipe id'], [1, 'title'], [1, 'subtitle'], [1, 'servings'], [1, 'yield unit'], [1, 'preparation minute'], [1, 'cooked minute'], [1, 'stand minute'], [1, 'source'], [1, 'introduction'], [1, 'directions'], [2, 'recipe id'], [2, 'protein'], [2, 'carbo'], [2, 'alcohol'], [2, 'total fat'], [2, 'saturated fat'], [2, 'cholesterol'], [2, 'sodium'], [2, 'iron'], [2, 'vitamin c'], [2, 'vitamin a'], [2, 'fiber'], [2, 'percentage calculation carbo'], [2, 'percentage calculation fat'], [2, 'percentage calculation protein'], [2, 'calories'], [3, 'quantity id'], [3, 'recipe id'], [3, 'ingredient id'], [3, 'maximum quantity'], [3, 'minimum quantity'], [3, 'unit'], [3, 'preparation'], [3, 'optional']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text'], 'primary_keys': [1, 5, 16, 32], 'foreign_keys': [[16, 5], [33, 16], [34, 1], [33, 5]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.name FROM Ingredient AS T1 INNER JOIN Quantity AS T2 ON T1.ingredient_id = T2.ingredient_id INNER JOIN Nutrition AS T3 ON T3.recipe_id = T2.recipe_id ORDER BY T3.vitamin_a DESC LIMIT 1"}],"db_id":"cookbook"}858{"prompt":"In which streets of the city of San Francisco are there restaurants that serve seafood?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In which streets of the city of San Francisco are there restaurants that serve seafood?\n        \n        ### Context:\n        #### Query Context:\n        street refers to street_name; seafood refers to food_type = 'seafood'\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.street_name FROM location AS T1 INNER JOIN generalinfo AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T1.city = 'san francisco' AND T2.food_type = 'seafood' AND street_name IS NOT NULL"}],"db_id":"restaurant"}859{"prompt":"Calculate the average age of the competitors who participated in the 1924 Winter.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average age of the competitors who participated in the 1924 Winter.\n        \n        ### Context:\n        #### Query Context:\n        AVG(age) where games_name = '1924 Winter';\n        \n        #### Database Schema:\n        {'db_id': 'olympics', 'table_names_original': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'table_names': ['city', 'games', 'games_city', 'medal', 'noc_region', 'person', 'games_competitor', 'person_region', 'sport', 'event', 'competitor_event'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'city_name'], [1, 'id'], [1, 'games_year'], [1, 'games_name'], [1, 'season'], [2, 'games_id'], [2, 'city_id'], [3, 'id'], [3, 'medal_name'], [4, 'id'], [4, 'noc'], [4, 'region_name'], [5, 'id'], [5, 'full_name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games_id'], [6, 'person_id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport_name'], [9, 'id'], [9, 'sport_id'], [9, 'event_name'], [10, 'event_id'], [10, 'competitor_id'], [10, 'medal_id']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'city name'], [1, 'id'], [1, 'games year'], [1, 'games name'], [1, 'season'], [2, 'games id'], [2, 'city id'], [3, 'id'], [3, 'medal name'], [4, 'id'], [4, 'noc'], [4, 'region name'], [5, 'id'], [5, 'full name'], [5, 'gender'], [5, 'height'], [5, 'weight'], [6, 'id'], [6, 'games id'], [6, 'person id'], [6, 'age'], [7, 'person_id'], [7, 'region_id'], [8, 'id'], [8, 'sport name'], [9, 'id'], [9, 'sport id'], [9, 'event name'], [10, 'event id'], [10, 'competitor id'], [10, 'medal id']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 9, 11, 14, 19, 25, 27], 'foreign_keys': [[7, 3], [8, 1], [21, 14], [20, 3], [24, 11], [23, 14], [28, 25], [32, 9], [30, 27], [31, 19]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.age) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.games_name = '1924 Winter'"}],"db_id":"olympics"}860{"prompt":"How many books were published in Japanese?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many books were published in Japanese?\n        \n        ### Context:\n        #### Query Context:\n        published in Japanese refers to language_name = 'Japanese'\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.book_id) FROM book_language AS T1 INNER JOIN book AS T2 ON T1.language_id = T2.language_id WHERE T1.language_name = 'Japanese'"}],"db_id":"books"}861{"prompt":"Does Yelp business No.\"11825\" have a \"parking lot\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Does Yelp business No.\"11825\" have a \"parking lot\"?\n        \n        ### Context:\n        #### Query Context:\n        business No.\"11825\" refers to business_id = '12476'; have a \"parking lot\" refers to attribute_value = 'parking_lot'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.attribute_value FROM Business_Attributes AS T1 INNER JOIN Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T1.business_id = 11825 AND T2.attribute_name = 'parking_lot'"}],"db_id":"public_review_platform"}862{"prompt":"Among all the crimes that had happened in Central Chicago, how many of them were cases of domestic violence?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the crimes that had happened in Central Chicago, how many of them were cases of domestic violence?\n        \n        ### Context:\n        #### Query Context:\n        Central Chicago refers to district_name = 'Central'; case of domestic violence refers to domestic = 'TRUE'\n        \n        #### Database Schema:\n        {'db_id': 'chicago_crime', 'table_names_original': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'table_names': ['Community_Area', 'District', 'FBI_Code', 'IUCR', 'Neighborhood', 'Ward', 'Crime'], 'column_names_original': [[-1, '*'], [0, 'community_area_no'], [0, 'community_area_name'], [0, 'side'], [0, 'population'], [1, 'district_no'], [1, 'district_name'], [1, 'address'], [1, 'zip_code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi_code_no'], [2, 'title'], [2, 'description'], [2, 'crime_against'], [3, 'iucr_no'], [3, 'primary_description'], [3, 'secondary_description'], [3, 'index_code'], [4, 'neighborhood_name'], [4, 'community_area_no'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report_no'], [6, 'case_number'], [6, 'date'], [6, 'block'], [6, 'iucr_no'], [6, 'location_description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district_no'], [6, 'ward_no'], [6, 'community_area_no'], [6, 'fbi_code_no'], [6, 'latitude'], [6, 'longitude']], 'column_names': [[-1, '*'], [0, 'community area number'], [0, 'community area name'], [0, 'side'], [0, 'population'], [1, 'district number'], [1, 'district name'], [1, 'address'], [1, 'zip code'], [1, 'commander'], [1, 'email'], [1, 'phone'], [1, 'fax'], [1, 'tty'], [1, 'twitter'], [2, 'fbi code number'], [2, 'title'], [2, 'description'], [2, 'crime against'], [3, 'iucr number'], [3, 'primary description'], [3, 'secondary description'], [3, 'index code'], [4, 'neighborhood name'], [4, 'community area number'], [5, 'ward_no'], [5, 'alderman_first_name'], [5, 'alderman_last_name'], [5, 'alderman_name_suffix'], [5, 'ward_office_address'], [5, 'ward_office_zip'], [5, 'ward_email'], [5, 'ward_office_phone'], [5, 'ward_office_fax'], [5, 'city_hall_office_room'], [5, 'city_hall_office_phone'], [5, 'city_hall_office_fax'], [5, 'Population'], [6, 'report number'], [6, 'case number'], [6, 'date'], [6, 'block'], [6, 'Illinois Uniform Crime Reporting number'], [6, 'location description'], [6, 'arrest'], [6, 'domestic'], [6, 'beat'], [6, 'district number'], [6, 'ward number'], [6, 'community area number'], [6, 'fbi code number'], [6, 'latitude'], [6, 'longitude']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 5, 15, 19, 23, 25, 38], 'foreign_keys': [[24, 1], [50, 15], [49, 1], [47, 5], [42, 19], [48, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(*) FROM Crime AS T1 INNER JOIN District AS T2 ON T1.district_no = T2.district_no WHERE T2.district_name = 'Central' AND T1.domestic = 'TRUE'"}],"db_id":"chicago_crime"}863{"prompt":"What product has the fewest online orders from one customer? List the product's class, line of business, and list price.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What product has the fewest online orders from one customer? List the product's class, line of business, and list price.\n        \n        ### Context:\n        #### Query Context:\n        fewest online orders refer to MIN(Quantity);\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 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'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Class, T2.ProductLine, T2.ListPrice FROM ShoppingCartItem AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID GROUP BY T1.ProductID ORDER BY SUM(Quantity) LIMIT 1"}],"db_id":"works_cycles"}864{"prompt":"Which actor has acted in at least 2 French films? Please list their IDs.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which actor has acted in at least 2 French films? Please list their IDs.\n        \n        ### Context:\n        #### Query Context:\n        France is a country\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.actorid FROM movies AS T1 INNER JOIN movies2actors AS T2 ON T1.movieid = T2.movieid WHERE T1.country = 'France' GROUP BY T2.actorid HAVING COUNT(T1.movieid) > 2"}],"db_id":"movielens"}865{"prompt":"What is the average winning margin of the matches held in Newlands?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average winning margin of the matches held in Newlands?\n        \n        ### Context:\n        #### Query Context:\n        average winning margin refers to avg(win_margin); held in Newlands refers to venue_name = 'Newlands'\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T1.win_margin) FROM Match AS T1 INNER JOIN Venue AS T2 ON T1.venue_id = T2.venue_id WHERE T2.venue_name = 'Newlands'"}],"db_id":"soccer_2016"}866{"prompt":"Give the country where St. George's Park is located.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the country where St. George's Park is located.\n        \n        ### Context:\n        #### Query Context:\n        country refers to Country_Name; St. George's Park refers to Venue_Name = 'St George''s Park'\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.Country_Name FROM Venue AS T1 INNER JOIN City AS T2 ON T2.City_Id = T1.City_Id INNER JOIN Country AS T3 ON T3.Country_Id = T2.Country_id WHERE T1.Venue_Name = 'St George''s Park'"}],"db_id":"soccer_2016"}867{"prompt":"Tell the path of the solution for the method \"ExportToRTF.RTFStyleSheet.H6Write\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tell the path of the solution for the method \"ExportToRTF.RTFStyleSheet.H6Write\".\n        \n        ### Context:\n        #### Query Context:\n        path of the solution refers to Path; method refers to Name; Name = 'ExportToRTF.RTFStyleSheet.H6Write';\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Path FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.Name = 'ExportToRTF.RTFStyleSheet.H6Write'"}],"db_id":"codebase_comments"}868{"prompt":"What percentage of podcasts are \"technology\" podcasts? List all of them.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What percentage of podcasts are \"technology\" podcasts? List all of them.\n        \n        ### Context:\n        #### Query Context:\n        \"technology\" podcast refers to category = 'technology'; percentage = Divide (Count (podcast_id (category = 'technology')), Count (podcast_id)) * 100\n        \n        #### Database Schema:\n        {'db_id': 'music_platform_2', 'table_names_original': ['runs', 'podcasts', 'reviews', 'categories'], 'table_names': ['runs', 'podcasts', 'reviews', 'categories'], 'column_names_original': [[-1, '*'], [0, 'run_at'], [0, 'max_rowid'], [0, 'reviews_added'], [1, 'podcast_id'], [1, 'itunes_id'], [1, 'slug'], [1, 'itunes_url'], [1, 'title'], [2, 'podcast_id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author_id'], [2, 'created_at'], [3, 'podcast_id'], [3, 'category']], 'column_names': [[-1, '*'], [0, 'run at'], [0, 'max row id'], [0, 'reviews added'], [1, 'podcast id'], [1, 'itunes id'], [1, 'slug'], [1, 'itunes url'], [1, 'title'], [2, 'podcast id'], [2, 'title'], [2, 'content'], [2, 'rating'], [2, 'author id'], [2, 'created at'], [3, 'podcast id'], [3, 'category']], 'column_types': ['text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [4, [15, 16]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T1.category = 'technology' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.title) OR '%' \"percentage\" FROM categories AS T1 INNER JOIN podcasts AS T2 ON T2.podcast_id = T1.podcast_id"}],"db_id":"music_platform_2"}869{"prompt":"How much money on average does Lucas Wyldbore spend on book orders?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much money on average does Lucas Wyldbore spend on book orders?\n        \n        ### Context:\n        #### Query Context:\n        average spend on book orders = AVG (price)\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(T1.price) \/ COUNT(*) FROM order_line AS T1 INNER JOIN cust_order AS T2 ON T2.order_id = T1.order_id INNER JOIN customer AS T3 ON T3.customer_id = T2.customer_id WHERE T3.first_name = 'Lucas' AND T3.last_name = 'Wyldbore'"}],"db_id":"books"}870{"prompt":"How many male patients have been described as immune to quadrivalent HPV?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many male patients have been described as immune to quadrivalent HPV?\n        \n        ### Context:\n        #### Query Context:\n        male refers to gender = 'M'; immune to quadrivalent HPV refers to immunizations.DESCRIPTION = 'HPV quadrivalent';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.patient) FROM patients AS T1 INNER JOIN immunizations AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'HPV  quadrivalent' AND T1.gender = 'M'"}],"db_id":"synthea"}871{"prompt":"What is the title of the first book that was written by A.J. Ayer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the title of the first book that was written by A.J. Ayer?\n        \n        ### Context:\n        #### Query Context:\n        \"A.J. Ayer\" is the author_name; first book refers to Min(publication_date)\n        \n        #### Database Schema:\n        {'db_id': 'books', 'table_names_original': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'table_names': ['address_status', 'author', 'book_language', 'country', 'address', 'customer', 'customer_address', 'order_status', 'publisher', 'book', 'book_author', 'shipping_method', 'cust_order', 'order_history', 'order_line'], 'column_names_original': [[-1, '*'], [0, 'status_id'], [0, 'address_status'], [1, 'author_id'], [1, 'author_name'], [2, 'language_id'], [2, 'language_code'], [2, 'language_name'], [3, 'country_id'], [3, 'country_name'], [4, 'address_id'], [4, 'street_number'], [4, 'street_name'], [4, 'city'], [4, 'country_id'], [5, 'customer_id'], [5, 'first_name'], [5, 'last_name'], [5, 'email'], [6, 'customer_id'], [6, 'address_id'], [6, 'status_id'], [7, 'status_id'], [7, 'status_value'], [8, 'publisher_id'], [8, 'publisher_name'], [9, 'book_id'], [9, 'title'], [9, 'isbn13'], [9, 'language_id'], [9, 'num_pages'], [9, 'publication_date'], [9, 'publisher_id'], [10, 'book_id'], [10, 'author_id'], [11, 'method_id'], [11, 'method_name'], [11, 'cost'], [12, 'order_id'], [12, 'order_date'], [12, 'customer_id'], [12, 'shipping_method_id'], [12, 'dest_address_id'], [13, 'history_id'], [13, 'order_id'], [13, 'status_id'], [13, 'status_date'], [14, 'line_id'], [14, 'order_id'], [14, 'book_id'], [14, 'price']], 'column_names': [[-1, '*'], [0, 'status id'], [0, 'address status'], [1, 'author id'], [1, 'author name'], [2, 'language id'], [2, 'language code'], [2, 'language name'], [3, 'country id'], [3, 'country name'], [4, 'address id'], [4, 'street number'], [4, 'street name'], [4, 'city'], [4, 'country id'], [5, 'customer id'], [5, 'first name'], [5, 'last name'], [5, 'email'], [6, 'customer id'], [6, 'address id'], [6, 'status id'], [7, 'status id'], [7, 'status value'], [8, 'publisher id'], [8, 'publisher name'], [9, 'book id'], [9, 'title'], [9, 'isbn13'], [9, 'language id'], [9, 'number pages'], [9, 'publication date'], [9, 'publisher_id'], [10, 'book id'], [10, 'author id'], [11, 'method id'], [11, 'method name'], [11, 'cost'], [12, 'order id'], [12, 'order date'], [12, 'customer id'], [12, 'shipping method id'], [12, 'destination address id'], [13, 'history id'], [13, 'order_id'], [13, 'status id'], [13, 'status date'], [14, 'line id'], [14, 'order id'], [14, 'book id'], [14, 'price']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'real', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 5, 8, 10, 15, [19, 20], 22, 24, 26, [33, 34], 35, 38, 43, 47], 'foreign_keys': [[14, 8], [19, 15], [20, 10], [32, 24], [29, 5], [33, 26], [34, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.title FROM book AS T1 INNER JOIN book_author AS T2 ON T1.book_id = T2.book_id INNER JOIN author AS T3 ON T3.author_id = T2.author_id WHERE T3.author_name = 'A.J. Ayer' ORDER BY T1.publication_date ASC LIMIT 1"}],"db_id":"books"}872{"prompt":"What is the average discount for the parts made by Manufacturer#5?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the average discount for the parts made by Manufacturer#5?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(SUM(l_discount), COUNT(l_partkey)) where p_mfgr = 'Manufacturer#5';\n        \n        #### Database Schema:\n        {'db_id': 'retails', 'table_names_original': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'table_names': ['customer', 'lineitem', 'nation', 'orders', 'part', 'partsupp', 'region', 'supplier'], 'column_names_original': [[-1, '*'], [0, 'c_custkey'], [0, 'c_mktsegment'], [0, 'c_nationkey'], [0, 'c_name'], [0, 'c_address'], [0, 'c_phone'], [0, 'c_acctbal'], [0, 'c_comment'], [1, 'l_shipdate'], [1, 'l_orderkey'], [1, 'l_discount'], [1, 'l_extendedprice'], [1, 'l_suppkey'], [1, 'l_quantity'], [1, 'l_returnflag'], [1, 'l_partkey'], [1, 'l_linestatus'], [1, 'l_tax'], [1, 'l_commitdate'], [1, 'l_receiptdate'], [1, 'l_shipmode'], [1, 'l_linenumber'], [1, 'l_shipinstruct'], [1, 'l_comment'], [2, 'n_nationkey'], [2, 'n_name'], [2, 'n_regionkey'], [2, 'n_comment'], [3, 'o_orderdate'], [3, 'o_orderkey'], [3, 'o_custkey'], [3, 'o_orderpriority'], [3, 'o_shippriority'], [3, 'o_clerk'], [3, 'o_orderstatus'], [3, 'o_totalprice'], [3, 'o_comment'], [4, 'p_partkey'], [4, 'p_type'], [4, 'p_size'], [4, 'p_brand'], [4, 'p_name'], [4, 'p_container'], [4, 'p_mfgr'], [4, 'p_retailprice'], [4, 'p_comment'], [5, 'ps_partkey'], [5, 'ps_suppkey'], [5, 'ps_supplycost'], [5, 'ps_availqty'], [5, 'ps_comment'], [6, 'r_regionkey'], [6, 'r_name'], [6, 'r_comment'], [7, 's_suppkey'], [7, 's_nationkey'], [7, 's_comment'], [7, 's_name'], [7, 's_address'], [7, 's_phone'], [7, 's_acctbal']], 'column_names': [[-1, '*'], [0, 'customer key'], [0, 'customer market segment'], [0, 'customer nation key'], [0, 'customer name'], [0, 'customer address'], [0, 'customer phone'], [0, 'customer account balance'], [0, 'customer comment'], [1, 'line item ship date'], [1, 'line item order key'], [1, 'line item discount'], [1, 'line item extended price'], [1, 'line item suppkey'], [1, 'line item quantity'], [1, 'line item return flag'], [1, 'line item part key'], [1, 'line item line status'], [1, 'line item tax'], [1, 'line item commit date'], [1, 'line item receipt date'], [1, 'line item ship mode'], [1, 'line item line number'], [1, 'line item ship instruct'], [1, 'line item comment'], [2, 'nation key'], [2, 'nation name'], [2, 'nation region key'], [2, 'nation comment'], [3, 'order date'], [3, 'order key'], [3, 'order customer key'], [3, 'order priority'], [3, 'order ship priority'], [3, 'order clerk'], [3, 'order status'], [3, 'order total price'], [3, 'order comment'], [4, 'part key'], [4, 'part type'], [4, 'part size'], [4, 'part brand'], [4, 'part name'], [4, 'part container'], [4, 'part manufacturer'], [4, 'part retail price'], [4, 'part comment'], [5, 'part supply part key'], [5, 'part supply supply key'], [5, 'part supply cost'], [5, 'part supply available quantity'], [5, 'part supply comment'], [6, 'region key'], [6, 'region name'], [6, 'region comment'], [7, 'supply key'], [7, 'supplier nation key'], [7, 'supplier comment'], [7, 'supplier name'], [7, 'supplier address'], [7, 'supplier phone'], [7, 'supplier account balance']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'date', 'integer', 'real', 'real', 'integer', 'integer', 'text', 'integer', 'text', 'real', 'date', 'date', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, [10, 22], 25, 30, 38, [47, 48], 52, 55], 'foreign_keys': [[3, 25], [16, 47], [13, 48], [10, 30], [27, 52], [31, 1], [48, 55], [47, 38], [56, 25]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T3.l_discount) FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey INNER JOIN lineitem AS T3 ON T2.ps_suppkey = T3.l_suppkey WHERE T1.p_mfgr = 'Manufacturer#5'"}],"db_id":"retails"}873{"prompt":"Based on the population in 2020, calculate the percentage for the population of Asian in the zip code where the CBSA was Atmore, AL.