NormalMatt/nl2sql

1{"prompt":"What is the total number of households in Arecibo county?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 households in Arecibo county?\n        \n        ### Context:\n        #### Query Context:\n        \"ARECIBO\" is the county; total number of households refer to sum(households)\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 SUM(T1.households) FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO'"}],"db_id":"address"}2{"prompt":"Among the films starring PENELOPE GUINESS, how many of them 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        Among the films starring PENELOPE GUINESS, how many of them are in English?\n        \n        ### Context:\n        #### Query Context:\n        \"English\" is the name of language\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(T3.film_id) 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 INNER JOIN language AS T4 ON T3.language_id = T4.language_id WHERE T4.name = 'English' AND T1.first_name = 'PENELOPE' AND T1.last_name = 'GUINESS'"}],"db_id":"movie_3"}3{"prompt":"List all the order dates for the customer named \"Adrian Kunzelmann\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 order dates for the customer named \"Adrian Kunzelmann\".\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 T3.order_date 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 = 'Adrian' AND T4.last_name = 'Kunzelmann'"}],"db_id":"books"}4{"prompt":"Calculate the difference between the number of root beers sold that use cane sugar and corn syrup.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 root beers sold that use cane sugar and corn syrup.\n        \n        ### Context:\n        #### Query Context:\n        difference = SUBTRACT(SUM(CaneSugar = 'TRUE'), SUM(CornSyrup = 'TRUE')); use cane sugar refers to CaneSugar = 'TRUE'; corn syrup refers to CornSyrup = '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(CASE WHEN T3.CaneSugar = 'TRUE' THEN T1.BrandID ELSE NULL END) - COUNT(CASE WHEN T3.CornSyrup = 'TRUE' THEN T1.BrandID ELSE NULL END) AS DIFFERENCE FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID"}],"db_id":"beer_factory"}5{"prompt":"Among the employees who have more than 10 hours of sick leave, how many of them 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        Among the employees who have more than 10 hours of sick leave, how many of them 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 10 hours of sick leave refer to SickLeaveHours >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'], [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, 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    You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of 4-year public schools from Madison Area Technical College's home state in the Alabama?\n        \n        ### Context:\n        #### Query Context:\n        percentage = MULTIPLY(DIVIDE(SUM(chronname = 'Madison Area Technical College'), SUM(state = 'Alabama')), 100); 4-year refers to level = '4-year'; public refers to control = 'Public'; Madison Area Technical College refers to chronname = 'Madison Area Technical College'; home state in the United States refers to state;\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, 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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 CAST(COUNT(DISTINCT CASE WHEN T1.state = ( SELECT T1.state FROM institution_details AS T1 INNER JOIN state_sector_details AS T2 ON T2.state = T1.state WHERE T1.chronname = 'Madison Area Technical College' ) AND T1.level = '4-year' AND T1.control = 'Public' THEN T1.chronname ELSE NULL END) AS REAL) * 100 \/ COUNT(DISTINCT CASE WHEN T2.state = 'Alabama' THEN T1.chronname ELSE NULL END) FROM institution_details AS T1 INNER JOIN state_sector_details AS T2 ON T2.state = T1.state"}],"db_id":"college_completion"}7{"prompt":"What is the minimum of International migrant stock, total of heavily indebted poor 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 minimum of International migrant stock, total of heavily indebted poor countries?\n        \n        ### Context:\n        #### Query Context:\n        IndicatorName = 'International migrant stock, total'; heavily indebted poor countries referred to by its abbreviated 'HIPC' = OtherGroups; MIN(Value);\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 MIN(T2.Value) FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.OtherGroups = 'HIPC' AND T2.IndicatorName = 'International migrant stock, total'"}],"db_id":"world_development_indicators"}8{"prompt":"Who was the director of the movie \"Tokyo Eyes\"\uff1f","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 director of the movie \"Tokyo Eyes\"\uff1f\n        \n        ### Context:\n        #### Query Context:\n        Tokyo Eyes' is movie_title, director refers to director_name;\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 director_name FROM movies WHERE movie_title = 'Tokyo Eyes'"}],"db_id":"movie_platform"}9{"prompt":"Calculate the total number of orders placed by the company 'GROSELLA-Restaurante'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of orders placed by the company 'GROSELLA-Restaurante'.\n        \n        ### Context:\n        #### Query Context:\n        'GROSELLA-Restaurante' 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 COUNT(T2.OrderID) FROM Customers AS T1 INNER JOIN Orders AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.CompanyName = 'GROSELLA-Restaurante'"}],"db_id":"retail_world"}10{"prompt":"List all cases from the year 2012 in which the subject was 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        List all cases from the year 2012 in which the subject was deceased\n        \n        ### Context:\n        #### Query Context:\n        case refers to case_number; year 2012 refers to date between '2012-01-01' and '2012-12-31'; subject was 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 case_number FROM incidents WHERE STRFTIME('%Y', date) > '2011' AND subject_statuses = 'Deceased'"}],"db_id":"shooting"}11{"prompt":"How many American movies have cast number more than 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 American movies have cast number more than 1?