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Based on the population in 2020, calculate the percentage for the population of Asian in the zip code where the CBSA was Atmore, AL.\n        \n        ### Context:\n        #### Query Context:\n        \"Atmore, AL\" is CBSA_name; percentage = Divide(asian_population, population_2020) * 100\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(T2.asian_population AS REAL) * 100 \/ T2.population_2010 FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Atmore, AL'"}],"db_id":"address"}874{"prompt":"What is the full name of clients who have issue about balance transfer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full name of clients who have issue about balance transfer?\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first, middle, last; issue about balance transfer refers to Issue = 'Balance transfer'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.Issue = 'Balance transfer'"}],"db_id":"retail_complains"}875{"prompt":"What is the long title of the work with the highest number of scenes in act 1?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the long title of the work with the highest number of scenes in act 1?\n        \n        ### Context:\n        #### Query Context:\n        highest number of scenes refers to max(count(Scene))\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.LongTitle FROM chapters AS T1 INNER JOIN works AS T2 ON T1.work_id = T2.id WHERE T1.Act = 1 ORDER BY T1.Scene DESC LIMIT 1"}],"db_id":"shakespeare"}876{"prompt":"List all the customers with name containing the word 'Group'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the customers with name containing the word 'Group'.\n        \n        ### Context:\n        #### Query Context:\n        name containing the word 'Group' refers to Customer Names LIKE '%Group%';\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T FROM ( SELECT IIF(`Customer Names` LIKE '%Group%', `Customer Names`, NULL) AS T FROM Customers ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}877{"prompt":"State the name of the publisher with the most games.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        State the name of the publisher with the most games.\n        \n        ### Context:\n        #### Query Context:\n        name of publisher refers to publisher_name; the most games refers to max(game_id)\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.publisher_name FROM ( SELECT T2.publisher_name, COUNT(DISTINCT T1.game_id) FROM game_publisher AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id GROUP BY T2.publisher_name ORDER BY COUNT(DISTINCT T1.game_id) DESC LIMIT 1 ) t"}],"db_id":"video_games"}878{"prompt":"How many sales team were from Northeast?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many sales team were from Northeast?\n        \n        ### Context:\n        #### Query Context:\n        Northeast is the name of the region;\n        \n        #### Database Schema:\n        {'db_id': 'regional_sales', 'table_names_original': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'table_names': ['Customers', 'Products', 'Regions', 'Sales Team', 'Store Locations', 'Sales Orders'], 'column_names_original': [[-1, '*'], [0, 'CustomerID'], [0, 'Customer Names'], [1, 'ProductID'], [1, 'Product Name'], [2, 'StateCode'], [2, 'State'], [2, 'Region'], [3, 'SalesTeamID'], [3, 'Sales Team'], [3, 'Region'], [4, 'StoreID'], [4, 'City Name'], [4, 'County'], [4, 'StateCode'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'AreaCode'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'OrderNumber'], [5, 'Sales Channel'], [5, 'WarehouseCode'], [5, 'ProcuredDate'], [5, 'OrderDate'], [5, 'ShipDate'], [5, 'DeliveryDate'], [5, 'CurrencyCode'], [5, '_SalesTeamID'], [5, '_CustomerID'], [5, '_StoreID'], [5, '_ProductID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_names': [[-1, '*'], [0, 'customer id'], [0, 'customer names'], [1, 'product id'], [1, 'product name'], [2, 'state code'], [2, 'State'], [2, 'Region'], [3, 'SalesTeam ID'], [3, 'Sales Team'], [3, 'Region'], [4, 'Store ID'], [4, 'City Name'], [4, 'County'], [4, 'State Code'], [4, 'State'], [4, 'Type'], [4, 'Latitude'], [4, 'Longitude'], [4, 'Area Code'], [4, 'Population'], [4, 'Household Income'], [4, 'Median Income'], [4, 'Land Area'], [4, 'Water Area'], [4, 'Time Zone'], [5, 'Order Number'], [5, 'Sales Channel'], [5, 'Warehouse Code'], [5, 'Procured Date'], [5, 'Order Date'], [5, 'Ship Date'], [5, 'Delivery Date'], [5, 'Currency Code'], [5, '_Sales Team ID'], [5, '_Customer ID'], [5, '_Store ID'], [5, '_Product ID'], [5, 'Order Quantity'], [5, 'Discount Applied'], [5, 'Unit Price'], [5, 'Unit Cost']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'text'], 'primary_keys': [1, 3, 5, 8, 11, 26], 'foreign_keys': [[14, 5], [37, 3], [36, 11], [35, 1], [34, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN Region = 'Northeast' THEN 1 ELSE 0 END) FROM `Sales Team`"}],"db_id":"regional_sales"}879{"prompt":"How many customers are there in Berlin, Germany?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many customers are there in Berlin, Germany?\n        \n        ### Context:\n        #### Query Context:\n        \"Berlin\" is the City; 'Germany' is the Country\n        \n        #### Database Schema:\n        {'db_id': 'retail_world', 'table_names_original': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'table_names': ['Categories', 'Customers', 'Employees', 'Shippers', 'Suppliers', 'Products', 'Orders', 'OrderDetails'], 'column_names_original': [[-1, '*'], [0, 'CategoryID'], [0, 'CategoryName'], [0, 'Description'], [1, 'CustomerID'], [1, 'CustomerName'], [1, 'ContactName'], [1, 'Address'], [1, 'City'], [1, 'PostalCode'], [1, 'Country'], [2, 'EmployeeID'], [2, 'LastName'], [2, 'FirstName'], [2, 'BirthDate'], [2, 'Photo'], [2, 'Notes'], [3, 'ShipperID'], [3, 'ShipperName'], [3, 'Phone'], [4, 'SupplierID'], [4, 'SupplierName'], [4, 'ContactName'], [4, 'Address'], [4, 'City'], [4, 'PostalCode'], [4, 'Country'], [4, 'Phone'], [5, 'ProductID'], [5, 'ProductName'], [5, 'SupplierID'], [5, 'CategoryID'], [5, 'Unit'], [5, 'Price'], [6, 'OrderID'], [6, 'CustomerID'], [6, 'EmployeeID'], [6, 'OrderDate'], [6, 'ShipperID'], [7, 'OrderDetailID'], [7, 'OrderID'], [7, 'ProductID'], [7, 'Quantity']], 'column_names': [[-1, '*'], [0, 'Category ID'], [0, 'Category Name'], [0, 'Description'], [1, 'Customer ID'], [1, 'Customer Name'], [1, 'Contact Name'], [1, 'Address'], [1, 'City'], [1, 'Postal Code'], [1, 'Country'], [2, 'Employee ID'], [2, 'Last Name'], [2, 'First Name'], [2, 'Birth Date'], [2, 'Photo'], [2, 'Notes'], [3, 'Shipper ID'], [3, 'Shipper Name'], [3, 'Phone'], [4, 'Supplier ID'], [4, 'Supplier Name'], [4, 'Contact Name'], [4, 'Address'], [4, 'City'], [4, 'Postal Code'], [4, 'Country'], [4, 'Phone'], [5, 'Product ID'], [5, 'Product Name'], [5, 'Supplier ID'], [5, 'Category ID'], [5, 'Unit'], [5, 'Price'], [6, 'Order ID'], [6, 'Customer ID'], [6, 'Employee ID'], [6, 'Order Date'], [6, 'Shipper ID'], [7, 'order detail id'], [7, 'Order ID'], [7, 'Product ID'], [7, 'Quantity']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'text', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'real', 'integer', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 11, 17, 20, 28, 34, 39], 'foreign_keys': [[30, 20], [31, 1], [38, 17], [35, 4], [36, 11], [41, 28], [40, 34]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(City) FROM Customers WHERE Country = 'Germany' AND City = 'Berlin'"}],"db_id":"retail_world"}880{"prompt":"How many more followers in percentage are there for the repository used by solution ID 18 than solution ID19?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many more followers in percentage are there for the repository used by solution ID 18 than solution ID19?\n        \n        ### Context:\n        #### Query Context:\n        followers refers to Forks; percentage = divide(SUBTRACT(Forks(Solution.ID = 18), Forks(Solution.ID = 19)), Forks(Solution.ID = 19))*100%\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST((SUM(CASE WHEN T2.Id = 18 THEN T1.Forks ELSE 0 END) - SUM(CASE WHEN T2.Id = 19 THEN T1.Forks ELSE 0 END)) AS REAL) * 100 \/ SUM(CASE WHEN T2.Id = 19 THEN T1.Forks ELSE 0 END) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId"}],"db_id":"codebase_comments"}881{"prompt":"How many schools in Brooklyn with urban metro and donations for an honoree have requested TT992 - Refill Pack for Safety Name Tags as a resource?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many schools in Brooklyn with urban metro and donations for an honoree have requested TT992 - Refill Pack for Safety Name Tags as a resource?\n        \n        ### Context:\n        #### Query Context:\n        in Brooklyn refers to school_city = 'Brooklyn'; urban metro refers to school_metro = 'urban'; donations for an honoree refers to for_honoree = 't'; requested TT992 - Refill Pack for Safety Name Tags refers to item_name = 'TT992 - Refill Pack for Safety Name Tags'\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.schoolid) FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid INNER JOIN donations AS T3 ON T2.projectid = T3.projectid WHERE T2.school_city = 'Brooklyn' AND T2.school_metro = 'urban' AND T3.for_honoree = 't' AND T1.item_name = 'TT992 - Refill Pack for Safety Name Tags'"}],"db_id":"donor"}882{"prompt":"Give the title and the characters name of the most recent work of Shakespeare.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the title and the characters name of the most recent work of Shakespeare.\n        \n        ### Context:\n        #### Query Context:\n        characters name refers to CharName; most recent work refers to max(Date)\n        \n        #### Database Schema:\n        {'db_id': 'shakespeare', 'table_names_original': ['chapters', 'characters', 'paragraphs', 'works'], 'table_names': ['chapters', 'characters', 'paragraphs', 'works'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work_id'], [1, 'id'], [1, 'CharName'], [1, 'Abbrev'], [1, 'Description'], [2, 'id'], [2, 'ParagraphNum'], [2, 'PlainText'], [2, 'character_id'], [2, 'chapter_id'], [3, 'id'], [3, 'Title'], [3, 'LongTitle'], [3, 'Date'], [3, 'GenreType']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'Act'], [0, 'Scene'], [0, 'Description'], [0, 'work id'], [1, 'id'], [1, 'char name'], [1, 'abbreviation'], [1, 'Description'], [2, 'id'], [2, 'paragraph number'], [2, 'Plain Text'], [2, 'character id'], [2, 'chapter id'], [3, 'id'], [3, 'Title'], [3, 'Long Title'], [3, 'character id'], [3, 'genre type']], 'column_types': ['text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 10, 15], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Title, T4.CharName FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id INNER JOIN paragraphs AS T3 ON T2.id = T3.chapter_id INNER JOIN characters AS T4 ON T3.character_id = T4.id ORDER BY T1.Date DESC LIMIT 1"}],"db_id":"shakespeare"}883{"prompt":"List all of Wolfgang Reitherman's movies and their voice actors.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all of Wolfgang Reitherman's movies and their voice actors.\n        \n        ### Context:\n        #### Query Context:\n        Wolfgang Reitherman refers to director = 'Wolfgang Reitherman';\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.name, T2.`voice-actor` FROM director AS T1 INNER JOIN `voice-actors` AS T2 ON T1.name = T2.movie WHERE T1.director = 'Wolfgang Reitherman'"}],"db_id":"disney"}884{"prompt":"Among the countries with more than 3% population growth rate, state the country name in full along with its GDP.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the countries with more than 3% population growth rate, state the country name in full along with its GDP.\n        \n        ### Context:\n        #### Query Context:\n        Population_growth = 3 means 3% population growth rate\n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name, T3.GDP FROM country AS T1 INNER JOIN population AS T2 ON T1.Code = T2.Country INNER JOIN economy AS T3 ON T3.Country = T2.Country WHERE T2.Population_Growth > 3"}],"db_id":"mondial_geo"}885{"prompt":"How many percent of universities got a score above 80 under International criteria in 2016? Among them, name the university which got the highest score.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many percent of universities got a score above 80 under International criteria in 2016? Among them, name the university which got the highest score.\n        \n        ### Context:\n        #### Query Context:\n        got a score above 80 refers to score > 80; under International criteria refers to criteria_name = 'International'; in 2016 refers to year = 2016; highest score refers to MAX(score)\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.score > 80 THEN 1 ELSE 0 END) AS REAL) \/ COUNT(*), ( SELECT T3.university_name FROM ranking_criteria AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.ranking_criteria_id INNER JOIN university AS T3 ON T3.id = T2.university_id WHERE T1.criteria_name = 'International' AND T2.year = 2016 AND T2.score > 80 ORDER BY T2.score DESC LIMIT 1 ) AS max FROM ranking_criteria AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.ranking_criteria_id INNER JOIN university AS T3 ON T3.id = T2.university_id WHERE T1.criteria_name = 'International' AND T2.year = 2016"}],"db_id":"university"}886{"prompt":"How many restricted horror movies were released between 1\/1\/1990 to 12\/31\/2015?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many restricted horror movies were released between 1\/1\/1990 to 12\/31\/2015?\n        \n        ### Context:\n        #### Query Context:\n        Restricted refers to MPAA_rating = 'R'; horror refers to genre = 'Horror'; released between 1\/1\/1990 to 12\/31\/2015 refers to (cast(SUBSTR(release_date, instr(release_date, ', ') + 1) as int) between 1990 and 2015);\n        \n        #### Database Schema:\n        {'db_id': 'disney', 'table_names_original': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'table_names': ['characters', 'director', 'movies_total_gross', 'revenue', 'voice-actors'], 'column_names_original': [[-1, '*'], [0, 'movie_title'], [0, 'release_date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie_title'], [2, 'release_date'], [2, 'genre'], [2, 'MPAA_rating'], [2, 'total_gross'], [2, 'inflation_adjusted_gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_names': [[-1, '*'], [0, 'movie title'], [0, 'release date'], [0, 'hero'], [0, 'villian'], [0, 'song'], [1, 'name'], [1, 'director'], [2, 'movie title'], [2, 'release date'], [2, 'genre'], [2, 'Motion Picture Association of America rating'], [2, 'total gross'], [2, 'inflation adjusted gross'], [3, 'Year'], [3, 'Studio Entertainment[NI 1]'], [3, 'Disney Consumer Products[NI 2]'], [3, 'Disney Interactive[NI 3][Rev 1]'], [3, 'Walt Disney Parks and Resorts'], [3, 'Disney Media Networks'], [3, 'Total'], [4, 'character'], [4, 'voice-actor'], [4, 'movie']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'real', 'text', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 6, [8, 9], 14, 21], 'foreign_keys': [[3, 21], [6, 1], [8, 1], [23, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(movie_title) FROM movies_total_gross WHERE MPAA_rating = 'R' AND genre = 'Horror' AND CAST(SUBSTR(release_date, INSTR(release_date, ', ') + 1) AS int) BETWEEN 1990 AND 2015"}],"db_id":"disney"}887{"prompt":"Indicate the location of all the events that occurred on April 30, 2016.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Indicate the location of all the events that occurred on April 30, 2016.\n        \n        ### Context:\n        #### Query Context:\n        location = longitude, latitude; on April 30, 2016 refers timestamp BETWEEN '2016-04-30 00:00:00' AND '2016-04-30 23:59:59';\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT longitude, latitude FROM events WHERE date(timestamp) = '2016-04-30'"}],"db_id":"talkingdata"}888{"prompt":"Please list the full names of all the sanitarians under the supervision of Darlisha Jacobs.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the full names of all the sanitarians under the supervision of Darlisha Jacobs.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name\n        \n        #### Database Schema:\n        {'db_id': 'food_inspection_2', 'table_names_original': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'table_names': ['employee', 'establishment', 'inspection', 'inspection_point', 'violation'], 'column_names_original': [[-1, '*'], [0, 'employee_id'], [0, 'first_name'], [0, 'last_name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license_no'], [1, 'dba_name'], [1, 'aka_name'], [1, 'facility_type'], [1, 'risk_level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection_id'], [2, 'inspection_date'], [2, 'inspection_type'], [2, 'results'], [2, 'employee_id'], [2, 'license_no'], [2, 'followup_to'], [3, 'point_id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection_id'], [4, 'point_id'], [4, 'fine'], [4, 'inspector_comment']], 'column_names': [[-1, '*'], [0, 'employee id'], [0, 'first name'], [0, 'last name'], [0, 'address'], [0, 'city'], [0, 'state'], [0, 'zip'], [0, 'phone'], [0, 'title'], [0, 'salary'], [0, 'supervisor'], [1, 'license number'], [1, 'doing business as name'], [1, 'as know as name'], [1, 'facility type'], [1, 'risk level'], [1, 'address'], [1, 'city'], [1, 'state'], [1, 'zip'], [1, 'latitude'], [1, 'longitude'], [1, 'ward'], [2, 'inspection id'], [2, 'inspection date'], [2, 'inspection type'], [2, 'results'], [2, 'employee id'], [2, 'license number'], [2, 'followup to'], [3, 'point id'], [3, 'Description'], [3, 'category'], [3, 'code'], [3, 'fine'], [3, 'point_level'], [4, 'inspection id'], [4, 'point id'], [4, 'fine'], [4, 'inspector_comment']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'real', 'real', 'integer', 'integer', 'date', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'text'], 'primary_keys': [1, 12, 24, 31, [37, 38]], 'foreign_keys': [[11, 1], [30, 24], [29, 12], [28, 1], [38, 31], [37, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT first_name, last_name FROM employee WHERE title = 'Sanitarian' AND supervisor = ( SELECT employee_id FROM employee WHERE first_name = 'Darlisha' AND last_name = 'Jacobs' )"}],"db_id":"food_inspection_2"}889{"prompt":"How many of the complaints are longer than 15 minutes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the complaints are longer than 15 minutes?\n        \n        ### Context:\n        #### Query Context:\n        longer than 15 minutes refers to ser_time > '00:15:00'\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(ser_time) FROM callcenterlogs WHERE strftime('%M', ser_time) > '15'"}],"db_id":"retail_complains"}890{"prompt":"Give the religion of the legislator whose YouTube name is MaxineWaters.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the religion of the legislator whose YouTube name is MaxineWaters.\n        \n        ### Context:\n        #### Query Context:\n        MaxineWaters relates to youtube\n        \n        #### Database Schema:\n        {'db_id': 'legislator', 'table_names_original': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'table_names': ['current', 'current-terms', 'historical', 'historical-terms', 'social-media'], 'column_names_original': [[-1, '*'], [0, 'ballotpedia_id'], [0, 'bioguide_id'], [0, 'birthday_bio'], [0, 'cspan_id'], [0, 'fec_id'], [0, 'first_name'], [0, 'gender_bio'], [0, 'google_entity_id_id'], [0, 'govtrack_id'], [0, 'house_history_id'], [0, 'icpsr_id'], [0, 'last_name'], [0, 'lis_id'], [0, 'maplight_id'], [0, 'middle_name'], [0, 'nickname_name'], [0, 'official_full_name'], [0, 'opensecrets_id'], [0, 'religion_bio'], [0, 'suffix_name'], [0, 'thomas_id'], [0, 'votesmart_id'], [0, 'wikidata_id'], [0, 'wikipedia_id'], [1, 'address'], [1, 'bioguide'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party_affiliations'], [1, 'phone'], [1, 'relation'], [1, 'rss_url'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia_id'], [2, 'bioguide_id'], [2, 'bioguide_previous_id'], [2, 'birthday_bio'], [2, 'cspan_id'], [2, 'fec_id'], [2, 'first_name'], [2, 'gender_bio'], [2, 'google_entity_id_id'], [2, 'govtrack_id'], [2, 'house_history_alternate_id'], [2, 'house_history_id'], [2, 'icpsr_id'], [2, 'last_name'], [2, 'lis_id'], [2, 'maplight_id'], [2, 'middle_name'], [2, 'nickname_name'], [2, 'official_full_name'], [2, 'opensecrets_id'], [2, 'religion_bio'], [2, 'suffix_name'], [2, 'thomas_id'], [2, 'votesmart_id'], [2, 'wikidata_id'], [2, 'wikipedia_id'], [3, 'address'], [3, 'bioguide'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party_affiliations'], [3, 'phone'], [3, 'relation'], [3, 'rss_url'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_names': [[-1, '*'], [0, 'ballotpedia id'], [0, 'bioguide id'], [0, 'birthday bio'], [0, 'cspan id'], [0, 'fec id'], [0, 'first name'], [0, 'gender bio'], [0, 'google entity id'], [0, 'govtrack id'], [0, 'house history id'], [0, 'interuniversity consortium for political and social research id'], [0, 'last name'], [0, 'legislator id'], [0, 'maplight id'], [0, 'middle name'], [0, 'nickname'], [0, 'official full name'], [0, 'opensecrets id'], [0, 'religion bio'], [0, 'suffix name'], [0, 'thomas id'], [0, 'votesmart id'], [0, 'wikidata id'], [0, 'wikipedia id'], [1, 'address'], [1, 'bioguide id'], [1, 'caucus'], [1, 'chamber'], [1, 'class'], [1, 'contact_form'], [1, 'district'], [1, 'end'], [1, 'fax'], [1, 'last'], [1, 'name'], [1, 'office'], [1, 'party'], [1, 'party affiliations'], [1, 'phone'], [1, 'relation'], [1, 'Really Simple Syndication URL'], [1, 'start'], [1, 'state'], [1, 'state_rank'], [1, 'title'], [1, 'type'], [1, 'url'], [2, 'ballotpedia id'], [2, 'bioguide id'], [2, 'bioguide previous id'], [2, 'birthday bio'], [2, 'cspan id'], [2, 'fec id'], [2, 'first name'], [2, 'gender bio'], [2, 'google entity id'], [2, 'govtrack id'], [2, 'house history alternate id'], [2, 'house history id'], [2, 'interuniversity consortium for political and social research id'], [2, 'last name'], [2, 'legislator id'], [2, 'maplight id'], [2, 'middle name'], [2, 'nickname'], [2, 'official full name'], [2, 'opensecrets id'], [2, 'religion bio'], [2, 'suffix name'], [2, 'thomas id'], [2, 'votesmart id'], [2, 'wikidata id'], [2, 'wikipedia id'], [3, 'address'], [3, 'bioguide id'], [3, 'chamber'], [3, 'class'], [3, 'contact_form'], [3, 'district'], [3, 'end'], [3, 'fax'], [3, 'last'], [3, 'middle'], [3, 'name'], [3, 'office'], [3, 'party'], [3, 'party affiliations'], [3, 'phone'], [3, 'relation'], [3, 'Really Simple Syndication URL'], [3, 'start'], [3, 'state'], [3, 'state_rank'], [3, 'title'], [3, 'type'], [3, 'url'], [4, 'bioguide'], [4, 'facebook'], [4, 'facebook_id'], [4, 'govtrack'], [4, 'instagram'], [4, 'instagram_id'], [4, 'thomas'], [4, 'twitter'], [4, 'twitter_id'], [4, 'youtube'], [4, 'youtube_id']], 'column_types': ['text', 'text', 'text', 'date', 'real', 'text', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'integer', 'text', 'real', 'text', 'text'], 'primary_keys': [[2, 4], [26, 32], 49, 75, 97], 'foreign_keys': [[26, 2], [75, 49], [97, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.religion_bio FROM `social-media` AS T1 INNER JOIN current AS T2 ON T1.bioguide = T2.bioguide_id WHERE T1.youtube = 'MaxineWaters'"}],"db_id":"legislator"}891{"prompt":"In 1975, what was the average point of all-star players coming from the USA?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        In 1975, what was the average point of all-star players coming from the USA?\n        \n        ### Context:\n        #### Query Context:\n        in 1975 refers to season_id = 1975; coming from USA refers to birthCountry = 'USA'; average point refers to Divide (Sum(points), Count(playerID))\n        \n        #### Database Schema:\n        {'db_id': 'professional_basketball', 'table_names_original': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'table_names': ['awards_players', 'coaches', 'draft', 'player_allstar', 'players', 'teams', 'awards_coaches', 'players_teams', 'series_post'], 'column_names_original': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [0, 'pos'], [1, 'coachID'], [1, 'year'], [1, 'tmID'], [1, 'lgID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post_wins'], [1, 'post_losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'draftSelection'], [2, 'draftOverall'], [2, 'tmID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'lgID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league_id'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'o_rebounds'], [3, 'd_rebounds'], [3, 'rebounds'], [3, 'assists'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'fg_attempted'], [3, 'fg_made'], [3, 'ft_attempted'], [3, 'ft_made'], [3, 'three_attempted'], [3, 'three_made'], [4, 'playerID'], [4, 'useFirst'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nameNick'], [4, 'pos'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'hsCity'], [4, 'hsState'], [4, 'hsCountry'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'lgID'], [5, 'tmID'], [5, 'franchID'], [5, 'confID'], [5, 'divID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'o_fgm'], [5, 'o_ftm'], [5, 'o_pts'], [5, 'd_pts'], [5, 'homeWon'], [5, 'homeLost'], [5, 'awayWon'], [5, 'awayLost'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'lgID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'tmID'], [7, 'lgID'], [7, 'GP'], [7, 'GS'], [7, 'minutes'], [7, 'points'], [7, 'oRebounds'], [7, 'dRebounds'], [7, 'rebounds'], [7, 'assists'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'PF'], [7, 'fgAttempted'], [7, 'fgMade'], [7, 'ftAttempted'], [7, 'ftMade'], [7, 'threeAttempted'], [7, 'threeMade'], [7, 'PostGP'], [7, 'PostGS'], [7, 'PostMinutes'], [7, 'PostPoints'], [7, 'PostoRebounds'], [7, 'PostdRebounds'], [7, 'PostRebounds'], [7, 'PostAssists'], [7, 'PostSteals'], [7, 'PostBlocks'], [7, 'PostTurnovers'], [7, 'PostPF'], [7, 'PostfgAttempted'], [7, 'PostfgMade'], [7, 'PostftAttempted'], [7, 'PostftMade'], [7, 'PostthreeAttempted'], [7, 'PostthreeMade'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'tmIDWinner'], [8, 'lgIDWinner'], [8, 'tmIDLoser'], [8, 'lgIDLoser'], [8, 'W'], [8, 'L']], 'column_names': [[-1, '*'], [0, 'playerID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [0, 'position'], [1, 'coachID'], [1, 'year'], [1, 'team ID'], [1, 'league ID'], [1, 'stint'], [1, 'won'], [1, 'lost'], [1, 'post season wins'], [1, 'post season losses'], [2, 'id'], [2, 'draftYear'], [2, 'draftRound'], [2, 'league ID'], [2, 'draft overall rank'], [2, 'team ID'], [2, 'firstName'], [2, 'lastName'], [2, 'suffixName'], [2, 'playerID'], [2, 'draftFrom'], [2, 'league ID'], [3, 'playerID'], [3, 'last_name'], [3, 'first_name'], [3, 'season_id'], [3, 'conference'], [3, 'league ID'], [3, 'games_played'], [3, 'minutes'], [3, 'points'], [3, 'offense rebounds'], [3, 'defense rebounds'], [3, 'rebounds'], [3, 'assistants'], [3, 'steals'], [3, 'blocks'], [3, 'turnovers'], [3, 'personal_fouls'], [3, 'field goal attempted'], [3, 'field goal made'], [3, 'free throw attempted'], [3, 'free throw made'], [3, 'three point attempted'], [3, 'three point made'], [4, 'playerID'], [4, 'use first name'], [4, 'firstName'], [4, 'middleName'], [4, 'lastName'], [4, 'nameGiven'], [4, 'fullGivenName'], [4, 'nameSuffix'], [4, 'nick name'], [4, 'position'], [4, 'firstseason'], [4, 'lastseason'], [4, 'height'], [4, 'weight'], [4, 'college'], [4, 'collegeOther'], [4, 'birthDate'], [4, 'birthCity'], [4, 'birthState'], [4, 'birthCountry'], [4, 'highSchool'], [4, 'high school city'], [4, 'high school state'], [4, 'high school country'], [4, 'deathDate'], [4, 'race'], [5, 'year'], [5, 'league ID'], [5, 'team ID'], [5, 'franchID'], [5, 'confID'], [5, 'division ID'], [5, 'rank'], [5, 'confRank'], [5, 'playoff'], [5, 'name'], [5, 'offense field goal made'], [5, 'offense free throw made'], [5, 'offense points'], [5, 'defense points'], [5, 'home wins'], [5, 'home loses'], [5, 'away wins'], [5, 'away loses'], [5, 'won'], [5, 'lost'], [5, 'games'], [5, 'arena'], [6, 'id'], [6, 'year'], [6, 'coachID'], [6, 'award'], [6, 'league ID'], [6, 'note'], [7, 'id'], [7, 'playerID'], [7, 'year'], [7, 'stint'], [7, 'team ID'], [7, 'lgID'], [7, 'game presentatons'], [7, 'game starting'], [7, 'minutes'], [7, 'points'], [7, 'offense rebounds'], [7, 'defense rebounds'], [7, 'rebounds'], [7, 'assistants'], [7, 'steals'], [7, 'blocks'], [7, 'turnovers'], [7, 'personal fouls'], [7, 'field goal attempted'], [7, 'field goal made'], [7, 'free throw attempted'], [7, 'free throw made'], [7, 'three point attempted'], [7, 'three point made'], [7, 'post season game presentations'], [7, 'post season game starting'], [7, 'post season minutes'], [7, 'post season points'], [7, 'post season offense rebounds'], [7, 'post season defense rebounds'], [7, 'post season defense rebounds'], [7, 'post season assistants'], [7, 'post season steals'], [7, 'post season blocks'], [7, 'post season turnovers'], [7, 'post season personal fouls'], [7, 'post season field goal attempted'], [7, 'post season field goal made'], [7, 'post season field free throw attempted'], [7, 'post season free throw made'], [7, 'post season three point attempted'], [7, 'post season three point made'], [7, 'note'], [8, 'id'], [8, 'year'], [8, 'round'], [8, 'series'], [8, 'team id winner'], [8, 'league id winner'], [8, 'team id loser'], [8, 'league id loser'], [8, 'wins'], [8, 'loses']], 'column_types': ['text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer'], 'primary_keys': [[1, 2, 3], [7, 8, 9, 11], 16, [28, 31], 51, [77, 79], 99, 105, 148], 'foreign_keys': [[1, 51], [9, 79], [8, 77], [21, 79], [17, 77], [28, 51], [101, 7], [100, 8], [109, 79], [107, 77], [154, 79], [149, 77], [152, 79], [149, 77]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(T2.points) FROM players AS T1 INNER JOIN player_allstar AS T2 ON T1.playerID = T2.playerID WHERE T2.season_id = 1975"}],"db_id":"professional_basketball"}892{"prompt":"Who is the eldest player and where did he\/she come from?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Who is the eldest player and where did he\/she come from?\n        \n        ### Context:\n        #### Query Context:\n        eldest player refers to MIN(DOB); where he\/she come from refers to Country_Name\n        \n        #### Database Schema:\n        {'db_id': 'soccer_2016', 'table_names_original': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'table_names': ['Batting_Style', 'Bowling_Style', 'City', 'Country', 'Extra_Type', 'Extra_Runs', 'Out_Type', 'Outcome', 'Player', 'Rolee', 'Season', 'Team', 'Toss_Decision', 'Umpire', 'Venue', 'Win_By', 'Match', 'Ball_by_Ball', 'Batsman_Scored', 'Player_Match', 'Wicket_Taken'], 'column_names_original': [[-1, '*'], [0, 'Batting_Id'], [0, 'Batting_hand'], [1, 'Bowling_Id'], [1, 'Bowling_skill'], [2, 'City_Id'], [2, 'City_Name'], [2, 'Country_id'], [3, 'Country_Id'], [3, 'Country_Name'], [4, 'Extra_Id'], [4, 'Extra_Name'], [5, 'Match_Id'], [5, 'Over_Id'], [5, 'Ball_Id'], [5, 'Extra_Type_Id'], [5, 'Extra_Runs'], [5, 'Innings_No'], [6, 'Out_Id'], [6, 'Out_Name'], [7, 'Outcome_Id'], [7, 'Outcome_Type'], [8, 'Player_Id'], [8, 'Player_Name'], [8, 'DOB'], [8, 'Batting_hand'], [8, 'Bowling_skill'], [8, 'Country_Name'], [9, 'Role_Id'], [9, 'Role_Desc'], [10, 'Season_Id'], [10, 'Man_of_the_Series'], [10, 'Orange_Cap'], [10, 'Purple_Cap'], [10, 'Season_Year'], [11, 'Team_Id'], [11, 'Team_Name'], [12, 'Toss_Id'], [12, 'Toss_Name'], [13, 'Umpire_Id'], [13, 'Umpire_Name'], [13, 'Umpire_Country'], [14, 'Venue_Id'], [14, 'Venue_Name'], [14, 'City_Id'], [15, 'Win_Id'], [15, 'Win_Type'], [16, 'Match_Id'], [16, 'Team_1'], [16, 'Team_2'], [16, 'Match_Date'], [16, 'Season_Id'], [16, 'Venue_Id'], [16, 'Toss_Winner'], [16, 'Toss_Decide'], [16, 'Win_Type'], [16, 'Win_Margin'], [16, 'Outcome_type'], [16, 'Match_Winner'], [16, 'Man_of_the_Match'], [17, 'Match_Id'], [17, 'Over_Id'], [17, 'Ball_Id'], [17, 'Innings_No'], [17, 'Team_Batting'], [17, 'Team_Bowling'], [17, 'Striker_Batting_Position'], [17, 'Striker'], [17, 'Non_Striker'], [17, 'Bowler'], [18, 'Match_Id'], [18, 'Over_Id'], [18, 'Ball_Id'], [18, 'Runs_Scored'], [18, 'Innings_No'], [19, 'Match_Id'], [19, 'Player_Id'], [19, 'Role_Id'], [19, 'Team_Id'], [20, 'Match_Id'], [20, 'Over_Id'], [20, 'Ball_Id'], [20, 'Player_Out'], [20, 'Kind_Out'], [20, 'Fielders'], [20, 'Innings_No']], 'column_names': [[-1, '*'], [0, 'batting id'], [0, 'batting hand'], [1, 'bowling id'], [1, 'bowling skill'], [2, 'city id'], [2, 'city name'], [2, 'country id'], [3, 'country id'], [3, 'country name'], [4, 'extra id'], [4, 'extra name'], [5, 'match id'], [5, 'over id'], [5, 'ball id'], [5, 'extra type id'], [5, 'extra runs'], [5, 'innings number'], [6, 'out id'], [6, 'out name'], [7, 'outcome id'], [7, 'outcome type'], [8, 'player id'], [8, 'player name'], [8, 'date of birth'], [8, 'batting hand'], [8, 'bowling skill'], [8, 'country name'], [9, 'role id'], [9, 'role description'], [10, 'season id'], [10, 'man of the series'], [10, 'orange cap'], [10, 'purple cap'], [10, 'season year'], [11, 'team id'], [11, 'team name'], [12, 'toss id'], [12, 'toss name'], [13, 'umpire id'], [13, 'umpire name'], [13, 'umpire country'], [14, 'venue id'], [14, 'venue name'], [14, 'city id'], [15, 'winning id'], [15, 'winning type'], [16, 'Match_Id'], [16, 'team 1'], [16, 'team 2'], [16, 'match date'], [16, 'season id'], [16, 'venue id'], [16, 'toss winner'], [16, 'toss decide'], [16, 'winning type'], [16, 'winning margin'], [16, 'outcome type'], [16, 'match winner'], [16, 'man of the match'], [17, 'match id'], [17, 'over id'], [17, 'ball id'], [17, 'innings number'], [17, 'team batting'], [17, 'team bowling'], [17, 'striker batting position'], [17, 'Striker'], [17, 'non striker'], [17, 'Bowler'], [18, 'match id'], [18, 'over id'], [18, 'ball id'], [18, 'runs scored'], [18, 'innings number'], [19, 'match id'], [19, 'player id'], [19, 'role id'], [19, 'team id'], [20, 'match id'], [20, 'over id'], [20, 'ball id'], [20, 'player out'], [20, 'kind out'], [20, 'Fielders'], [20, 'innings number']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'date', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'date', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8, 10, [12, 13, 14, 17], 18, 20, 22, 28, 30, 35, 37, 39, 42, 45, 47, [60, 61, 62, 63], [70, 71, 72, 74], [75, 76, 77], [79, 80, 81, 85]], 'foreign_keys': [[15, 10], [27, 8], [26, 3], [25, 1], [41, 8], [44, 5], [59, 22], [58, 35], [57, 18], [55, 45], [54, 37], [53, 35], [52, 42], [51, 30], [49, 35], [48, 35], [60, 47], [70, 47], [77, 28], [78, 35], [76, 22], [75, 47], [84, 22], [83, 18], [82, 22], [79, 47]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Player_Name, T2.Country_Name FROM Player AS T1 INNER JOIN Country AS T2 ON T1.Country_Name = T2.Country_Id ORDER BY T1.DOB LIMIT 1"}],"db_id":"soccer_2016"}893{"prompt":"What is the category of businesses with highest star rating?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the category of businesses with highest star rating?\n        \n        ### Context:\n        #### Query Context:\n        category refers to category_name; highest star rating refers to max(stars)\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.category_name FROM Business AS T1 INNER JOIN Business_Categories AS T2 ON T1.business_id = T2.business_id INNER JOIN Categories AS T3 ON T2.category_id = T3.category_id ORDER BY T1.stars DESC LIMIT 1"}],"db_id":"public_review_platform"}894{"prompt":"Among the male patients, list down 5 birth dates of patients taking the medication \"Penicillin V Potassium 250 MG\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the male patients, list down 5 birth dates of patients taking the medication \"Penicillin V Potassium 250 MG\".\n        \n        ### Context:\n        #### Query Context:\n        male patients refers to gender = 'M'; Penicillin V Potassium 250 MG refers to medications.DESCRIPTION = 'Penicillin V Potassium 250 MG';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.birthdate FROM medications AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T1.DESCRIPTION = 'Penicillin V Potassium 250 MG' AND T2.gender = 'M' LIMIT 5"}],"db_id":"synthea"}895{"prompt":"Name both the alias and the bad alias of zip code 38015.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Name both the alias and the bad alias of zip code 38015.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.alias, T2.bad_alias FROM alias AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.zip_code = 38015"}],"db_id":"address"}896{"prompt":"Please provide the address of the customer whose first name is SUSAN with the postal code 77948.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please provide the address of the customer whose first name is SUSAN with the postal code 77948.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'movie_3', 'table_names_original': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'table_names': ['film_text', 'actor', 'address', 'category', 'city', 'country', 'customer', 'film', 'film_actor', 'film_category', 'inventory', 'language', 'payment', 'rental', 'staff', 'store'], 'column_names_original': [[-1, '*'], [0, 'film_id'], [0, 'title'], [0, 'description'], [1, 'actor_id'], [1, 'first_name'], [1, 'last_name'], [1, 'last_update'], [2, 'address_id'], [2, 'address'], [2, 'address2'], [2, 'district'], [2, 'city_id'], [2, 'postal_code'], [2, 'phone'], [2, 'last_update'], [3, 'category_id'], [3, 'name'], [3, 'last_update'], [4, 'city_id'], [4, 'city'], [4, 'country_id'], [4, 'last_update'], [5, 'country_id'], [5, 'country'], [5, 'last_update'], [6, 'customer_id'], [6, 'store_id'], [6, 'first_name'], [6, 'last_name'], [6, 'email'], [6, 'address_id'], [6, 'active'], [6, 'create_date'], [6, 'last_update'], [7, 'film_id'], [7, 'title'], [7, 'description'], [7, 'release_year'], [7, 'language_id'], [7, 'original_language_id'], [7, 'rental_duration'], [7, 'rental_rate'], [7, 'length'], [7, 'replacement_cost'], [7, 'rating'], [7, 'special_features'], [7, 'last_update'], [8, 'actor_id'], [8, 'film_id'], [8, 'last_update'], [9, 'film_id'], [9, 'category_id'], [9, 'last_update'], [10, 'inventory_id'], [10, 'film_id'], [10, 'store_id'], [10, 'last_update'], [11, 'language_id'], [11, 'name'], [11, 'last_update'], [12, 'payment_id'], [12, 'customer_id'], [12, 'staff_id'], [12, 'rental_id'], [12, 'amount'], [12, 'payment_date'], [12, 'last_update'], [13, 'rental_id'], [13, 'rental_date'], [13, 'inventory_id'], [13, 'customer_id'], [13, 'return_date'], [13, 'staff_id'], [13, 'last_update'], [14, 'staff_id'], [14, 'first_name'], [14, 'last_name'], [14, 'address_id'], [14, 'picture'], [14, 'email'], [14, 'store_id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last_update'], [15, 'store_id'], [15, 'manager_staff_id'], [15, 'address_id'], [15, 'last_update']], 'column_names': [[-1, '*'], [0, 'film id'], [0, 'title'], [0, 'description'], [1, 'actor id'], [1, 'first name'], [1, 'last name'], [1, 'last update'], [2, 'address id'], [2, 'address'], [2, 'address 2'], [2, 'district'], [2, 'city_id'], [2, 'postal code'], [2, 'phone'], [2, 'last update'], [3, 'category id'], [3, 'name'], [3, 'last update'], [4, 'city id'], [4, 'city'], [4, 'country id'], [4, 'last update'], [5, 'country id'], [5, 'the name of the country'], [5, 'last update'], [6, 'country id'], [6, 'store id'], [6, 'first name'], [6, 'last name'], [6, 'email'], [6, 'address id'], [6, 'active'], [6, 'create date'], [6, 'last update'], [7, 'film id'], [7, 'title'], [7, 'description'], [7, 'release year'], [7, 'language id'], [7, 'original language id'], [7, 'rental duration'], [7, 'rental rate'], [7, 'length'], [7, 'replacement cost'], [7, 'rating'], [7, 'special features'], [7, 'last update'], [8, 'actor id'], [8, 'film id'], [8, 'last update'], [9, 'film id'], [9, 'category id'], [9, 'last update'], [10, 'inventory id'], [10, 'film id'], [10, 'store id'], [10, 'last update'], [11, 'language id'], [11, 'name'], [11, 'last update'], [12, 'payment id'], [12, 'customer id'], [12, 'staff id'], [12, 'rental id'], [12, 'amount'], [12, 'payment date'], [12, 'last update'], [13, 'rental id'], [13, 'rental date'], [13, 'inventory id'], [13, 'customer id'], [13, 'return date'], [13, 'staff id'], [13, 'last update'], [14, 'staff id'], [14, 'first name'], [14, 'last name'], [14, 'address id'], [14, 'picture of the staff'], [14, 'email of the staff'], [14, 'store id'], [14, 'active'], [14, 'username'], [14, 'password'], [14, 'last update'], [15, 'store id'], [15, 'manager staff id'], [15, 'address id'], [15, 'last update']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'real', 'integer', 'real', 'text', 'text', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'integer', 'integer', 'datetime', 'integer', 'text', 'datetime', 'integer', 'integer', 'integer', 'integer', 'real', 'datetime', 'datetime', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'integer', 'datetime', 'integer', 'text', 'text', 'integer', 'blob', 'text', 'integer', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'datetime'], 'primary_keys': [1, 4, 8, 16, 19, 23, 26, 35, [48, 49], [51, 52], 54, 58, 61, 68, 75, 86], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.address FROM address AS T1 INNER JOIN customer AS T2 ON T1.address_id = T2.address_id WHERE T2.first_name = 'SUSAN' AND T1.postal_code = 77948"}],"db_id":"movie_3"}897{"prompt":"List all the country in East Asia & Pacific region that have more than 2000000 urban population in 1970.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List all the country in East Asia & Pacific region that have more than 2000000 urban population in 1970.\n        \n        ### Context:\n        #### Query Context:\n        country refers to LongName; have more than 2000000 urban population refers to value where indicatorname = 'Urban population'>2000000; in 1970 refers to year = '1970'\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.CountryCode FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T2.Year = 1970 AND T1.Region = 'East Asia & Pacific' AND T2.Value > 2000000 AND t2.indicatorname = 'Urban population'"}],"db_id":"world_development_indicators"}898{"prompt":"How many Catalan-language Wikipedia pages are there overall?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many Catalan-language Wikipedia pages are there overall?\n        \n        ### Context:\n        #### Query Context:\n        Catalan-language refers to lang = 'ca';\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT pages FROM langs WHERE lang = 'ca'"}],"db_id":"language_corpus"}899{"prompt":"How many teenagers are working as Machine-op-inspct?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many teenagers are working as Machine-op-inspct?\n        \n        ### Context:\n        #### Query Context:\n        teenager is a person aged between 13 and 19 years; OCCUPATION = 'Machine-op-inspct';\n        \n        #### Database Schema:\n        {'db_id': 'software_company', 'table_names_original': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'table_names': ['Demog', 'mailings3', 'Customers', 'Mailings1_2', 'Sales'], 'column_names_original': [[-1, '*'], [0, 'GEOID'], [0, 'INHABITANTS_K'], [0, 'INCOME_K'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFID'], [1, 'REF_DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL_STATUS'], [2, 'GEOID'], [2, 'EDUCATIONNUM'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFID'], [3, 'REF_DATE'], [3, 'RESPONSE'], [4, 'EVENTID'], [4, 'REFID'], [4, 'EVENT_DATE'], [4, 'AMOUNT']], 'column_names': [[-1, '*'], [0, 'GEOGRAPHIC ID'], [0, 'INHABITANTS (THOUSANDS)'], [0, 'INCOME (THOUSANDS)'], [0, 'A_VAR1'], [0, 'A_VAR2'], [0, 'A_VAR3'], [0, 'A_VAR4'], [0, 'A_VAR5'], [0, 'A_VAR6'], [0, 'A_VAR7'], [0, 'A_VAR8'], [0, 'A_VAR9'], [0, 'A_VAR10'], [0, 'A_VAR11'], [0, 'A_VAR12'], [0, 'A_VAR13'], [0, 'A_VAR14'], [0, 'A_VAR15'], [0, 'A_VAR16'], [0, 'A_VAR17'], [0, 'A_VAR18'], [1, 'REFERENCE ID'], [1, 'REFERENCE DATE'], [1, 'RESPONSE'], [2, 'ID'], [2, 'SEX'], [2, 'MARITAL STATUS'], [2, 'GEOGRAPHIC ID'], [2, 'EDUCATION NUMBER'], [2, 'OCCUPATION'], [2, 'age'], [3, 'REFERENCE ID'], [3, 'REFERENCE DATE'], [3, 'RESPONSE'], [4, 'EVENT ID'], [4, 'REFERENCE ID'], [4, 'EVENT DATE'], [4, 'AMOUNT']], 'column_types': ['text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer', 'datetime', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'datetime', 'text', 'integer', 'integer', 'datetime', 'real'], 'primary_keys': [1, 22, 25, 32, 35], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(ID) teenager_number FROM Customers WHERE OCCUPATION = 'Machine-op-inspct' AND age >= 13 AND age <= 19"}],"db_id":"software_company"}900{"prompt":"Please calculate the percentage of Sub-Saharan African countries which are in the Special trade system.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please calculate the percentage of Sub-Saharan African countries which are in the Special trade system.\n        \n        ### Context:\n        #### Query Context:\n        Sub-Saharan African is the name of the region; SystemOfTrade = 'Special trade system'; countries refer to CountryCode; DIVIDE(COUNT (CountryCode where SystemOfTrade = 'Special trade system' and Region = 'Sub-Saharan Africa'), COUNT(CountryCode where Region = 'Sub-Saharan Africa')) as percentage;\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT  CAST(SUM(CASE WHEN Region = 'Sub-Saharan Africa' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(CountryCode) FROM Country WHERE SystemOfTrade = 'Special trade system'"}],"db_id":"world_development_indicators"}901{"prompt":"How many languages are used in Cyprus?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many languages are used in Cyprus?\n        \n        ### Context:\n        #### Query Context:\n        Cyprus is a name of Country;\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T1.Name = 'Cyprus' THEN 1 ELSE 0 END) FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode"}],"db_id":"world"}902{"prompt":"How many cities are there in the country ruled by Kostis Stefanopoulos?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many cities are there in the country ruled by Kostis Stefanopoulos?\n        \n        ### Context:\n        #### Query Context:\n        ruled by Kostis Stefanopoulos refers to HeadOfState = 'Kostis Stefanopoulos';\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.Name) FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T2.HeadOfState = 'Kostis Stefanopoulos'"}],"db_id":"world"}903{"prompt":"Among all the donors from New York, how many of them are teachers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among all the donors from New York, how many of them are teachers?\n        \n        ### Context:\n        #### Query Context:\n        from New York refers to donor_city = 'New York'; donor is a teacher refers to is_teacher_acct = 't';\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(donationid) FROM donations WHERE is_teacher_acct = 't' AND donor_city = 'New York'"}],"db_id":"donor"}904{"prompt":"What is the teacher prefix for the teacher who wrote the project 'Creating Memories and Inspiring Dreams'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the teacher prefix for the teacher who wrote the project 'Creating Memories and Inspiring Dreams'?\n        \n        ### Context:\n        #### Query Context:\n        Creating Memories and Inspiring Dreams is title;\n        \n        #### Database Schema:\n        {'db_id': 'donor', 'table_names_original': ['essays', 'projects', 'donations', 'resources'], 'table_names': ['essays', 'projects', 'donations', 'resources'], 'column_names_original': [[-1, '*'], [0, 'projectid'], [0, 'teacher_acctid'], [0, 'title'], [0, 'short_description'], [0, 'need_statement'], [0, 'essay'], [1, 'projectid'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school_ncesid'], [1, 'school_latitude'], [1, 'school_longitude'], [1, 'school_city'], [1, 'school_state'], [1, 'school_zip'], [1, 'school_metro'], [1, 'school_district'], [1, 'school_county'], [1, 'school_charter'], [1, 'school_magnet'], [1, 'school_year_round'], [1, 'school_nlns'], [1, 'school_kipp'], [1, 'school_charter_ready_promise'], [1, 'teacher_prefix'], [1, 'teacher_teach_for_america'], [1, 'teacher_ny_teaching_fellow'], [1, 'primary_focus_subject'], [1, 'primary_focus_area'], [1, 'secondary_focus_subject'], [1, 'secondary_focus_area'], [1, 'resource_type'], [1, 'poverty_level'], [1, 'grade_level'], [1, 'fulfillment_labor_materials'], [1, 'total_price_excluding_optional_support'], [1, 'total_price_including_optional_support'], [1, 'students_reached'], [1, 'eligible_double_your_impact_match'], [1, 'eligible_almost_home_match'], [1, 'date_posted'], [2, 'donationid'], [2, 'projectid'], [2, 'donor_acctid'], [2, 'donor_city'], [2, 'donor_state'], [2, 'donor_zip'], [2, 'is_teacher_acct'], [2, 'donation_timestamp'], [2, 'donation_to_project'], [2, 'donation_optional_support'], [2, 'donation_total'], [2, 'dollar_amount'], [2, 'donation_included_optional_support'], [2, 'payment_method'], [2, 'payment_included_acct_credit'], [2, 'payment_included_campaign_gift_card'], [2, 'payment_included_web_purchased_gift_card'], [2, 'payment_was_promo_matched'], [2, 'via_giving_page'], [2, 'for_honoree'], [2, 'donation_message'], [3, 'resourceid'], [3, 'projectid'], [3, 'vendorid'], [3, 'vendor_name'], [3, 'project_resource_type'], [3, 'item_name'], [3, 'item_number'], [3, 'item_unit_price'], [3, 'item_quantity']], 'column_names': [[-1, '*'], [0, 'project id'], [0, 'teacher acctid'], [0, 'title'], [0, 'short description'], [0, 'need statement'], [0, 'essay'], [1, 'project id'], [1, 'teacher_acctid'], [1, 'schoolid'], [1, 'school national center for education statistics id'], [1, 'school latitude'], [1, 'school longitude'], [1, 'school city'], [1, 'school state'], [1, 'school zip'], [1, 'school metro'], [1, 'school district'], [1, 'school county'], [1, 'school charter'], [1, 'school magnet'], [1, 'school year round'], [1, 'school Nonleafy Normal Stature'], [1, 'school Knowledge Is Power Program'], [1, 'school charter ready promise'], [1, 'teacher prefix'], [1, 'teachers teach for america'], [1, 'teacher_new_york_teaching_fellow'], [1, 'primary focus subject'], [1, 'primary focus area'], [1, 'secondary focus subject'], [1, 'secondary focus area'], [1, 'resource type'], [1, 'poverty level'], [1, 'grade level'], [1, 'fulfillment labor materials'], [1, 'total price excluding optional support'], [1, 'total price including optional support'], [1, 'students reached'], [1, 'eligible double your impact match'], [1, 'eligible almost home match'], [1, 'date posted'], [2, 'donation id'], [2, 'project id'], [2, 'donor accid'], [2, 'donor city'], [2, 'donor state'], [2, 'donor zip'], [2, 'is teacher acct'], [2, 'donation timestamp'], [2, 'donation to project'], [2, 'donation optional support'], [2, 'donation total'], [2, 'dollar amount'], [2, 'donation included optional support'], [2, 'payment method'], [2, 'payment included acct credit'], [2, 'payment included campaign gift card'], [2, 'payment included web purchased gift card'], [2, 'payment was promo matched'], [2, 'via giving page'], [2, 'for honoree'], [2, 'donation message'], [3, 'resource id'], [3, 'project id'], [3, 'vendor id'], [3, 'vendor name'], [3, 'project resource type'], [3, 'item name'], [3, 'item number'], [3, 'item unit price'], [3, 'item quantity']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'integer', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'datetime', 'real', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer'], 'primary_keys': [7, 42, 63], 'foreign_keys': [[43, 7], [64, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.teacher_prefix FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T1.title LIKE 'Creating Memories and Inspiring Dreams%'"}],"db_id":"donor"}905{"prompt":"List the name of married employees with less than 20 vacation hours.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the name of married employees with less than 20 vacation hours.\n        \n        ### Context:\n        #### Query Context:\n        married employee refers to MaritalStatus = 'M'; less than 20 vacation hours refers to VacationHours<20\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 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'PerAssemblyQty'], [50, 'ModifiedDate'], [51, 'BusinessEntityID'], [51, 'rowguid'], [51, 'ModifiedDate'], [52, 'ContactTypeID'], [52, 'Name'], [52, 'ModifiedDate'], [53, 'CurrencyRateID'], [53, 'CurrencyRateDate'], [53, 'FromCurrencyCode'], [53, 'ToCurrencyCode'], [53, 'AverageRate'], [53, 'EndOfDayRate'], [53, 'ModifiedDate'], [54, 'DepartmentID'], [54, 'Name'], [54, 'GroupName'], [54, 'ModifiedDate'], [55, 'BusinessEntityID'], [55, 'DepartmentID'], [55, 'ShiftID'], [55, 'StartDate'], [55, 'EndDate'], [55, 'ModifiedDate'], [56, 'BusinessEntityID'], [56, 'RateChangeDate'], [56, 'Rate'], [56, 'PayFrequency'], [56, 'ModifiedDate'], [57, 'JobCandidateID'], [57, 'BusinessEntityID'], [57, 'Resume'], [57, 'ModifiedDate'], [58, 'LocationID'], [58, 'Name'], [58, 'CostRate'], [58, 'Availability'], [58, 'ModifiedDate'], [59, 'PhoneNumberTypeID'], [59, 'Name'], [59, 'ModifiedDate'], [60, 'ProductID'], [60, 'Name'], [60, 'ProductNumber'], [60, 'MakeFlag'], [60, 'FinishedGoodsFlag'], [60, 'Color'], [60, 'SafetyStockLevel'], [60, 'ReorderPoint'], [60, 'StandardCost'], [60, 'ListPrice'], [60, 'Size'], [60, 'SizeUnitMeasureCode'], [60, 'WeightUnitMeasureCode'], [60, 'Weight'], [60, 'DaysToManufacture'], [60, 'ProductLine'], [60, 'Class'], [60, 'Style'], [60, 'ProductSubcategoryID'], [60, 'ProductModelID'], [60, 'SellStartDate'], [60, 'SellEndDate'], [60, 'DiscontinuedDate'], [60, 'rowguid'], [60, 'ModifiedDate'], [61, 'DocumentNode'], [61, 'DocumentLevel'], [61, 'Title'], [61, 'Owner'], [61, 'FolderFlag'], [61, 'FileName'], [61, 'FileExtension'], [61, 'Revision'], [61, 'ChangeNumber'], [61, 'Status'], [61, 'DocumentSummary'], [61, 'Document'], [61, 'rowguid'], [61, 'ModifiedDate'], [62, 'StateProvinceID'], [62, 'StateProvinceCode'], [62, 'CountryRegionCode'], [62, 'IsOnlyStateProvinceFlag'], [62, 'Name'], [62, 'TerritoryID'], [62, 'rowguid'], [62, 'ModifiedDate'], [63, 'CreditCardID'], [63, 'CardType'], [63, 'CardNumber'], [63, 'ExpMonth'], [63, 'ExpYear'], [63, 'ModifiedDate'], [64, 'SalesOrderID'], [64, 'RevisionNumber'], [64, 'OrderDate'], [64, 'DueDate'], [64, 'ShipDate'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'ShipMethodID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'TaxAmt'], [64, 'Freight'], [64, 'TotalDue'], [64, 'Comment'], [64, 'rowguid'], [64, 'ModifiedDate']], 'column_names': [[-1, '*'], [0, 'Country Region Code'], [0, 'Name'], [0, 'modified date'], [1, 'Culture ID'], [1, 'Name'], [1, 'modified date'], [2, 'Currency Code'], [2, 'Name'], [2, 'modified date'], [3, 'Country Region Code'], [3, 'Currency Code'], [3, 'modified date'], [4, 'Business Entity ID'], [4, 'Person Type'], [4, 'Name Style'], [4, 'Title'], [4, 'First Name'], [4, 'Middle Name'], [4, 'Last Name'], [4, 'Suffix'], [4, 'Email Promotion'], [4, 'Additional Contact Info'], [4, 'Demographics'], [4, 'rowguid'], [4, 'modified date'], [5, 'Business Entity ID'], [5, 'Person ID'], [5, 'Contact Type ID'], [5, 'rowguid'], [5, 'modified date'], [6, 'Business Entity ID'], [6, 'Email Address ID'], [6, 'Email Address'], [6, 'rowguid'], [6, 'modified date'], [7, 'Business Entity ID'], [7, 'National ID Number'], [7, 'Login ID'], [7, 'Organization Node'], [7, 'Organization Level'], [7, 'Job Title'], [7, 'Birth Date'], [7, 'Marital Status'], [7, 'Gender'], [7, 'Hire Date'], [7, 'Salaried Flag'], [7, 'Vacation Hours'], [7, 'Sick Leave Hours'], [7, 'Current Flag'], [7, 'rowguid'], [7, 'modified date'], [8, 'Business Entity ID'], [8, 'Password Hash'], [8, 'Password Salt'], [8, 'rowguid'], [8, 'modified date'], [9, 'Business Entity ID'], [9, 'Credit Card ID'], [9, 'modified date'], [10, 'Product Category ID'], [10, 'Name'], [10, 'rowguid'], [10, 'modified date'], [11, 'Product Description ID'], [11, 'Description'], [11, 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[63, 'Credit Card ID'], [63, 'Card Type'], [63, 'Card Number'], [63, 'Expiration Month'], [63, 'Expiration Year'], [63, 'modified date'], [64, 'Sales Order ID'], [64, 'RevisionNumber'], [64, 'Order Date'], [64, 'DueDate'], [64, 'Ship Date'], [64, 'Status'], [64, 'OnlineOrderFlag'], [64, 'SalesOrderNumber'], [64, 'PurchaseOrderNumber'], [64, 'AccountNumber'], [64, 'CustomerID'], [64, 'SalesPersonID'], [64, 'TerritoryID'], [64, 'BillToAddressID'], [64, 'ShipToAddressID'], [64, 'Ship Method ID'], [64, 'CreditCardID'], [64, 'CreditCardApprovalCode'], [64, 'CurrencyRateID'], [64, 'SubTotal'], [64, 'Tax Amount'], [64, 'Freight'], [64, 'Total Due'], [64, 'Comment'], [64, 'rowguid'], [64, 'modified date']], 'column_types': ['text', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'text', 'text', 'datetime', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 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'datetime', 'integer', 'integer', 'integer', 'datetime', 'datetime', 'text', 'integer', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'datetime', 'text', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'text', 'datetime', 'integer', 'integer', 'integer', 'date', 'date', 'datetime', 'integer', 'datetime', 'real', 'integer', 'datetime', 'integer', 'integer', 'text', 'datetime', 'integer', 'text', 'real', 'real', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'real', 'real', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'text', 'datetime', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'text', 'blob', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'integer', 'integer', 'datetime', 'integer', 'integer', 'datetime', 'datetime', 'datetime', 'integer', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'real', 'real', 'real', 'real', 'text', 'text', 'datetime'], 'primary_keys': [1, 4, 7, [10, 11], 13, [26, 27, 28], [31, 32], 36, 52, [57, 58], 60, 64, 68, [74, 75, 76], 78, 84, 89, 93, 103, [112, 113], [117, 118, 119], 123, 126, 131, 137, [148, 149, 150], 153, 160, [166, 167], 169, 178, [181, 182], [186, 187], [189, 190], [196, 197], 200, 208, [214, 215], 219, 229, 238, [246, 247], 257, 271, 281, [291, 292, 293], 303, [310, 311], 315, 324, 328, 337, 340, 343, 350, [354, 355, 356, 357], [360, 361], 365, 369, 374, 377, 402, 416, 424, 430], 'foreign_keys': [[11, 7], [10, 1], [13, 337], [27, 13], [28, 340], [26, 337], [31, 13], [36, 13], [52, 13], [57, 13], [58, 424], [76, 4], [75, 64], [74, 68], [85, 60], [95, 1], [104, 93], [103, 36], [112, 103], [118, 93], [117, 103], [148, 337], [150, 324], [149, 315], [154, 416], [162, 103], [160, 337], [167, 89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.FirstName, T1.LastName FROM Person AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.MaritalStatus = 'M' AND T2.VacationHours < 20"}],"db_id":"works_cycles"}906{"prompt":"What is the name of the airline with the highest number of non-cancelled flights?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the airline with the highest number of non-cancelled flights?\n        \n        ### Context:\n        #### Query Context:\n        names of the airlines refers to Description; highest number of non-cancelled flights refers to MAX(COUNT(CANCELLED = 0));\n        \n        #### Database Schema:\n        {'db_id': 'airline', 'table_names_original': ['Air Carriers', 'Airports', 'Airlines'], 'table_names': ['Air Carriers', 'Airports', 'Airlines'], 'column_names_original': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'FL_DATE'], [2, 'OP_CARRIER_AIRLINE_ID'], [2, 'TAIL_NUM'], [2, 'OP_CARRIER_FL_NUM'], [2, 'ORIGIN_AIRPORT_ID'], [2, 'ORIGIN_AIRPORT_SEQ_ID'], [2, 'ORIGIN_CITY_MARKET_ID'], [2, 'ORIGIN'], [2, 'DEST_AIRPORT_ID'], [2, 'DEST_AIRPORT_SEQ_ID'], [2, 'DEST_CITY_MARKET_ID'], [2, 'DEST'], [2, 'CRS_DEP_TIME'], [2, 'DEP_TIME'], [2, 'DEP_DELAY'], [2, 'DEP_DELAY_NEW'], [2, 'ARR_TIME'], [2, 'ARR_DELAY'], [2, 'ARR_DELAY_NEW'], [2, 'CANCELLED'], [2, 'CANCELLATION_CODE'], [2, 'CRS_ELAPSED_TIME'], [2, 'ACTUAL_ELAPSED_TIME'], [2, 'CARRIER_DELAY'], [2, 'WEATHER_DELAY'], [2, 'NAS_DELAY'], [2, 'SECURITY_DELAY'], [2, 'LATE_AIRCRAFT_DELAY']], 'column_names': [[-1, '*'], [0, 'Code'], [0, 'Description'], [1, 'Code'], [1, 'Description'], [2, 'flight date'], [2, 'operator carrier airline id'], [2, 'tail number'], [2, 'operator carrier flight number'], [2, 'origin airport id'], [2, 'origin airport sequence id'], [2, 'origin city market id'], [2, 'ORIGIN'], [2, 'destination airport id'], [2, 'destination airport sequence id'], [2, 'destination city market id'], [2, 'destination'], [2, 'scheduled local departure time'], [2, 'departure time'], [2, 'Departure delay'], [2, 'departure delay new'], [2, 'arrival time'], [2, 'arrival delay'], [2, 'arrival delay new'], [2, 'CANCELLED'], [2, 'cancellation code'], [2, 'scheduled elapsed time'], [2, 'actual elapsed time'], [2, 'carrier delay'], [2, 'weather delay'], [2, 'National Aviavtion System delay'], [2, 'security delay'], [2, 'late aircraft delay']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3], 'foreign_keys': [[6, 1], [16, 3], [12, 3]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.CANCELLED = 0 GROUP BY T2.Description ORDER BY COUNT(T1.CANCELLED) DESC LIMIT 1"}],"db_id":"airline"}907{"prompt":"Which game has the longest name?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which game has the longest name?\n        \n        ### Context:\n        #### Query Context:\n        game refers to game_name; the longest name refers to max(length(game_name))\n        \n        #### Database Schema:\n        {'db_id': 'video_games', 'table_names_original': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'table_names': ['genre', 'game', 'platform', 'publisher', 'game_publisher', 'game_platform', 'region', 'region_sales'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre_id'], [1, 'game_name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game_id'], [4, 'publisher_id'], [5, 'id'], [5, 'game_publisher_id'], [5, 'platform_id'], [5, 'release_year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game_platform_id'], [7, 'num_sales']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'genre_name'], [1, 'id'], [1, 'genre id'], [1, 'game name'], [2, 'id'], [2, 'platform_name'], [3, 'id'], [3, 'publisher_name'], [4, 'id'], [4, 'game id'], [4, 'publisher id'], [5, 'id'], [5, 'game publisher id'], [5, 'platform id'], [5, 'release year'], [6, 'id'], [6, 'region_name'], [7, 'region_id'], [7, 'game platform id'], [7, 'number sales']], 'column_types': ['text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 3, 6, 8, 10, 13, 17], 'foreign_keys': [[4, 1], [12, 8], [11, 3], [15, 6], [14, 10], [19, 17], [20, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.game_name FROM game AS T ORDER BY LENGTH(T.game_name) DESC LIMIT 1"}],"db_id":"video_games"}908{"prompt":"What is the episode ID that received 2 stars and 9 votes?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the episode ID that received 2 stars and 9 votes?\n        \n        ### Context:\n        #### Query Context:\n        2 stars refers to stars = 2; 9 votes refers to votes = 9\n        \n        #### Database Schema:\n        {'db_id': 'simpson_episodes', 'table_names_original': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'table_names': ['Episode', 'Person', 'Award', 'Character_Award', 'Credit', 'Keyword', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birthdate'], [1, 'birth_name'], [1, 'birth_place'], [1, 'birth_region'], [1, 'birth_country'], [1, 'height_meters'], [1, 'nickname'], [2, 'award_id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode_id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award_id'], [3, 'character'], [4, 'episode_id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'keyword'], [6, 'episode_id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'name'], [1, 'birth date'], [1, 'birth name'], [1, 'birth place'], [1, 'birth region'], [1, 'birth country'], [1, 'height meters'], [1, 'nickname'], [2, 'award id'], [2, 'organization'], [2, 'year'], [2, 'award_category'], [2, 'award'], [2, 'person'], [2, 'role'], [2, 'episode id'], [2, 'season'], [2, 'song'], [2, 'result'], [3, 'award id'], [3, 'character'], [4, 'episode id'], [4, 'category'], [4, 'person'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'keyword'], [6, 'episode id'], [6, 'stars'], [6, 'votes'], [6, 'percent']], 'column_types': ['text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, 11, 19, [37, 38]], 'foreign_keys': [[26, 1], [24, 11], [30, 19], [34, 11], [32, 1], [37, 1], [39, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT episode_id FROM Vote WHERE stars = 2 AND votes = 9;"}],"db_id":"simpson_episodes"}909{"prompt":"Among the players that weigh more than 90 kg, how many of them have a position of defense?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the players that weigh more than 90 kg, how many of them have a position of defense?\n        \n        ### Context:\n        #### Query Context:\n        weigh more than 90 kg refers to weight_in_kg > 90; position of defense refers to position_info = 'D' ;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.ELITEID) FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id WHERE T2.weight_in_kg > 90 AND T1.position_info = 'D'"}],"db_id":"ice_hockey_draft"}910{"prompt":"Among the lists with at least one follower, how many were created by user who was subscriber when created the list?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the lists with at least one follower, how many were created by user who was subscriber when created the list?\n        \n        ### Context:\n        #### Query Context:\n        lists with at least one follower refers to list_followers > = 1; was a subscriber refers to user_subscriber = 1\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.list_id) FROM lists_users AS T1 INNER JOIN lists AS T2 ON T1.list_id = T2.list_id WHERE T2.list_followers >= 1 AND T1.user_subscriber = 1"}],"db_id":"movie_platform"}911{"prompt":"Tweets posted from which city has a higher number of average likes, Bangkok or Chiang Mai?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Tweets posted from which city has a higher number of average likes, Bangkok or Chiang Mai?\n        \n        ### Context:\n        #### Query Context:\n        \"Bangkok\" and \"Chiang Mai\" are both City; average number of like = Divide (Sum(Likes), Count(TweetID))\n        \n        #### Database Schema:\n        {'db_id': 'social_media', 'table_names_original': ['location', 'user', 'twitter'], 'table_names': ['location', 'user', 'twitter'], 'column_names_original': [[-1, '*'], [0, 'LocationID'], [0, 'Country'], [0, 'State'], [0, 'StateCode'], [0, 'City'], [1, 'UserID'], [1, 'Gender'], [2, 'TweetID'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'Lang'], [2, 'IsReshare'], [2, 'Reach'], [2, 'RetweetCount'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'LocationID'], [2, 'UserID']], 'column_names': [[-1, '*'], [0, 'location id'], [0, 'Country'], [0, 'State'], [0, 'state code'], [0, 'City'], [1, 'user id'], [1, 'Gender'], [2, 'tweet id'], [2, 'Weekday'], [2, 'Hour'], [2, 'Day'], [2, 'language'], [2, 'is reshare'], [2, 'Reach'], [2, 'retweet count'], [2, 'Likes'], [2, 'Klout'], [2, 'Sentiment'], [2, 'text'], [2, 'location id'], [2, 'user id']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'real', 'text', 'integer', 'text'], 'primary_keys': [1, 6, 8], 'foreign_keys': [[21, 6], [20, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.City = 'Bangkok' THEN Likes ELSE NULL END) \/ COUNT(CASE WHEN T2.City = 'Bangkok' THEN 1 ELSE 0 END) AS bNum , SUM(CASE WHEN City = 'Chiang Mai' THEN Likes ELSE NULL END) \/ COUNT(CASE WHEN City = 'Chiang Mai' THEN TweetID ELSE NULL END) AS cNum FROM twitter AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID WHERE T2.City IN ('Bangkok', 'Chiang Mai')"}],"db_id":"social_media"}912{"prompt":"For the user who joined Yelp in \"2010\", with an average of \"4.5\" stars review and has got uber number of fans, how many \"funny\" compliments has he\/she received from other users?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For the user who joined Yelp in \"2010\", with an average of \"4.5\" stars review and has got uber number of fans, how many \"funny\" compliments has he\/she received from other users?\n        \n        ### Context:\n        #### Query Context:\n        in \"2010\" refers to user_yelping_since_year = '2010'; average of \"4.5\" stars review refers to user_average_stars = '4.5'; uber number of fans refers to user_average_stars = '4.5'; \"funny\" compliments refers to compliment_type = 'funny'\n        \n        #### Database Schema:\n        {'db_id': 'public_review_platform', 'table_names_original': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'table_names': ['Attributes', 'Categories', 'Compliments', 'Days', 'Years', 'Business_Attributes', 'Business_Categories', 'Business_Hours', 'Checkins', 'Elite', 'Reviews', 'Tips', 'Users_Compliments', 'Business', 'Users'], 'column_names_original': [[-1, '*'], [0, 'attribute_id'], [0, 'attribute_name'], [1, 'category_id'], [1, 'category_name'], [2, 'compliment_id'], [2, 'compliment_type'], [3, 'day_id'], [3, 'day_of_week'], [4, 'year_id'], [4, 'actual_year'], [5, 'attribute_id'], [5, 'business_id'], [5, 'attribute_value'], [6, 'business_id'], [6, 'category_id'], [7, 'business_id'], [7, 'day_id'], [7, 'opening_time'], [7, 'closing_time'], [8, 'business_id'], [8, 'day_id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user_id'], [9, 'year_id'], [10, 'business_id'], [10, 'user_id'], [10, 'review_stars'], [10, 'review_votes_funny'], [10, 'review_votes_useful'], [10, 'review_votes_cool'], [10, 'review_length'], [11, 'business_id'], [11, 'user_id'], [11, 'likes'], [11, 'tip_length'], [12, 'compliment_id'], [12, 'user_id'], [12, 'number_of_compliments'], [13, 'business_id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review_count'], [14, 'user_id'], [14, 'user_yelping_since_year'], [14, 'user_average_stars'], [14, 'user_votes_funny'], [14, 'user_votes_useful'], [14, 'user_votes_cool'], [14, 'user_review_count'], [14, 'user_fans']], 'column_names': [[-1, '*'], [0, 'attribute id'], [0, 'attribute name'], [1, 'category id'], [1, 'category name'], [2, 'compliment id'], [2, 'compliment type'], [3, 'day id'], [3, 'day of week'], [4, 'year id'], [4, 'actual year'], [5, 'attribute id'], [5, 'business id'], [5, 'attribute value'], [6, 'business id'], [6, 'category id'], [7, 'business id'], [7, 'day id'], [7, 'opening time'], [7, 'closing time'], [8, 'business id'], [8, 'day id'], [8, 'label_time_0'], [8, 'label_time_1'], [8, 'label_time_2'], [8, 'label_time_3'], [8, 'label_time_4'], [8, 'label_time_5'], [8, 'label_time_6'], [8, 'label_time_7'], [8, 'label_time_8'], [8, 'label_time_9'], [8, 'label_time_10'], [8, 'label_time_11'], [8, 'label_time_12'], [8, 'label_time_13'], [8, 'label_time_14'], [8, 'label_time_15'], [8, 'label_time_16'], [8, 'label_time_17'], [8, 'label_time_18'], [8, 'label_time_19'], [8, 'label_time_20'], [8, 'label_time_21'], [8, 'label_time_22'], [8, 'label_time_23'], [9, 'user id'], [9, 'year id'], [10, 'business id'], [10, 'user id'], [10, 'review stars'], [10, 'review votes funny'], [10, 'review votes useful'], [10, 'review votes cool'], [10, 'review length'], [11, 'business id'], [11, 'user id'], [11, 'Likes'], [11, 'tip length'], [12, 'compliment id'], [12, 'user id'], [12, 'number of compliments'], [13, 'business id'], [13, 'active'], [13, 'city'], [13, 'state'], [13, 'stars'], [13, 'review count'], [14, 'user id'], [14, 'user yelping since year'], [14, 'user average stars'], [14, 'user votes funny'], [14, 'user votes useful'], [14, 'user votes cool'], [14, 'user review count'], [14, 'user fans']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'real', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 3, 5, 7, 9, [11, 12], [14, 15], [16, 17], [20, 21], [46, 47], [48, 49], [55, 56], [59, 60], 62, 68], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T2.user_id) FROM Users AS T1 INNER JOIN Users_Compliments AS T2 ON T1.user_id = T2.user_id INNER JOIN Compliments AS T3 ON T2.compliment_id = T3.compliment_id WHERE T1.user_yelping_since_year = 2010 AND T1.user_average_stars = 4.5 AND T1.user_fans = 'Uber' AND T3.compliment_type = 'funny'"}],"db_id":"public_review_platform"}913{"prompt":"Which sales areas are expected to have the highest yearly sales quota?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which sales areas are expected to have the highest yearly sales quota?\n        \n        ### Context:\n        #### Query Context:\n        highest yearly sales quota refers to Max(SalesQuota);\n        \n        #### Database Schema:\n        {'db_id': 'works_cycles', 'table_names_original': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'table_names': ['CountryRegion', 'Culture', 'Currency', 'CountryRegionCurrency', 'Person', 'BusinessEntityContact', 'EmailAddress', 'Employee', 'Password', 'PersonCreditCard', 'ProductCategory', 'ProductDescription', 'ProductModel', 'ProductModelProductDescriptionCulture', 'ProductPhoto', 'ProductSubcategory', 'SalesReason', 'SalesTerritory', 'SalesPerson', 'SalesPersonQuotaHistory', 'SalesTerritoryHistory', 'ScrapReason', 'Shift', 'ShipMethod', 'SpecialOffer', 'BusinessEntityAddress', 'SalesTaxRate', 'Store', 'SalesOrderHeaderSalesReason', 'TransactionHistoryArchive', 'UnitMeasure', 'ProductCostHistory', 'ProductDocument', 'ProductInventory', 'ProductProductPhoto', 'ProductReview', 'ShoppingCartItem', 'SpecialOfferProduct', 'SalesOrderDetail', 'TransactionHistory', 'Vendor', 'ProductVendor', 'PurchaseOrderHeader', 'PurchaseOrderDetail', 'WorkOrder', 'WorkOrderRouting', 'Customer', 'ProductListPriceHistory', 'Address', 'AddressType', 'BillOfMaterials', 'BusinessEntity', 'ContactType', 'CurrencyRate', 'Department', 'EmployeeDepartmentHistory', 'EmployeePayHistory', 'JobCandidate', 'Location', 'PhoneNumberType', 'Product', 'Document', 'StateProvince', 'CreditCard', 'SalesOrderHeader'], 'column_names_original': [[-1, '*'], [0, 'CountryRegionCode'], [0, 'Name'], [0, 'ModifiedDate'], [1, 'CultureID'], [1, 'Name'], [1, 'ModifiedDate'], [2, 'CurrencyCode'], [2, 'Name'], [2, 'ModifiedDate'], [3, 'CountryRegionCode'], [3, 'CurrencyCode'], [3, 'ModifiedDate'], [4, 'BusinessEntityID'], [4, 'PersonType'], [4, 'NameStyle'], [4, 'Title'], [4, 'FirstName'], [4, 'MiddleName'], [4, 'LastName'], [4, 'Suffix'], [4, 'EmailPromotion'], [4, 'AdditionalContactInfo'], [4, 'Demographics'], [4, 'rowguid'], [4, 'ModifiedDate'], [5, 'BusinessEntityID'], [5, 'PersonID'], [5, 'ContactTypeID'], [5, 'rowguid'], [5, 'ModifiedDate'], [6, 'BusinessEntityID'], [6, 'EmailAddressID'], [6, 'EmailAddress'], [6, 'rowguid'], [6, 'ModifiedDate'], [7, 'BusinessEntityID'], [7, 'NationalIDNumber'], [7, 'LoginID'], [7, 'OrganizationNode'], [7, 'OrganizationLevel'], [7, 'JobTitle'], [7, 'BirthDate'], [7, 'MaritalStatus'], [7, 'Gender'], [7, 'HireDate'], [7, 'SalariedFlag'], [7, 'VacationHours'], [7, 'SickLeaveHours'], [7, 'CurrentFlag'], [7, 'rowguid'], [7, 'ModifiedDate'], [8, 'BusinessEntityID'], [8, 'PasswordHash'], [8, 'PasswordSalt'], [8, 'rowguid'], [8, 'ModifiedDate'], [9, 'BusinessEntityID'], [9, 'CreditCardID'], [9, 'ModifiedDate'], [10, 'ProductCategoryID'], [10, 'Name'], [10, 'rowguid'], [10, 'ModifiedDate'], [11, 'ProductDescriptionID'], [11, 'Description'], [11, 'rowguid'], [11, 'ModifiedDate'], [12, 'ProductModelID'], [12, 'Name'], [12, 'CatalogDescription'], [12, 'Instructions'], [12, 'rowguid'], [12, 'ModifiedDate'], [13, 'ProductModelID'], [13, 'ProductDescriptionID'], [13, 'CultureID'], [13, 'ModifiedDate'], [14, 'ProductPhotoID'], [14, 'ThumbNailPhoto'], [14, 'ThumbnailPhotoFileName'], [14, 'LargePhoto'], [14, 'LargePhotoFileName'], [14, 'ModifiedDate'], [15, 'ProductSubcategoryID'], [15, 'ProductCategoryID'], [15, 'Name'], [15, 'rowguid'], [15, 'ModifiedDate'], [16, 'SalesReasonID'], [16, 'Name'], [16, 'ReasonType'], [16, 'ModifiedDate'], [17, 'TerritoryID'], [17, 'Name'], [17, 'CountryRegionCode'], [17, 'Group'], [17, 'SalesYTD'], [17, 'SalesLastYear'], [17, 'CostYTD'], [17, 'CostLastYear'], [17, 'rowguid'], [17, 'ModifiedDate'], [18, 'BusinessEntityID'], [18, 'TerritoryID'], [18, 'SalesQuota'], [18, 'Bonus'], [18, 'CommissionPct'], [18, 'SalesYTD'], [18, 'SalesLastYear'], [18, 'rowguid'], [18, 'ModifiedDate'], [19, 'BusinessEntityID'], [19, 'QuotaDate'], [19, 'SalesQuota'], [19, 'rowguid'], [19, 'ModifiedDate'], [20, 'BusinessEntityID'], [20, 'TerritoryID'], [20, 'StartDate'], [20, 'EndDate'], [20, 'rowguid'], [20, 'ModifiedDate'], [21, 'ScrapReasonID'], [21, 'Name'], [21, 'ModifiedDate'], [22, 'ShiftID'], [22, 'Name'], [22, 'StartTime'], [22, 'EndTime'], [22, 'ModifiedDate'], [23, 'ShipMethodID'], [23, 'Name'], [23, 'ShipBase'], [23, 'ShipRate'], [23, 'rowguid'], [23, 'ModifiedDate'], [24, 'SpecialOfferID'], [24, 'Description'], [24, 'DiscountPct'], [24, 'Type'], [24, 'Category'], [24, 'StartDate'], [24, 'EndDate'], [24, 'MinQty'], [24, 'MaxQty'], [24, 'rowguid'], [24, 'ModifiedDate'], [25, 'BusinessEntityID'], [25, 'AddressID'], [25, 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89], [166, 430], [181, 377], [187, 402], [186, 377], [190, 369], [189, 377], [197, 78], [196, 377], [201, 377], [211, 377], [215, 377], [214, 137], [223, 214], [222, 215], [218, 430], [230, 377], [238, 337], [255, 178], [247, 238], [246, 377], [262, 131], [261, 238], [260, 36], [274, 377], [270, 257], [289, 123], [282, 377], [294, 369], [291, 281], [305, 160], [306, 93], [304, 13], [310, 377]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name FROM SalesPerson AS T1 INNER JOIN SalesTerritory AS T2 ON T1.TerritoryID = T2.TerritoryID GROUP BY T1.TerritoryID ORDER BY SUM(T1.SalesQuota) DESC LIMIT 1"}],"db_id":"works_cycles"}914{"prompt":"What is the name of the category which most users belong to?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the category which most users belong to?\n        \n        ### Context:\n        #### Query Context:\n        most users belong to refers to MAX(COUNT(app_id)); name of category refers to category;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T.category FROM ( SELECT T2.category, COUNT(T1.app_id) AS num FROM app_labels AS T1 INNER JOIN label_categories AS T2 ON T2.label_id = T1.label_id GROUP BY T1.app_id, T2.category ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"talkingdata"}915{"prompt":"List down the first name of patients who have cystitis condition.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List down the first name of patients who have cystitis condition.\n        \n        ### Context:\n        #### Query Context:\n        cystitis refers to conditions where DESCRIPTION = 'Cystitis';\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.first FROM patients AS T1 INNER JOIN conditions AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'Cystitis'"}],"db_id":"synthea"}916{"prompt":"How many adventure movies are there that were released in 2000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many adventure movies are there that were released in 2000?\n        \n        ### Context:\n        #### Query Context:\n        adventure movies refers to genre_name = 'Adventure'; released in 2000 refers to release_date LIKE '2000%'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.movie_id) FROM movie AS T1 INNER JOIN movie_genres AS T2 ON T1.movie_id = T2.movie_id INNER JOIN genre AS T3 ON T2.genre_id = T3.genre_id WHERE T3.genre_name = 'Adventure' AND CAST(STRFTIME('%Y', T1.release_date) AS INT) = 2000"}],"db_id":"movies_4"}917{"prompt":"What country has the latest trade data with a series code of \"SP.DYN.CDRT.IN\n\"? List the table name of the country.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What country has the latest trade data with a series code of \"SP.DYN.CDRT.IN\n\"? List the table name of the country.\n        \n        ### Context:\n        #### Query Context:\n        the latest trade data refers to LatestTradeData = '2013'; with a series code of \"SP.DYN.CDRT.IN\n\" refers to indicatorcode = 'SP.DYN.CDRT.IN'\n        \n        #### Database Schema:\n        {'db_id': 'world_development_indicators', 'table_names_original': ['Country', 'Series', 'CountryNotes', 'Footnotes', 'Indicators', 'SeriesNotes'], 'table_names': ['Country', 'Series', 'CountryNotes', 'FootNotes', 'Indicators', 'SeriesNotes'], 'column_names_original': [[-1, '*'], [0, 'CountryCode'], [0, 'ShortName'], [0, 'TableName'], [0, 'LongName'], [0, 'Alpha2Code'], [0, 'CurrencyUnit'], [0, 'SpecialNotes'], [0, 'Region'], [0, 'IncomeGroup'], [0, 'Wb2Code'], [0, 'NationalAccountsBaseYear'], [0, 'NationalAccountsReferenceYear'], [0, 'SnaPriceValuation'], [0, 'LendingCategory'], [0, 'OtherGroups'], [0, 'SystemOfNationalAccounts'], [0, 'AlternativeConversionFactor'], [0, 'PppSurveyYear'], [0, 'BalanceOfPaymentsManualInUse'], [0, 'ExternalDebtReportingStatus'], [0, 'SystemOfTrade'], [0, 'GovernmentAccountingConcept'], [0, 'ImfDataDisseminationStandard'], [0, 'LatestPopulationCensus'], [0, 'LatestHouseholdSurvey'], [0, 'SourceOfMostRecentIncomeAndExpenditureData'], [0, 'VitalRegistrationComplete'], [0, 'LatestAgriculturalCensus'], [0, 'LatestIndustrialData'], [0, 'LatestTradeData'], [0, 'LatestWaterWithdrawalData'], [1, 'SeriesCode'], [1, 'Topic'], [1, 'IndicatorName'], [1, 'ShortDefinition'], [1, 'LongDefinition'], [1, 'UnitOfMeasure'], [1, 'Periodicity'], [1, 'BasePeriod'], [1, 'OtherNotes'], [1, 'AggregationMethod'], [1, 'LimitationsAndExceptions'], [1, 'NotesFromOriginalSource'], [1, 'GeneralComments'], [1, 'Source'], [1, 'StatisticalConceptAndMethodology'], [1, 'DevelopmentRelevance'], [1, 'RelatedSourceLinks'], [1, 'OtherWebLinks'], [1, 'RelatedIndicators'], [1, 'LicenseType'], [2, 'Countrycode'], [2, 'Seriescode'], [2, 'Description'], [3, 'Countrycode'], [3, 'Seriescode'], [3, 'Year'], [3, 'Description'], [4, 'CountryName'], [4, 'CountryCode'], [4, 'IndicatorName'], [4, 'IndicatorCode'], [4, 'Year'], [4, 'Value'], [5, 'Seriescode'], [5, 'Year'], [5, 'Description']], 'column_names': [[-1, '*'], [0, 'Country Code'], [0, 'Short Name'], [0, 'Table Name'], [0, 'Long Name'], [0, 'Alpha to Code'], [0, 'Currency Unit'], [0, 'Special Notes'], [0, 'Region'], [0, 'Income Group'], [0, 'world bank to code'], [0, 'National Accounts Base Year'], [0, 'National Accounts Reference Year'], [0, 'SNA Price Valuation'], [0, 'Lending Category'], [0, 'Other groups'], [0, 'System Of National Accounts'], [0, 'Alternative Conversion Factor'], [0, 'purchasing power parity survey year'], [0, 'Balance Of Payments Manual In Use'], [0, 'External Debt Reporting Status'], [0, 'System Of Trade'], [0, 'Government Accounting Concept'], [0, 'International Monetory Fund Data Dissemination Standard'], [0, 'Latest Population Census'], [0, 'Latest Household Survey'], [0, 'Source Of Most Recent Income And Expenditure Data'], [0, 'Vital Registration Complete'], [0, 'Latest Agricultural Census'], [0, 'Latest Industrial Data'], [0, 'Latest Trade Data'], [0, 'Latest Water Withdrawal Data'], [1, 'Series Code'], [1, 'Topic'], [1, 'Indicator Name'], [1, 'Short Definition'], [1, 'Long Definition'], [1, 'Unit Of Measure'], [1, 'Periodicity'], [1, 'Base Period'], [1, 'Other Notes'], [1, 'Aggregation Method'], [1, 'Limitations And Exceptions'], [1, 'Notes From Original Source'], [1, 'General Comments'], [1, 'Source'], [1, 'Statistical Concept And Methodology'], [1, 'Development Relevance'], [1, 'Related Source Links'], [1, 'Other Web Links'], [1, 'Related Indicators'], [1, 'License Type'], [2, 'Country code'], [2, 'Series code'], [2, 'Description'], [3, 'Country code'], [3, 'Series code'], [3, 'Year'], [3, 'Description'], [4, 'Country code'], [4, 'Series code'], [4, 'Indicator Name'], [4, 'Indicator Code'], [4, 'Year'], [4, 'Value'], [5, 'Series code'], [5, 'Year'], [5, 'Description']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text'], 'primary_keys': [1, 32, [52, 53], [55, 56, 57], [60, 62, 63], [65, 66]], 'foreign_keys': [[52, 1], [53, 32], [55, 1], [56, 32], [60, 1], [65, 32]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T1.TableName FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.LatestTradeData = 2013 AND T2.IndicatorCode = 'SP.DYN.CDRT.IN'"}],"db_id":"world_development_indicators"}918{"prompt":"Among the coaches who have gotten in the Hall of Fame, how many of them have a weight of over 195?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the coaches who have gotten in the Hall of Fame, how many of them have a weight of over 195?\n        \n        ### Context:\n        #### Query Context:\n        weight of over 195 refers to weight>195\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], [5, 'nameNick'], [5, 'height'], [5, 'weight'], [5, 'shootCatch'], [5, 'legendsID'], [5, 'ihdbID'], [5, 'hrefID'], [5, 'firstNHL'], [5, 'lastNHL'], [5, 'firstWHA'], [5, 'lastWHA'], [5, 'pos'], [5, 'birthYear'], [5, 'birthMon'], [5, 'birthDay'], [5, 'birthCountry'], [5, 'birthState'], [5, 'birthCity'], [5, 'deathYear'], [5, 'deathMon'], [5, 'deathDay'], [5, 'deathCountry'], [5, 'deathState'], [5, 'deathCity'], [6, 'playerID'], [6, 'award'], [6, 'year'], [6, 'lgID'], [6, 'note'], [6, 'pos'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'tmID'], [7, 'oppID'], [7, 'R\/P'], [7, 'IDgoalie1'], [7, 'IDgoalie2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'tmID'], [8, 'lgID'], [8, 'GP'], [8, 'Min'], [8, 'W'], [8, 'L'], [8, 'T\/OL'], [8, 'ENG'], [8, 'SHO'], [8, 'GA'], [8, 'SA'], [8, 'PostGP'], [8, 'PostMin'], [8, 'PostW'], [8, 'PostL'], [8, 'PostT'], [8, 'PostENG'], [8, 'PostSHO'], [8, 'PostGA'], [8, 'PostSA'], [9, 'playerID'], [9, 'year'], [9, 'tmID'], [9, 'lgID'], [9, 'GP'], [9, 'Min'], [9, 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[15, 'lgIDLoser'], [15, 'W'], [15, 'L'], [15, 'T'], [15, 'GoalsWinner'], [15, 'GoalsLoser'], [15, 'note'], [16, 'year'], [16, 'lgID'], [16, 'tmID'], [16, 'hW'], [16, 'hL'], [16, 'hT'], [16, 'hOTL'], [16, 'rW'], [16, 'rL'], [16, 'rT'], [16, 'rOTL'], [16, 'SepW'], [16, 'SepL'], [16, 'SepT'], [16, 'SepOL'], [16, 'OctW'], [16, 'OctL'], [16, 'OctT'], [16, 'OctOL'], [16, 'NovW'], [16, 'NovL'], [16, 'NovT'], [16, 'NovOL'], [16, 'DecW'], [16, 'DecL'], [16, 'DecT'], [16, 'DecOL'], [16, 'JanW'], [16, 'JanL'], [16, 'JanT'], [16, 'JanOL'], [16, 'FebW'], [16, 'FebL'], [16, 'FebT'], [16, 'FebOL'], [16, 'MarW'], [16, 'MarL'], [16, 'MarT'], [16, 'MarOL'], [16, 'AprW'], [16, 'AprL'], [16, 'AprT'], [16, 'AprOL'], [17, 'year'], [17, 'lgID'], [17, 'tmID'], [17, 'oppID'], [17, 'W'], [17, 'L'], [17, 'T'], [17, 'OTL'], [18, 'year'], [18, 'lgID'], [18, 'tmID'], [18, 'half'], [18, 'rank'], [18, 'G'], [18, 'W'], [18, 'L'], [18, 'T'], [18, 'GF'], [18, 'GA'], [19, 'year'], [19, 'lgID'], [19, 'tmID'], [19, 'G'], [19, 'W'], [19, 'L'], [19, 'T'], [19, 'GF'], [19, 'GA'], [19, 'PIM'], [19, 'BenchMinor'], [19, 'PPG'], [19, 'PPC'], [19, 'SHA'], [19, 'PKG'], [19, 'PKC'], [19, 'SHF'], [20, 'year'], [20, 'lgID'], [20, 'tmID'], [20, 'G'], [20, 'W'], [20, 'L'], [20, 'T'], [20, 'GF'], [20, 'GA'], [20, 'PIM'], [21, 'Type'], [21, 'Code'], [21, 'Fullname']], 'column_names': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'league ID'], [0, 'note'], [1, 'year'], [1, 'hall of fame id'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'league ID'], [2, 'team ID'], [2, 'Franchise ID'], [2, 'Conference ID'], [2, 'Division ID'], [2, 'rank'], [2, 'playoff'], [2, 'games'], [2, 'wins'], [2, 'loses'], [2, 'ties'], [2, 'Overtime losses'], [2, 'points'], [2, 'Shootout wins'], [2, 'Shootout loses'], [2, 'Goals for'], [2, 'Goals against'], [2, 'Full team name'], [2, 'Penalty minutes'], [2, 'Bench minors (minutes)'], [2, 'Power play goals'], [2, 'Power play chances'], [2, 'Shorthanded goals against'], [2, 'Power play goals against'], [2, 'Penalty kill chances'], [2, 'Shorthanded goals for'], [3, 'coach ID'], [3, 'year'], [3, 'team ID'], [3, 'league ID'], [3, 'stint'], [3, 'notes'], [3, 'games'], [3, 'wins'], [3, 'loses'], [3, 'ties'], [3, 'post-season games'], [3, 'post-season wins'], [3, 'post-season loses'], [3, 'post-season ties'], [4, 'coach ID'], [4, 'award'], [4, 'year'], [4, 'league ID'], [4, 'note'], [5, 'playerID'], [5, 'coach ID'], [5, 'hofID'], [5, 'first name'], [5, 'last name'], [5, 'name note'], [5, 'name given'], [5, 'Nickname'], [5, 'height'], [5, 'weight'], [5, 'shoot catch'], [5, 'legends ID'], [5, 'Internet Hockey Database ID'], [5, 'Hockey-Reference.com ID'], [5, 'First NHL season'], [5, 'Last NHL season'], [5, 'First WHA season'], [5, 'Last WHA season'], [5, 'position'], [5, 'birth Year'], [5, 'birth Month'], [5, 'birth Day'], [5, 'birth Country'], [5, 'birth State'], [5, 'birth city'], [5, 'death year'], [5, 'death month'], [5, 'death day'], [5, 'death country'], [5, 'death state'], [5, 'death city'], [6, 'player id'], [6, 'award'], [6, 'year'], [6, 'league ID'], [6, 'note'], [6, 'position'], [7, 'year'], [7, 'month'], [7, 'date'], [7, 'team ID'], [7, 'opposite team ID'], [7, 'regular \/ postseason'], [7, 'ID of goalie 1'], [7, 'ID of goalie 2'], [8, 'playerID'], [8, 'year'], [8, 'stint'], [8, 'team ID'], [8, 'league ID'], [8, 'Games played'], [8, 'Minutes'], [8, 'wins'], [8, 'loses'], [8, 'Ties \/ overtime losses'], [8, 'Empty net goals'], [8, 'Shutouts'], [8, 'Goals against'], [8, 'Shots against'], [8, 'Postseason games played'], [8, 'Postseason minutes'], [8, 'Postseason wins'], [8, 'Postseason loses'], [8, 'Postseason ties'], [8, 'Postseason empty net goals'], [8, 'Postseason Shutouts'], [8, 'Postseason Goals against'], [8, 'Postseason Shots against'], [9, 'playerID'], [9, 'year'], [9, 'team ID'], [9, 'league ID'], [9, 'Games played'], [9, 'Minutes'], [9, 'Wins'], [9, 'Loses'], [9, 'Ties'], [9, 'Shutouts'], [9, 'Goals against'], [10, 'playerID'], [10, 'year'], [10, 'stint'], [10, 'team ID'], [10, 'Wins'], [10, 'Loses'], [10, 'Shots against'], [10, 'Goals against'], [11, 'playerID'], [11, 'year'], [11, 'stint'], [11, 'team id'], [11, 'league id'], [11, 'position'], [11, 'game played'], [11, 'goals'], [11, 'assists'], [11, 'points'], [11, 'Penalty minutes'], [11, 'Plus \/ minus'], [11, 'Power play goals'], [11, 'Power play assists'], [11, 'Shorthanded goals'], [11, 'Shorthanded assists'], [11, 'Game-winning goals'], [11, 'Game-tying goals'], [11, 'Shots on goal'], [11, 'Postseason games played'], [11, 'Postseason goals'], [11, 'Postseason assists'], [11, 'Postseason points'], [11, 'Postseason penalty minutes'], [11, 'Postseason Plus \/ minus'], [11, 'Postseason power play goals'], [11, 'Postseason power play assists'], [11, 'Postseason Shorthanded goals'], [11, 'Postseason Shorthanded assists'], [11, 'Postseason game-winning goals'], [11, 'Postseason shots on goal'], [12, 'playerID'], [12, 'year'], [12, 'team ID'], 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ties'], [16, 'October overtime loses'], [16, 'November wins'], [16, 'November loses'], [16, 'November ties'], [16, 'November overtime loses'], [16, 'December wins'], [16, 'December loses'], [16, 'December ties'], [16, 'December overtime loses'], [16, 'January wins'], [16, 'January loses'], [16, 'January ties'], [16, 'January overtime loses'], [16, 'February wins'], [16, 'February loses'], [16, 'February ties'], [16, 'February overtime loses'], [16, 'March wins'], [16, 'March loses'], [16, 'March ties'], [16, 'March overtime loses'], [16, 'April wins'], [16, 'April loses'], [16, 'April ties'], [16, 'April overtime loses'], [17, 'year'], [17, 'league ID'], [17, 'team ID'], [17, 'opponent ID'], [17, 'wins'], [17, 'loses'], [17, 'ties'], [17, 'overtime loses'], [18, 'year'], [18, 'league ID'], [18, 'team ID'], [18, 'half'], [18, 'rank'], [18, 'Games'], [18, 'wins'], [18, 'loses'], [18, 'ties'], [18, 'goals for'], [18, 'goals against'], [19, 'year'], [19, 'league ID'], [19, 'team ID'], [19, 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'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text'], 'primary_keys': [1, 8, [11, 13], [38, 39, 40, 42], [88, 89, 90], [102, 103, 104], [125, 126], [209, 211], [252, 254, 255], [260, 262, 263], [271, 273], [288, 290], [298, 299]], 'foreign_keys': [[39, 11], [40, 13], [52, 38], [58, 38], [88, 57], [101, 57], [100, 57], [102, 57], [103, 11], [105, 13], [125, 57], [126, 11], [127, 13], [136, 57], [137, 11], [139, 13], [144, 57], [145, 11], [147, 13], [175, 57], [176, 11], [177, 13], [185, 57], [186, 11], [188, 13], [192, 57], [196, 11], [201, 13], [196, 11], [199, 13], [209, 11], [211, 13], [255, 13], [252, 11], [252, 11], [254, 13], [262, 13], [260, 11], [271, 11], [273, 13], [288, 11], [290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(DISTINCT T1.coachID) FROM Master AS T1 INNER JOIN HOF AS T2 ON T1.hofID = T2.hofID WHERE T1.weight > 195"}],"db_id":"hockey"}919{"prompt":"For countries with area between 500000 to 1000000, state the country and infant mortality rate.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        For countries with area between 500000 to 1000000, state the country and infant mortality rate.\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.Name, T2.Infant_Mortality FROM country AS T1 INNER JOIN population AS T2 ON T1.Code = T2.Country WHERE T1.Area BETWEEN 500000 AND 1000000"}],"db_id":"mondial_geo"}920{"prompt":"What the percentage of the english methods among the methods whose comments is XML format?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What the percentage of the english methods among the methods whose comments is XML format?\n        \n        ### Context:\n        #### Query Context:\n        english methods refers to lang = 'en'; comment is XML format refers to CommentIsXml = 1;\n        \n        #### Database Schema:\n        {'db_id': 'codebase_comments', 'table_names_original': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'table_names': ['Method', 'MethodParameter', 'Repo', 'Solution'], 'column_names_original': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'FullComment'], [0, 'Summary'], [0, 'ApiCalls'], [0, 'CommentIsXml'], [0, 'SampledAt'], [0, 'SolutionId'], [0, 'Lang'], [0, 'NameTokenized'], [1, 'Id'], [1, 'MethodId'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'ProcessedTime'], [3, 'Id'], [3, 'RepoId'], [3, 'Path'], [3, 'ProcessedTime'], [3, 'WasCompiled']], 'column_names': [[-1, '*'], [0, 'Id'], [0, 'Name'], [0, 'Full Comment'], [0, 'Summary'], [0, 'Api Calls'], [0, 'CommentIs Xml'], [0, 'Sampled At'], [0, 'Solution Id'], [0, 'Language'], [0, 'Name Tokenized'], [1, 'Id'], [1, 'Method Id'], [1, 'Type'], [1, 'Name'], [2, 'Id'], [2, 'Url'], [2, 'Stars'], [2, 'Forks'], [2, 'Watchers'], [2, 'Processed Time'], [3, 'Id'], [3, 'Repository Id'], [3, 'Path'], [3, 'Processed Time'], [3, 'Was Compiled']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer'], 'primary_keys': [1, 11, 15, 21], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN Lang = 'en' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(Name) FROM Method WHERE CommentIsXml = 1"}],"db_id":"codebase_comments"}921{"prompt":"How many apps were last updated in January of 2018? Please write one translated review with positive sentiment for each app, if there's any.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many apps were last updated in January of 2018? Please write one translated review with positive sentiment for each app, if there's any.\n        \n        ### Context:\n        #### Query Context:\n        updated in January of 2018 refers to Last Updated BETWEEN 'January 1, 2018' and 'January 31, 2018';\n        \n        #### Database Schema:\n        {'db_id': 'app_store', 'table_names_original': ['playstore', 'user_reviews'], 'table_names': ['googleplaystore', 'googleplaystore_user_reviews'], 'column_names_original': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment_Polarity'], [1, 'Sentiment_Subjectivity']], 'column_names': [[-1, '*'], [0, 'App'], [0, 'Category'], [0, 'Rating'], [0, 'Reviews'], [0, 'Size'], [0, 'Installs'], [0, 'Type'], [0, 'Price'], [0, 'Content Rating'], [0, 'Genres'], [1, 'App'], [1, 'Translated_Review'], [1, 'Sentiment'], [1, 'Sentiment Polarity'], [1, 'Sentiment Subjectivity']], 'column_types': ['text', 'text', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text'], 'primary_keys': [], 'foreign_keys': [[11, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT Translated_Review FROM user_reviews WHERE App IN ( SELECT App FROM playstore WHERE `Last Updated` BETWEEN 'January 1, 2018' AND 'January 31, 2018' ) AND Sentiment = 'Positive'"}],"db_id":"app_store"}922{"prompt":"Which department has the most disabled students?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which department has the most disabled students?\n        \n        ### Context:\n        #### Query Context:\n        department refers to organ; most disabled students refers to MAX(disabled.name)\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.organ, COUNT(T1.name) FROM disabled AS T1 INNER JOIN enlist AS T2 ON T1.`name` = T2.`name` GROUP BY T2.organ ORDER BY COUNT(T1.name) DESC LIMIT 1"}],"db_id":"student_loan"}923{"prompt":"Which zip code in Massachusetts that have more than 1 area code?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which zip code in Massachusetts that have more than 1 area code?\n        \n        ### Context:\n        #### Query Context:\n        \"Massachusetts\" is the state; zip code more than 1 area code refers to Count (zip_code) > 1\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.zip_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.state = 'MA' GROUP BY T1.zip_code HAVING COUNT(T1.area_code) > 1"}],"db_id":"address"}924{"prompt":"What is the name of the team that got more wins than loses in the Stanley Cup finals in 1917?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the name of the team that got more wins than loses in the Stanley Cup finals in 1917?\n        \n        ### Context:\n        #### Query Context:\n        more wins than loses refers to W>L; year = 1917;\n        \n        #### Database Schema:\n        {'db_id': 'hockey', 'table_names_original': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'table_names': ['AwardsMisc', 'HOF', 'Teams', 'Coaches', 'AwardsCoaches', 'Master', 'AwardsPlayers', 'CombinedShutouts', 'Goalies', 'GoaliesSC', 'GoaliesShootout', 'Scoring', 'ScoringSC', 'ScoringShootout', 'ScoringSup', 'SeriesPost', 'TeamSplits', 'TeamVsTeam', 'TeamsHalf', 'TeamsPost', 'TeamsSC', 'abbrev'], 'column_names_original': [[-1, '*'], [0, 'name'], [0, 'ID'], [0, 'award'], [0, 'year'], [0, 'lgID'], [0, 'note'], [1, 'year'], [1, 'hofID'], [1, 'name'], [1, 'category'], [2, 'year'], [2, 'lgID'], [2, 'tmID'], [2, 'franchID'], [2, 'confID'], [2, 'divID'], [2, 'rank'], [2, 'playoff'], [2, 'G'], [2, 'W'], [2, 'L'], [2, 'T'], [2, 'OTL'], [2, 'Pts'], [2, 'SoW'], [2, 'SoL'], [2, 'GF'], [2, 'GA'], [2, 'name'], [2, 'PIM'], [2, 'BenchMinor'], [2, 'PPG'], [2, 'PPC'], [2, 'SHA'], [2, 'PKG'], [2, 'PKC'], [2, 'SHF'], [3, 'coachID'], [3, 'year'], [3, 'tmID'], [3, 'lgID'], [3, 'stint'], [3, 'notes'], [3, 'g'], [3, 'w'], [3, 'l'], [3, 't'], [3, 'postg'], [3, 'postw'], [3, 'postl'], [3, 'postt'], [4, 'coachID'], [4, 'award'], [4, 'year'], [4, 'lgID'], [4, 'note'], [5, 'playerID'], [5, 'coachID'], [5, 'hofID'], [5, 'firstName'], [5, 'lastName'], [5, 'nameNote'], [5, 'nameGiven'], 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"},{"role":"assistant","content":"SELECT T2.name FROM TeamsSC AS T1 INNER JOIN Teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.year = '1917' AND T1.W > T1.L"}],"db_id":"hockey"}925{"prompt":"Provide the ID of the university with the highest percentage of female students in 2012.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the ID of the university with the highest percentage of female students in 2012.\n        \n        ### Context:\n        #### Query Context:\n        in 2012 refers to year = 2012; highest percentage of female students\u00a0 refers to MAX(pct_female_students); ID of the university refers to university_id\n        \n        #### Database Schema:\n        {'db_id': 'university', 'table_names_original': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'table_names': ['country', 'ranking_system', 'ranking_criteria', 'university', 'university_ranking_year', 'university_year'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'country_name'], [1, 'id'], [1, 'system_name'], [2, 'id'], [2, 'ranking_system_id'], [2, 'criteria_name'], [3, 'id'], [3, 'country_id'], [3, 'university_name'], [4, 'university_id'], [4, 'ranking_criteria_id'], [4, 'year'], [4, 'score'], [5, 'university_id'], [5, 'year'], [5, 'num_students'], [5, 'student_staff_ratio'], [5, 'pct_international_students'], [5, 'pct_female_students']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'country name'], [1, 'id'], [1, 'system name'], [2, 'id'], [2, 'ranking system id'], [2, 'criteria name'], [3, 'id'], [3, 'country id'], [3, 'university name'], [4, 'university id'], [4, 'ranking criteria id'], [4, 'year'], [4, 'score'], [5, 'university id'], [5, 'year'], [5, 'number of students'], [5, 'student staff ratio'], [5, 'pct internation student'], [5, 'pct female students']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer'], 'primary_keys': [1, 3, 5, 8], 'foreign_keys': [[6, 3], [9, 1], [11, 8], [12, 5], [15, 8]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT university_id FROM university_year WHERE year = 2012 ORDER BY pct_female_students DESC LIMIT 1"}],"db_id":"university"}926{"prompt":"List the food type of the restaurant located in 22779 6th St., Hayward City.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the food type of the restaurant located in 22779 6th St., Hayward City.\n        \n        ### Context:\n        #### Query Context:\n        22779 refers to street_num = 22779; 6th St. refers to street_name = '6th St'\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.food_type FROM location AS T1 INNER JOIN generalinfo AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T1.street_num = 22779 AND T1.street_name = '6th St' AND T2.city = 'hayward'"}],"db_id":"restaurant"}927{"prompt":"How many images have an x-coordinate of 5 and y-coordinate of 5?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many images have an x-coordinate of 5 and y-coordinate of 5?\n        \n        ### Context:\n        #### Query Context:\n        X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID;\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(IMG_ID) FROM IMG_OBJ WHERE X = 5 AND Y = 5"}],"db_id":"image_and_language"}928{"prompt":"What is the full address of Albert's Caf\u00e9?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the full address of Albert's Caf\u00e9?\n        \n        ### Context:\n        #### Query Context:\n        full address = street_num, street_name, city; Albert's Caf\u00e9 refers to label = 'Albert's Caf\u00e9'\n        \n        #### Database Schema:\n        {'db_id': 'restaurant', 'table_names_original': ['geographic', 'generalinfo', 'location'], 'table_names': ['geographic', 'generalinfo', 'location'], 'column_names_original': [[-1, '*'], [0, 'city'], [0, 'county'], [0, 'region'], [1, 'id_restaurant'], [1, 'label'], [1, 'food_type'], [1, 'city'], [1, 'review'], [2, 'id_restaurant'], [2, 'street_num'], [2, 'street_name'], [2, 'city']], 'column_names': [[-1, '*'], [0, 'city'], [0, 'country'], [0, 'region'], [1, 'id restaurant'], [1, 'label'], [1, 'food type'], [1, 'city'], [1, 'review'], [2, 'id restaurant'], [2, 'street number'], [2, 'street name'], [2, 'city']], 'column_types': ['text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'real', 'integer', 'integer', 'text', 'text'], 'primary_keys': [1, 4, 9], 'foreign_keys': [[7, 1], [9, 4], [12, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.street_num, T2.street_name, T1.city FROM generalinfo AS T1 INNER JOIN location AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T1.label = 'Albert''s Caf\u00e9'"}],"db_id":"restaurant"}929{"prompt":"Which students that are in the marines have been absent for 6 months?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Which students that are in the marines have been absent for 6 months?\n        \n        ### Context:\n        #### Query Context:\n        in the marines refers to organ = 'marines'; absent for 6 months refers to month = 6\n        \n        #### Database Schema:\n        {'db_id': 'student_loan', 'table_names_original': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankrupcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'table_names': ['bool', 'person', 'disabled', 'enlist', 'filed_for_bankruptcy', 'longest_absense_from_school', 'male', 'no_payment_due', 'unemployed', 'enrolled'], 'column_names_original': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organ'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_names': [[-1, '*'], [0, 'name'], [1, 'name'], [2, 'name'], [3, 'name'], [3, 'organization'], [4, 'name'], [5, 'name'], [5, 'month'], [6, 'name'], [7, 'name'], [7, 'bool'], [8, 'name'], [9, 'name'], [9, 'school'], [9, 'month']], 'column_types': ['text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 2, 3, 6, 7, 9, 10, 12, [13, 14]], 'foreign_keys': [[3, 2], [4, 2], [6, 2], [7, 2], [9, 2], [11, 1], [10, 2], [12, 2], [13, 2]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.name FROM longest_absense_from_school AS T1 INNER JOIN enlist AS T2 ON T1.`name` = T2.`name` WHERE T2.organ = 'marines' AND T1.`month` = 6"}],"db_id":"student_loan"}930{"prompt":"List out 5 most common conditions for underweight patient.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List out 5 most common conditions for underweight patient.\n        \n        ### Context:\n        #### Query Context:\n        most common condition refers to MAX(COUNT(conditions.DESCRIPTION)); underweight patient refers to MIN(observations.VALUE WHERE observations.DESCRIPTION = 'Body Mass Index');\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT DISTINCT T2.DESCRIPTION, T2.VALUE, T2.UNITS FROM patients AS T1 INNER JOIN observations AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'Body Mass Index' GROUP BY T2.VALUE ORDER BY COUNT(T2.VALUE) LIMIT 5"}],"db_id":"synthea"}931{"prompt":"How much higher is the average rating score of the movie \"Innocence Unprotected\" than the movie \"When Will I Be Loved\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much higher is the average rating score of the movie \"Innocence Unprotected\" than the movie \"When Will I Be Loved\"?\n        \n        ### Context:\n        #### Query Context:\n        Innocence Unprotected' and 'When Will I Be Loved' are movie_title; Average rating score = Divide(Sum(rating_score), Count(rating_id));\n        \n        #### Database Schema:\n        {'db_id': 'movie_platform', 'table_names_original': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'table_names': ['lists', 'movies', 'ratings_users', 'lists_users', 'ratings'], 'column_names_original': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_names': [[-1, '*'], [0, 'user_id'], [0, 'list_id'], [0, 'list_title'], [0, 'list_movie_number'], [0, 'list_update_timestamp_utc'], [0, 'list_creation_timestamp_utc'], [0, 'list_followers'], [0, 'list_url'], [0, 'list_comments'], [0, 'list_description'], [0, 'list_cover_image_url'], [0, 'list_first_image_url'], [0, 'list_second_image_url'], [0, 'list_third_image_url'], [1, 'movie_id'], [1, 'movie_title'], [1, 'movie_release_year'], [1, 'movie_url'], [1, 'movie_title_language'], [1, 'movie_popularity'], [1, 'movie_image_url'], [1, 'director_id'], [1, 'director_name'], [1, 'director_url'], [2, 'user_id'], [2, 'rating_date_utc'], [2, 'user_trialist'], [2, 'user_subscriber'], [2, 'user_avatar_image_url'], [2, 'user_cover_image_url'], [2, 'user_eligible_for_trial'], [2, 'user_has_payment_method'], [3, 'user_id'], [3, 'list_id'], [3, 'list_update_date_utc'], [3, 'list_creation_date_utc'], [3, 'user_trialist'], [3, 'user_subscriber'], [3, 'user_avatar_image_url'], [3, 'user_cover_image_url'], [3, 'user_eligible_for_trial'], [3, 'user_has_payment_method'], [4, 'movie_id'], [4, 'rating_id'], [4, 'rating_url'], [4, 'rating_score'], [4, 'rating_timestamp_utc'], [4, 'critic'], [4, 'critic_likes'], [4, 'critic_comments'], [4, 'user_id'], [4, 'user_trialist'], [4, 'user_subscriber'], [4, 'user_eligible_for_trial'], [4, 'user_has_payment_method']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [2, 15, [33, 34]], 'foreign_keys': [[1, 33], [25, 33], [33, 1], [34, 2], [51, 25], [51, 33], [43, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT SUM(CASE WHEN T2.movie_title = 'Innocence Unprotected' THEN T1.rating_score ELSE 0 END) \/ SUM(CASE WHEN T2.movie_title = 'Innocence Unprotected' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.movie_title = 'When Will I Be Loved' THEN T1.rating_score ELSE 0 END) \/ SUM(CASE WHEN T2.movie_title = 'When Will I Be Loved' THEN 1 ELSE 0 END) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id"}],"db_id":"movie_platform"}932{"prompt":"Among the patients with acute bronchitis, what is the percentage of Asian women?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the patients with acute bronchitis, what is the percentage of Asian women?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(PATIENT where gender = 'F' and race = 'asian'), COUNT(PATIENT)) as percentage where DESCRIPTION = 'Acute bronchitis (disorder)' ;\n        \n        #### Database Schema:\n        {'db_id': 'synthea', 'table_names_original': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'table_names': ['all_prevalences', 'patients', 'encounters', 'allergies', 'careplans', 'conditions', 'immunizations', 'medications', 'observations', 'procedures', 'claims'], 'column_names_original': [[-1, '*'], [0, 'ITEM'], [0, 'POPULATION TYPE'], [0, 'OCCURRENCES'], [0, 'POPULATION COUNT'], [0, 'PREVALENCE RATE'], [0, 'PREVALENCE PERCENTAGE'], [1, 'patient'], [1, 'birthdate'], [1, 'deathdate'], [1, 'ssn'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birthplace'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'REASONDESCRIPTION'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'REASONDESCRIPTION'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'REASONDESCRIPTION'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'REASONCODE'], [9, 'REASONDESCRIPTION'], [10, 'ID'], [10, 'PATIENT'], [10, 'BILLABLEPERIOD'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_names': [[-1, '*'], [0, 'ITEM'], [0, 'population type'], [0, 'OCCURRENCES'], [0, 'population count'], [0, 'prevalence rate'], [0, 'prevalence percentage'], [1, 'patient'], [1, 'birth date'], [1, 'death date'], [1, 'social security number'], [1, 'drivers'], [1, 'passport'], [1, 'prefix'], [1, 'first'], [1, 'last'], [1, 'suffix'], [1, 'maiden'], [1, 'marital'], [1, 'race'], [1, 'ethnicity'], [1, 'gender'], [1, 'birth place'], [1, 'address'], [2, 'ID'], [2, 'DATE'], [2, 'PATIENT'], [2, 'CODE'], [2, 'DESCRIPTION'], [2, 'REASONCODE'], [2, 'reason description'], [3, 'START'], [3, 'STOP'], [3, 'PATIENT'], [3, 'ENCOUNTER'], [3, 'CODE'], [3, 'DESCRIPTION'], [4, 'ID'], [4, 'START'], [4, 'STOP'], [4, 'PATIENT'], [4, 'ENCOUNTER'], [4, 'CODE'], [4, 'DESCRIPTION'], [4, 'REASONCODE'], [4, 'reason description'], [5, 'START'], [5, 'STOP'], [5, 'PATIENT'], [5, 'ENCOUNTER'], [5, 'CODE'], [5, 'DESCRIPTION'], [6, 'DATE'], [6, 'PATIENT'], [6, 'ENCOUNTER'], [6, 'CODE'], [6, 'DESCRIPTION'], [7, 'START'], [7, 'STOP'], [7, 'PATIENT'], [7, 'ENCOUNTER'], [7, 'CODE'], [7, 'DESCRIPTION'], [7, 'REASONCODE'], [7, 'reason description'], [8, 'DATE'], [8, 'PATIENT'], [8, 'ENCOUNTER'], [8, 'CODE'], [8, 'DESCRIPTION'], [8, 'VALUE'], [8, 'UNITS'], [9, 'DATE'], [9, 'PATIENT'], [9, 'ENCOUNTER'], [9, 'CODE'], [9, 'DESCRIPTION'], [9, 'reason code'], [9, 'reason description'], [10, 'ID'], [10, 'PATIENT'], [10, 'billable period'], [10, 'ORGANIZATION'], [10, 'ENCOUNTER'], [10, 'DIAGNOSIS'], [10, 'TOTAL']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'real', 'real', 'text', 'date', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'date', 'text', 'text', 'real', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'date', 'text', 'text', 'integer', 'text', 'date', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'date', 'text', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'integer'], 'primary_keys': [1, 7, 24, [33, 34, 35], [52, 53, 54, 55], [57, 59, 60, 61], 79], 'foreign_keys': [[26, 7], [33, 7], [34, 24], [40, 7], [41, 24], [51, 1], [48, 7], [49, 24], [53, 7], [54, 24], [59, 7], [60, 24], [66, 7], [67, 24], [73, 7], [74, 24]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(CASE WHEN T2.gender = 'F' AND T2.race = 'asian' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.gender) FROM conditions AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T1.DESCRIPTION = 'Acute bronchitis (disorder)'"}],"db_id":"synthea"}933{"prompt":"How much higher in percentage does the word \"grec\" occur on the page titled \"\u00c0bac\" than on the page titled \"Astronomia\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How much higher in percentage does the word \"grec\" occur on the page titled \"\u00c0bac\" than on the page titled \"Astronomia\"?\n        \n        ### Context:\n        #### Query Context:\n        grec refers to word = 'grec'; \u00c0bac refers to title = '\u00c0bac'; Astronomia refers to title = 'Astronomia'; percentage = DIVIDE(SUBTRACT(occurrences where title = '\u00c0bac' AND word = 'grec', occurrences where title = 'Astronomia' AND word = 'grec'), occurrences where title = 'Astronomia' AND word = 'grec')\n        \n        #### Database Schema:\n        {'db_id': 'language_corpus', 'table_names_original': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'table_names': ['langs', 'pages', 'words', 'langs_words', 'pages_words', 'biwords'], 'column_names_original': [[-1, '*'], [0, 'lid'], [0, 'lang'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'pid'], [1, 'lid'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'wid'], [2, 'word'], [2, 'occurrences'], [3, 'lid'], [3, 'wid'], [3, 'occurrences'], [4, 'pid'], [4, 'wid'], [4, 'occurrences'], [5, 'lid'], [5, 'w1st'], [5, 'w2nd'], [5, 'occurrences']], 'column_names': [[-1, '*'], [0, 'language id'], [0, 'language'], [0, 'locale'], [0, 'pages'], [0, 'words'], [1, 'page id'], [1, 'language id'], [1, 'page'], [1, 'revision'], [1, 'title'], [1, 'words'], [2, 'word id'], [2, 'word'], [2, 'occurrences'], [3, 'language id'], [3, 'word id'], [3, 'occurrences'], [4, 'page id'], [4, 'word id'], [4, 'occurrences'], [5, 'language id'], [5, 'word id of the first word'], [5, 'word id of the second word'], [5, 'occurrences']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 6, 12, [15, 16], [18, 19], [21, 22, 23]], 'foreign_keys': [[7, 1], [16, 12], [15, 1], [19, 12], [18, 6], [23, 12], [22, 12], [21, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST((SUM(CASE WHEN T3.title = '\u00c0bac' THEN T2.occurrences END) - SUM(CASE WHEN T3.title = 'Astronomia' THEN T2.occurrences END)) AS REAL) * 100 \/ SUM(CASE WHEN T3.title = 'Astronomia' THEN T2.occurrences END) FROM words AS T1 INNER JOIN pages_words AS T2 ON T1.wid = T2.wid INNER JOIN pages AS T3 ON T2.pid = T3.pid WHERE T1.word = 'grec'"}],"db_id":"language_corpus"}934{"prompt":"Describe the number of app IDs and location of the event ID \"79641\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Describe the number of app IDs and location of the event ID \"79641\".\n        \n        ### Context:\n        #### Query Context:\n        location = longitude, latitude;\n        \n        #### Database Schema:\n        {'db_id': 'talkingdata', 'table_names_original': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'table_names': ['app_all', 'app_events', 'app_events_relevant', 'app_labels', 'events', 'events_relevant', 'gender_age', 'gender_age_test', 'gender_age_train', 'label_categories', 'phone_brand_device_model2', 'sample_submission'], 'column_names_original': [[-1, '*'], [0, 'app_id'], [1, 'event_id'], [1, 'app_id'], [1, 'is_installed'], [1, 'is_active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app_id'], [3, 'label_id'], [4, 'event_id'], [4, 'device_id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device_id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label_id'], [9, 'category'], [10, 'device_id'], [10, 'phone_brand'], [10, 'device_model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_names': [[-1, '*'], [0, 'app_id'], [1, 'event id'], [1, 'app id'], [1, 'is installed'], [1, 'is active'], [2, 'event_id'], [2, 'app_id'], [2, 'is_installed'], [2, 'is_active'], [3, 'app id'], [3, 'label id'], [4, 'event id'], [4, 'device id'], [4, 'timestamp'], [4, 'longitude'], [4, 'latitude'], [5, 'event_id'], [5, 'device_id'], [5, 'timestamp'], [5, 'longitude'], [5, 'latitude'], [6, 'device id'], [6, 'gender'], [6, 'age'], [6, 'group'], [7, 'device_id'], [8, 'device_id'], [8, 'gender'], [8, 'age'], [8, 'group'], [9, 'label id'], [9, 'category'], [10, 'device id'], [10, 'phone brand'], [10, 'device model'], [11, 'device_id'], [11, 'F23-'], [11, 'F24-26'], [11, 'F27-28'], [11, 'F29-32'], [11, 'F33-42'], [11, 'F43+'], [11, 'M22-'], [11, 'M23-26'], [11, 'M27-28'], [11, 'M29-31'], [11, 'M32-38'], [11, 'M39+']], 'column_types': ['text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'integer', 'datetime', 'real', 'real', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'real'], 'primary_keys': [1, [2, 3], [6, 7], 12, 17, 22, 26, 27, 31, [33, 34, 35], 36], 'foreign_keys': [[2, 12], [7, 1], [6, 17], [10, 1], [11, 31], [18, 22], [22, 33]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.app_id), T2.longitude, T2.latitude FROM app_events AS T1 INNER JOIN events AS T2 ON T1.event_id = T2.event_id WHERE T1.event_id = 79641 GROUP BY T2.longitude, T2.latitude"}],"db_id":"talkingdata"}935{"prompt":"Provide the zip codes, cities, and locations of the postal points that have Shared Reshipper as a bad alias.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the zip codes, cities, and locations of the postal points that have Shared Reshipper as a bad alias.\n        \n        ### Context:\n        #### Query Context:\n        latitude and longitude coordinates can be used to identify the location;\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.zip_code, T2.city, T2.latitude, T2.longitude FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.bad_alias = 'Shared Reshipper'"}],"db_id":"address"}936{"prompt":"What's the descripition of BitBar?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What's the descripition of BitBar?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'coinmarketcap', 'table_names_original': ['coins', 'historical'], 'table_names': ['Coins', 'Historical'], 'column_names_original': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform_id'], [0, 'date_added'], [0, 'date_launched'], [1, 'date'], [1, 'coin_id'], [1, 'cmc_rank'], [1, 'market_cap'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent_change_1h'], [1, 'percent_change_24h'], [1, 'percent_change_7d'], [1, 'circulating_supply'], [1, 'total_supply'], [1, 'max_supply'], [1, 'num_market_pairs']], 'column_names': [[-1, '*'], [0, 'id'], [0, 'name'], [0, 'slug'], [0, 'symbol'], [0, 'status'], [0, 'category'], [0, 'description'], [0, 'subreddit'], [0, 'notice'], [0, 'tags'], [0, 'tag_names'], [0, 'website'], [0, 'platform id'], [0, 'date added'], [0, 'date lanched'], [1, 'date'], [1, 'coin_id'], [1, 'coinmarketcap rank'], [1, 'Market capitalization'], [1, 'price'], [1, 'open'], [1, 'high'], [1, 'low'], [1, 'close'], [1, 'time_high'], [1, 'time_low'], [1, 'volume_24h'], [1, 'percent change 1h'], [1, 'percent change 24h'], [1, 'percent change 7d'], [1, 'circulating supply'], [1, 'total supply'], [1, 'max supply'], [1, 'number market pairs']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'real', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'real', 'real', 'real', 'real', 'real', 'integer'], 'primary_keys': [1], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT description FROM coins WHERE name = 'BitBar'"}],"db_id":"coinmarketcap"}937{"prompt":"How many of the movies rated 5 are rated by a user between the ages of 25 and 35?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the movies rated 5 are rated by a user between the ages of 25 and 35?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(T1.movieid) FROM u2base AS T1 INNER JOIN users AS T2 ON T1.userid = T2.userid WHERE T1.rating = 5 AND T2.age BETWEEN 25 AND 35"}],"db_id":"movielens"}938{"prompt":"Provide the country with republic government which has the highest population growth?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Provide the country with republic government which has the highest population growth?\n        \n        ### Context:\n        #### Query Context:\n        \n        \n        #### Database Schema:\n        {'db_id': 'mondial_geo', 'table_names_original': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergesWith', 'mountain', 'mountainOnIsland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'table_names': ['borders', 'city', 'continent', 'country', 'desert', 'economy', 'encompasses', 'ethnicGroup', 'geo_desert', 'geo_estuary', 'geo_island', 'geo_lake', 'geo_mountain', 'geo_river', 'geo_sea', 'geo_source', 'island', 'islandIn', 'isMember', 'lake', 'language', 'located', 'locatedOn', 'mergeswith', 'mountain', 'mountainonisland', 'organization', 'politics', 'population', 'province', 'religion', 'river', 'sea', 'target'], 'column_names_original': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'GDP'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'Population_Growth'], [28, 'Infant_Mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'CapProv'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_names': [[-1, '*'], [0, 'Country1'], [0, 'Country2'], [0, 'Length'], [1, 'Name'], [1, 'Country'], [1, 'Province'], [1, 'Population'], [1, 'Longitude'], [1, 'Latitude'], [2, 'Name'], [2, 'Area'], [3, 'Name'], [3, 'Code'], [3, 'Capital'], [3, 'Province'], [3, 'Area'], [3, 'Population'], [4, 'Name'], [4, 'Area'], [4, 'Longitude'], [4, 'Latitude'], [5, 'Country'], [5, 'gross domestic product'], [5, 'Agriculture'], [5, 'Service'], [5, 'Industry'], [5, 'Inflation'], [6, 'Country'], [6, 'Continent'], [6, 'Percentage'], [7, 'Country'], [7, 'Name'], [7, 'Percentage'], [8, 'Desert'], [8, 'Country'], [8, 'Province'], [9, 'River'], [9, 'Country'], [9, 'Province'], [10, 'Island'], [10, 'Country'], [10, 'Province'], [11, 'Lake'], [11, 'Country'], [11, 'Province'], [12, 'Mountain'], [12, 'Country'], [12, 'Province'], [13, 'River'], [13, 'Country'], [13, 'Province'], [14, 'Sea'], [14, 'Country'], [14, 'Province'], [15, 'River'], [15, 'Country'], [15, 'Province'], [16, 'Name'], [16, 'Islands'], [16, 'Area'], [16, 'Height'], [16, 'Type'], [16, 'Longitude'], [16, 'Latitude'], [17, 'Island'], [17, 'Sea'], [17, 'Lake'], [17, 'River'], [18, 'Country'], [18, 'Organization'], [18, 'Type'], [19, 'Name'], [19, 'Area'], [19, 'Depth'], [19, 'Altitude'], [19, 'Type'], [19, 'River'], [19, 'Longitude'], [19, 'Latitude'], [20, 'Country'], [20, 'Name'], [20, 'Percentage'], [21, 'City'], [21, 'Province'], [21, 'Country'], [21, 'River'], [21, 'Lake'], [21, 'Sea'], [22, 'City'], [22, 'Province'], [22, 'Country'], [22, 'Island'], [23, 'Sea1'], [23, 'Sea2'], [24, 'Name'], [24, 'Mountains'], [24, 'Height'], [24, 'Type'], [24, 'Longitude'], [24, 'Latitude'], [25, 'Mountain'], [25, 'Island'], [26, 'Abbreviation'], [26, 'Name'], [26, 'City'], [26, 'Country'], [26, 'Province'], [26, 'Established'], [27, 'Country'], [27, 'Independence'], [27, 'Dependent'], [27, 'Government'], [28, 'Country'], [28, 'population growth'], [28, 'infant mortality'], [29, 'Name'], [29, 'Country'], [29, 'Population'], [29, 'Area'], [29, 'Capital'], [29, 'capital province'], [30, 'Country'], [30, 'Name'], [30, 'Percentage'], [31, 'Name'], [31, 'River'], [31, 'Lake'], [31, 'Sea'], [31, 'Length'], [31, 'SourceLongitude'], [31, 'SourceLatitude'], [31, 'Mountains'], [31, 'SourceAltitude'], [31, 'EstuaryLongitude'], [31, 'EstuaryLatitude'], [32, 'Name'], [32, 'Depth'], [33, 'Country'], [33, 'Target']], 'column_types': ['text', 'text', 'text', 'real', 'text', 'text', 'text', 'integer', 'real', 'real', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'integer', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'text', 'real', 'real', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'text', 'real', 'real', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'text', 'date', 'text', 'text', 'text', 'real', 'real', 'text', 'text', 'integer', 'real', 'text', 'text', 'text', 'text', 'real', 'text', 'text', 'text', 'text', 'real', 'real', 'real', 'text', 'real', 'real', 'real', 'text', 'real', 'text', 'text'], 'primary_keys': [[1, 2], [4, 6], 10, 13, 18, 22, [28, 29], [31, 32], [34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48], [49, 50, 51], [52, 53, 54], [55, 56, 57], 58, [69, 70], 72, [80, 81], [89, 90, 91, 92], [93, 94], 95, [101, 102], 103, 109, 113, [116, 117], [122, 123], 125, 136, 138], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Country FROM population AS T1 INNER JOIN politics AS T2 ON T1.Country = T2.Country WHERE T2.Government = 'republic' ORDER BY T1.Population_Growth DESC LIMIT 1"}],"db_id":"mondial_geo"}939{"prompt":"Among the cities with a population between 140000 and 150000, list the country that has life expectancy greater than 80% life expectancy of all countries.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Among the cities with a population between 140000 and 150000, list the country that has life expectancy greater than 80% life expectancy of all countries.\n        \n        ### Context:\n        #### Query Context:\n        life expectancy greater than 80% life expectancy of all countries refers to LifeExpectancy < (MULTIPLY(AVG(LifeExpectancy), 0.8));\n        \n        #### Database Schema:\n        {'db_id': 'world', 'table_names_original': ['City', 'Country', 'CountryLanguage'], 'table_names': ['City', 'Country', 'CountryLanguage'], 'column_names_original': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'CountryCode'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'SurfaceArea'], [1, 'IndepYear'], [1, 'Population'], [1, 'LifeExpectancy'], [1, 'GNP'], [1, 'GNPOld'], [1, 'LocalName'], [1, 'GovernmentForm'], [1, 'HeadOfState'], [1, 'Capital'], [1, 'Code2'], [2, 'CountryCode'], [2, 'Language'], [2, 'IsOfficial'], [2, 'Percentage']], 'column_names': [[-1, '*'], [0, 'ID'], [0, 'Name'], [0, 'Country Code'], [0, 'District'], [0, 'Population'], [1, 'Code'], [1, 'Name'], [1, 'Continent'], [1, 'Region'], [1, 'Surface Area'], [1, 'Independence Year'], [1, 'Population'], [1, 'Life Expectancy'], [1, 'Gross National Product'], [1, 'Gross National Product Old'], [1, 'Local Name'], [1, 'Government Form'], [1, 'Head Of State'], [1, 'Capital'], [1, 'Code2'], [2, 'Country Code'], [2, 'Language'], [2, 'Is Official'], [2, 'Percentage']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real', 'integer', 'integer', 'real', 'real', 'real', 'text', 'text', 'text', 'integer', 'text', 'text', 'text', 'text', 'real'], 'primary_keys': [1, 6, [21, 22]], 'foreign_keys': [[3, 6], [21, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.Name FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T1.Population BETWEEN 140000 AND 150000 GROUP BY T2.Name, LifeExpectancy HAVING LifeExpectancy < ( SELECT AVG(LifeExpectancy) FROM Country ) * 0.8"}],"db_id":"world"}940{"prompt":"List the character names in the \"Open Water\" movie.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        List the character names in the \"Open Water\" movie.\n        \n        ### Context:\n        #### Query Context:\n        \"Open Water\" movie refers to title = 'Open Water'\n        \n        #### Database Schema:\n        {'db_id': 'movies_4', 'table_names_original': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'table_names': ['country', 'department', 'gender', 'genre', 'keyword', 'language', 'language_role', 'movie', 'movie_genres', 'movie_languages', 'person', 'movie_crew', 'production_company', 'production_country', 'movie_cast', 'movie_keywords', 'movie_company'], 'column_names_original': [[-1, '*'], [0, 'country_id'], [0, 'country_iso_code'], [0, 'country_name'], [1, 'department_id'], [1, 'department_name'], [2, 'gender_id'], [2, 'gender'], [3, 'genre_id'], [3, 'genre_name'], [4, 'keyword_id'], [4, 'keyword_name'], [5, 'language_id'], [5, 'language_code'], [5, 'language_name'], [6, 'role_id'], [6, 'language_role'], [7, 'movie_id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release_date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote_average'], [7, 'vote_count'], [8, 'movie_id'], [8, 'genre_id'], [9, 'movie_id'], [9, 'language_id'], [9, 'language_role_id'], [10, 'person_id'], [10, 'person_name'], [11, 'movie_id'], [11, 'person_id'], [11, 'department_id'], [11, 'job'], [12, 'company_id'], [12, 'company_name'], [13, 'movie_id'], [13, 'country_id'], [14, 'movie_id'], [14, 'person_id'], [14, 'character_name'], [14, 'gender_id'], [14, 'cast_order'], [15, 'movie_id'], [15, 'keyword_id'], [16, 'movie_id'], [16, 'company_id']], 'column_names': [[-1, '*'], [0, 'country id'], [0, 'country iso code'], [0, 'country name'], [1, 'department id'], [1, 'department name'], [2, 'gender id'], [2, 'gender'], [3, 'genre id'], [3, 'genre_name'], [4, 'keyword id'], [4, 'keyword name'], [5, 'language id'], [5, 'language code'], [5, 'language name'], [6, 'role id'], [6, 'language role'], [7, 'movie id'], [7, 'title'], [7, 'budget'], [7, 'homepage'], [7, 'overview'], [7, 'popularity'], [7, 'release date'], [7, 'revenue'], [7, 'runtime'], [7, 'movie_status'], [7, 'tagline'], [7, 'vote average'], [7, 'vote count'], [8, 'movie id'], [8, 'genre id'], [9, 'movie id'], [9, 'language id'], [9, 'language role id'], [10, 'person id'], [10, 'person name'], [11, 'movie id'], [11, 'person id'], [11, 'department id'], [11, 'job'], [12, 'company id'], [12, 'company name'], [13, 'mivie id'], [13, 'country id'], [14, 'movie id'], [14, 'person id'], [14, 'character name'], [14, 'gender id'], [14, 'cast order'], [15, 'movie id'], [15, 'keyword id'], [16, 'movie id'], [16, 'company id']], 'column_types': ['text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text', 'integer', 'text', 'integer', 'text', 'text', 'real', 'date', 'integer', 'integer', 'text', 'text', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 6, 8, 10, 12, 15, 17, 35, 41], 'foreign_keys': [[30, 17], [31, 8], [34, 15], [32, 17], [33, 12], [38, 35], [37, 17], [39, 4], [43, 17], [44, 1], [46, 35], [45, 17], [48, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T2.character_name FROM movie AS T1 INNER JOIN movie_cast AS T2 ON T1.movie_id = T2.movie_id WHERE T1.title = 'Open Water'"}],"db_id":"movies_4"}941{"prompt":"How many episodes have not won any Law & Order series awards?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many episodes have not won any Law & Order series awards?\n        \n        ### Context:\n        #### Query Context:\n        have not won any award refers to Result = 'Nominee'\n        \n        #### Database Schema:\n        {'db_id': 'law_episode', 'table_names_original': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'table_names': ['Episode', 'Keyword', 'Person', 'Award', 'Credit', 'Vote'], 'column_names_original': [[-1, '*'], [0, 'episode_id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number_in_series'], [0, 'title'], [0, 'summary'], [0, 'air_date'], [0, 'episode_image'], [0, 'rating'], [0, 'votes'], [1, 'episode_id'], [1, 'keyword'], [2, 'person_id'], [2, 'name'], [2, 'birthdate'], [2, 'birth_name'], [2, 'birth_place'], [2, 'birth_region'], [2, 'birth_country'], [2, 'height_meters'], [2, 'nickname'], [3, 'award_id'], [3, 'organization'], [3, 'year'], [3, 'award_category'], [3, 'award'], [3, 'series'], [3, 'episode_id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode_id'], [4, 'person_id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode_id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_names': [[-1, '*'], [0, 'episode id'], [0, 'series'], [0, 'season'], [0, 'episode'], [0, 'number in series'], [0, 'title'], [0, 'summary'], [0, 'air date'], [0, 'episode image'], [0, 'rating'], [0, 'votes'], [1, 'episode id'], [1, 'keyword'], [2, 'person id'], [2, 'name'], [2, 'birth date'], [2, 'birth name'], [2, 'birth place'], [2, 'birth region'], [2, 'birth country'], [2, 'height meters'], [2, 'nickname'], [3, 'award id'], [3, 'organization'], [3, 'year'], [3, 'award category'], [3, 'award'], [3, 'series'], [3, 'episode id'], [3, 'person_id'], [3, 'role'], [3, 'result'], [4, 'episode id'], [4, 'person id'], [4, 'category'], [4, 'role'], [4, 'credited'], [5, 'episode id'], [5, 'stars'], [5, 'votes'], [5, 'percent']], 'column_types': ['text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'text', 'real', 'integer', 'text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'real', 'text', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'real'], 'primary_keys': [1, [12, 13], 14, 23, [33, 34]], 'foreign_keys': [[12, 1], [30, 14], [29, 1], [34, 14], [33, 1], [38, 1]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(award_id) FROM Award WHERE Result = 'Nominee'"}],"db_id":"law_episode"}942{"prompt":"Give the X and Y coordinates of the sample object of image ID 23 that has the 'cast' attribute class.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Give the X and Y coordinates of the sample object of image ID 23 that has the 'cast' attribute class.\n        \n        ### Context:\n        #### Query Context:\n        X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast';\n        \n        #### Database Schema:\n        {'db_id': 'image_and_language', 'table_names_original': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'table_names': ['ATT_CLASSES', 'OBJ_CLASSES', 'IMG_OBJ', 'IMG_OBJ_ATT', 'PRED_CLASSES', 'IMG_REL'], 'column_names_original': [[-1, '*'], [0, 'ATT_CLASS_ID'], [0, 'ATT_CLASS'], [1, 'OBJ_CLASS_ID'], [1, 'OBJ_CLASS'], [2, 'IMG_ID'], [2, 'OBJ_SAMPLE_ID'], [2, 'OBJ_CLASS_ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMG_ID'], [3, 'ATT_CLASS_ID'], [3, 'OBJ_SAMPLE_ID'], [4, 'PRED_CLASS_ID'], [4, 'PRED_CLASS'], [5, 'IMG_ID'], [5, 'PRED_CLASS_ID'], [5, 'OBJ1_SAMPLE_ID'], [5, 'OBJ2_SAMPLE_ID']], 'column_names': [[-1, '*'], [0, 'ATTRIBUTE CLASS ID'], [0, 'ATTRIBUTE CLASS'], [1, 'OBJECT CLASS ID'], [1, 'OBJECT CLASS'], [2, 'IMAGE ID'], [2, 'OBJECT SAMPLE ID'], [2, 'OBJECT CLASS ID'], [2, 'X'], [2, 'Y'], [2, 'W'], [2, 'H'], [3, 'IMAGE ID'], [3, 'ATTRIBUTE CLASS ID'], [3, 'OBJECT SAMPLE ID'], [4, 'PREDICTION CLASS ID'], [4, 'PREDICTION CLASS'], [5, 'IMAGE ID'], [5, 'PREDICTION CLASS ID'], [5, 'OBJECT1 SAMPLE ID'], [5, 'OBJECT2 SAMPLE ID']], 'column_types': ['text', 'integer', 'text', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 3, [5, 6], [12, 13, 14], 15, [17, 18, 19, 20]], 'foreign_keys': [[7, 3], [12, 5], [14, 6], [13, 1], [17, 5], [20, 6], [17, 5], [19, 6], [18, 15]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T3.OBJ_SAMPLE_ID, T3.X, T3.Y FROM ATT_CLASSES AS T1 INNER JOIN IMG_OBJ_ATT AS T2 ON T1.ATT_CLASS_ID = T2.ATT_CLASS_ID INNER JOIN IMG_OBJ AS T3 ON T2.IMG_ID = T3.IMG_ID WHERE T3.IMG_ID = 23 AND T1.ATT_CLASS = 'cast'"}],"db_id":"image_and_language"}943{"prompt":"Please list the full names of all the clients whose complaints are still in progress.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Please list the full names of all the clients whose complaints are still in progress.\n        \n        ### Context:\n        #### Query Context:\n        full name = first, middle, last; complaints are still in progress refers to \"Company response to consumer\" = 'In progress';\n        \n        #### Database Schema:\n        {'db_id': 'retail_complains', 'table_names_original': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'table_names': ['state', 'callcenterlogs', 'client', 'district', 'events', 'reviews'], 'column_names_original': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phonefinal'], [1, 'vru+line'], [1, 'call_id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'ser_start'], [1, 'ser_exit'], [1, 'ser_time'], [2, 'client_id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district_id'], [3, 'district_id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client_ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_names': [[-1, '*'], [0, 'StateCode'], [0, 'State'], [0, 'Region'], [1, 'Date received'], [1, 'Complaint ID'], [1, 'rand client'], [1, 'phone final'], [1, 'voice response unit line'], [1, 'call id'], [1, 'priority'], [1, 'type'], [1, 'outcome'], [1, 'server'], [1, 'server start'], [1, 'server exit'], [1, 'server time'], [2, 'client id'], [2, 'sex'], [2, 'day'], [2, 'month'], [2, 'year'], [2, 'age'], [2, 'social'], [2, 'first'], [2, 'middle'], [2, 'last'], [2, 'phone'], [2, 'email'], [2, 'address_1'], [2, 'address_2'], [2, 'city'], [2, 'state'], [2, 'zipcode'], [2, 'district id'], [3, 'district id'], [3, 'city'], [3, 'state_abbrev'], [3, 'division'], [4, 'Date received'], [4, 'Product'], [4, 'Sub-product'], [4, 'Issue'], [4, 'Sub-issue'], [4, 'Consumer complaint narrative'], [4, 'Tags'], [4, 'Tags Consumer consent provided?'], [4, 'Submitted via'], [4, 'Date sent to company'], [4, 'Company response to consumer'], [4, 'Timely response?'], [4, 'Consumer disputed?'], [4, 'Complaint ID'], [4, 'Client ID'], [5, 'Date'], [5, 'Stars'], [5, 'Reviews'], [5, 'Product'], [5, 'district_id']], 'column_types': ['text', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'text', 'date', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'date', 'integer', 'text', 'text', 'integer'], 'primary_keys': [1, 5, 17, 35, [52, 53], 54], 'foreign_keys': [[6, 17], [34, 35], [37, 1], [53, 17], [52, 5], [58, 35]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.`Company response to consumer` = 'In progress'"}],"db_id":"retail_complains"}944{"prompt":"How many of the worst actors are men and how many of the worst actors are women? Indicate your answer in ratio form.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        How many of the worst actors are men and how many of the worst actors are women? Indicate your answer in ratio form.\n        \n        ### Context:\n        #### Query Context:\n        The worst actors means a_quality = 0; Men and male share the same meaning; men actors refers to a_gender = 'M'\n        \n        #### Database Schema:\n        {'db_id': 'movielens', 'table_names_original': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'table_names': ['users', 'directors', 'actors', 'movies', 'movies2actors', 'movies2directors', 'u2base'], 'column_names_original': [[-1, '*'], [0, 'userid'], [0, 'age'], [0, 'u_gender'], [0, 'occupation'], [1, 'directorid'], [1, 'd_quality'], [1, 'avg_revenue'], [2, 'actorid'], [2, 'a_gender'], [2, 'a_quality'], [3, 'movieid'], [3, 'year'], [3, 'isEnglish'], [3, 'country'], [3, 'runningtime'], [4, 'movieid'], [4, 'actorid'], [4, 'cast_num'], [5, 'movieid'], [5, 'directorid'], [5, 'genre'], [6, 'userid'], [6, 'movieid'], [6, 'rating']], 'column_names': [[-1, '*'], [0, 'user id'], [0, 'age'], [0, 'user gender'], [0, 'occupation'], [1, 'director id'], [1, 'director quality'], [1, 'average revenue'], [2, 'actor id'], [2, 'actor gender'], [2, 'actor quality'], [3, 'movie id'], [3, 'year'], [3, 'is English'], [3, 'country'], [3, 'runningtime'], [4, 'movie id'], [4, 'actor id'], [4, 'cast number'], [5, 'movie id'], [5, 'director id'], [5, 'genre'], [6, 'user id'], [6, 'movie id'], [6, 'rating']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'text'], 'primary_keys': [1, 5, 8, 11, [16, 17], [19, 20], [22, 23]], 'foreign_keys': []}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(IIF(a_gender = 'M', 1, 0)) AS REAL) \/ SUM(IIF(a_gender = 'F', 1, 0)) FROM actors WHERE a_quality = 0"}],"db_id":"movielens"}945{"prompt":"Calculate the average of 2020's population in each zip code.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Calculate the average of 2020's population in each zip code.\n        \n        ### Context:\n        #### Query Context:\n        average of 2020 population in each zip code refers to Divide (Sum(population_2020), Count(zip_code))\n        \n        #### Database Schema:\n        {'db_id': 'address', 'table_names_original': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'table_names': ['CBSA', 'state', 'congress', 'zip_data', 'alias', 'area_code', 'avoid', 'country', 'zip_congress'], 'column_names_original': [[-1, '*'], [0, 'CBSA'], [0, 'CBSA_name'], [0, 'CBSA_type'], [1, 'abbreviation'], [1, 'name'], [2, 'cognress_rep_id'], [2, 'first_name'], [2, 'last_name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land_area'], [3, 'zip_code'], [3, 'city'], [3, 'state'], [3, 'multi_county'], [3, 'type'], [3, 'organization'], [3, 'time_zone'], [3, 'daylight_savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state_fips'], [3, 'county_fips'], [3, 'region'], [3, 'division'], [3, 'population_2020'], [3, 'population_2010'], [3, 'households'], [3, 'avg_house_value'], [3, 'avg_income_per_household'], [3, 'persons_per_household'], [3, 'white_population'], [3, 'black_population'], [3, 'hispanic_population'], [3, 'asian_population'], [3, 'american_indian_population'], [3, 'hawaiian_population'], [3, 'other_population'], [3, 'male_population'], [3, 'female_population'], [3, 'median_age'], [3, 'male_median_age'], [3, 'female_median_age'], [3, 'residential_mailboxes'], [3, 'business_mailboxes'], [3, 'total_delivery_receptacles'], [3, 'businesses'], [3, '1st_quarter_payroll'], [3, 'annual_payroll'], [3, 'employees'], [3, 'water_area'], [3, 'land_area'], [3, 'single_family_delivery_units'], [3, 'multi_family_delivery_units'], [3, 'total_beneficiaries'], [3, 'retired_workers'], [3, 'disabled_workers'], [3, 'parents_and_widowed'], [3, 'spouses'], [3, 'children'], [3, 'over_65'], [3, 'monthly_benefits_all'], [3, 'monthly_benefits_retired_workers'], [3, 'monthly_benefits_widowed'], [3, 'CBSA'], [4, 'zip_code'], [4, 'alias'], [5, 'zip_code'], [5, 'area_code'], [6, 'zip_code'], [6, 'bad_alias'], [7, 'zip_code'], [7, 'county'], [7, 'state'], [8, 'zip_code'], [8, 'district']], 'column_names': [[-1, '*'], [0, 'CBSA'], [0, 'cbsa name'], [0, 'cbsa type'], [1, 'abbreviation'], [1, 'name'], [2, 'congress representative id'], [2, 'first name'], [2, 'last name'], [2, 'CID'], [2, 'party'], [2, 'state'], [2, 'abbreviation'], [2, 'House'], [2, 'District'], [2, 'land area'], [3, 'zip code'], [3, 'city'], [3, 'state'], [3, 'multi country'], [3, 'type'], [3, 'organization'], [3, 'time zone'], [3, 'daylight savings'], [3, 'latitude'], [3, 'longitude'], [3, 'elevation'], [3, 'state fips'], [3, 'country fips'], [3, 'region'], [3, 'division'], [3, 'population 2020'], [3, 'population 2010'], [3, 'households'], [3, 'average house value'], [3, 'average income per household'], [3, 'persons per household'], [3, 'white population'], [3, 'black population'], [3, 'Hispanic population'], [3, 'Asian population'], [3, 'American Indian population'], [3, 'Hawaiian population'], [3, 'other population'], [3, 'male population'], [3, 'female population'], [3, 'median age'], [3, 'male median age'], [3, 'female median age'], [3, 'residential mailboxes'], [3, 'business mailboxes'], [3, 'total delivery receptacles'], [3, 'businesses'], [3, '1st quarter payroll'], [3, 'annual payroll'], [3, 'employees'], [3, 'water area'], [3, 'land area'], [3, 'single-family delivery units'], [3, 'multi-family delivery units'], [3, 'total beneficiaries'], [3, 'retired workers'], [3, 'disable workers'], [3, 'parents and widowed'], [3, 'spouses'], [3, 'children'], [3, 'over 65'], [3, 'monthly benefits all'], [3, 'monthly benefits retired workers'], [3, 'monthly benefits widowed'], [3, 'CBSA'], [4, 'zip code'], [4, 'alias'], [5, 'zip code'], [5, 'area code'], [6, 'zip code'], [6, 'bad alias'], [7, 'zip code'], [7, 'country'], [7, 'state'], [8, 'zip code'], [8, 'district']], 'column_types': ['text', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'integer', 'real', 'integer', 'text', 'text', 'text', 'text', 'text', 'text', 'text', 'real', 'real', 'integer', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'real', 'real', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'integer', 'text'], 'primary_keys': [1, 4, 6, 16, 71, [73, 74], [75, 76], [77, 78], [80, 81]], 'foreign_keys': [[12, 4], [70, 1], [18, 4], [71, 16], [73, 16], [75, 16], [79, 4], [77, 16], [80, 16], [81, 6]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT CAST(SUM(population_2020) AS REAL) \/ COUNT(zip_code) FROM zip_data"}],"db_id":"address"}946{"prompt":"What is the birthplace of Aaron Gagnon?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        What is the birthplace of Aaron Gagnon?\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\n        \n        #### Database Schema:\n        {'db_id': 'ice_hockey_draft', 'table_names_original': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'table_names': ['height_info', 'weight_info', 'PlayerInfo', 'SeasonStatus'], 'column_names_original': [[-1, '*'], [0, 'height_id'], [0, 'height_in_cm'], [0, 'height_in_inch'], [1, 'weight_id'], [1, 'weight_in_kg'], [1, 'weight_in_lbs'], [2, 'ELITEID'], [2, 'PlayerName'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position_info'], [2, 'shoots'], [2, 'draftyear'], [2, 'draftround'], [2, 'overall'], [2, 'overallby'], [2, 'CSS_rank'], [2, 'sum_7yr_GP'], [2, 'sum_7yr_TOI'], [2, 'GP_greater_than_0'], [3, 'ELITEID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAMETYPE'], [3, 'GP'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'PIM'], [3, 'PLUSMINUS']], 'column_names': [[-1, '*'], [0, 'height id'], [0, 'height in cm'], [0, 'height in inch'], [1, 'weight id'], [1, 'weight in kg'], [1, 'weight in lbs'], [2, 'ELITE ID'], [2, 'Player Name'], [2, 'birthdate'], [2, 'birthyear'], [2, 'birthmonth'], [2, 'birthday'], [2, 'birthplace'], [2, 'nation'], [2, 'height'], [2, 'weight'], [2, 'position information'], [2, 'shoots'], [2, 'draft year'], [2, 'draft round'], [2, 'overall'], [2, 'overallby'], [2, 'Central Scouting Service ranking'], [2, 'sum 7-year game plays'], [2, 'sum 7-year time on ice'], [2, 'game play greater than 0'], [3, 'ELITE ID'], [3, 'SEASON'], [3, 'TEAM'], [3, 'LEAGUE'], [3, 'GAME TYPE'], [3, 'Game Plays'], [3, 'G'], [3, 'A'], [3, 'P'], [3, 'Penalty Minutes'], [3, 'Plus Minutes']], 'column_types': ['text', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'integer', 'text', 'text', 'date', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'text', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'integer', 'integer', 'text', 'integer', 'text', 'text', 'text', 'text', 'integer', 'integer', 'integer', 'integer', 'integer', 'integer'], 'primary_keys': [1, 4, 7], 'foreign_keys': [[16, 4], [15, 1], [27, 7]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT birthplace FROM PlayerInfo WHERE PlayerName = 'Aaron Gagnon'"}],"db_id":"ice_hockey_draft"}947