\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 COUNT(T2.actorid) FROM movies AS T1 INNER JOIN movies2actors AS T2 ON T1.movieid = T2.movieid WHERE T1.country = 'USA' AND T2.cast_num > 1"}],"db_id":"movielens"}12{"prompt":"For all phone numbers, what percentage of the total is cell phone?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 phone numbers, what percentage of the total is cell phone?\n        \n        ### Context:\n        #### Query Context:\n        Cellphone referes to the name of the phone type, therefore PhoneNumberTypeID = 1; DIVIDE(COUNT(PhoneNumberTypeID = 1), (COUNT(PhoneNumberTypeID)) as percentage\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, 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'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 CAST(SUM(CASE WHEN T2.Name = 'Cell' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.Name) FROM PersonPhone AS T1 INNER JOIN PhoneNumberType AS T2 ON T1.PhoneNumberTypeID = T2.PhoneNumberTypeID"}],"db_id":"works_cycles"}13{"prompt":"Which ingredient appeared the most in recipes? Calculate its amount of appearance 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        Which ingredient appeared the most in recipes? Calculate its amount of appearance in percentage.\n        \n        ### Context:\n        #### Query Context:\n        ingredient appeared the most in recipes refers to MAX(COUNT(ingredient_id)); calculation = MULTIPLY(DIVIDE(COUNT(MAX(ingredient_id)), COUNT(ingredient_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 T1.name, CAST(COUNT(T2.ingredient_id) AS FLOAT) * 100 \/ ( SELECT COUNT(T2.ingredient_id) FROM Ingredient AS T1 INNER JOIN Quantity AS T2 ON T2.ingredient_id = T1.ingredient_id ) AS \"percentage\" FROM Ingredient AS T1 INNER JOIN Quantity AS T2 ON T2.ingredient_id = T1.ingredient_id GROUP BY T2.ingredient_id ORDER BY COUNT(T2.ingredient_id) DESC LIMIT 1"}],"db_id":"cookbook"}14{"prompt":"Which employee is in charge of the sales in Hollis? Please give the employee's 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        Which employee is in charge of the sales in Hollis? Please give the employee's full name.\n        \n        ### Context:\n        #### Query Context:\n        Hollis refers to TerritoryDescription = 'Hollis'; 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 T1.FirstName, T1.LastName FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID WHERE T3.TerritoryDescription = 'Hollis'"}],"db_id":"retail_world"}15{"prompt":"At what date and time did San Jose Diridon Caltrain Station have most bikes available.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        At what date and time did San Jose Diridon Caltrain Station have most bikes available.\n        \n        ### Context:\n        #### Query Context:\n        San Jose Diridon Caltrain Station is the name of the station; most bikes available refers to MAX(bikes_available);\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 FROM station AS T1 INNER JOIN status AS T2 ON T2.station_id = T1.id WHERE T1.name = 'San Jose Diridon Caltrain Station' AND T2.bikes_available = ( SELECT MAX(T2.bikes_available) FROM station AS T1 INNER JOIN status AS T2 ON T2.station_id = T1.id WHERE T1.name = 'San Jose Diridon Caltrain Station' )"}],"db_id":"bike_share_1"}16{"prompt":"How many Yelp_Business under the category of \"Food\" are good for kids?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Yelp_Business under the category of \"Food\" are good for kids?\n        \n        ### Context:\n        #### Query Context:\n        under the category of \"Food\" refers to category_name = 'Food'; are good for kids refers to attribute_name = 'Good for Kids' 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 COUNT(T3.stars) 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 Business_Attributes AS T4 ON T3.business_id = T4.business_id INNER JOIN Attributes AS T5 ON T4.attribute_id = T5.attribute_id WHERE T1.category_name LIKE 'Food' AND T5.attribute_name LIKE 'Good for Kids' AND T4.attribute_value LIKE 'TRUE'"}],"db_id":"public_review_platform"}17{"prompt":"How many male users of the \"Galaxy S5\" device model?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 users of the \"Galaxy S5\" device model?\n        \n        ### Context:\n        #### Query Context:\n        male refers to gender = 'M';\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 T1.device_id = T2.device_id WHERE T1.device_model = 'Galaxy S5' AND T2.gender = 'M'"}],"db_id":"talkingdata"}18{"prompt":"For the students with an intelligence of 5, list the full name and courses taken by them who have less than a 3 GPA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 students with an intelligence of 5, list the full name and courses taken by them who have less than a 3 GPA.\n        \n        ### Context:\n        #### Query Context:\n        full name of the students = f_name, l_name; 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 T1.f_name, T1.l_name, 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.intelligence = 5 AND T1.gpa < 3"}],"db_id":"cs_semester"}19{"prompt":"List down the inspection ID with the inspector's comment \"A certified food service manager must be present in all establishments at which potentially hazardous food is prepared or served. NO CERTIFIED FOOD MANAGER ON DUTY AT THIS TIME FOODS ARE COOKED AND SERVED SERIOUS CITATION ISSUED\" and inspection category of Personnel.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 inspection ID with the inspector's comment \"A certified food service manager must be present in all establishments at which potentially hazardous food is prepared or served. NO CERTIFIED FOOD MANAGER ON DUTY AT THIS TIME FOODS ARE COOKED AND SERVED SERIOUS CITATION ISSUED\" and inspection category of Personnel.\n        \n        ### Context:\n        #### Query Context:\n        inspector's comment \"A certified food service manager must be present in all establishments at which potentially hazardous food is prepared or served. NO CERTIFIED FOOD MANAGER ON DUTY AT THIS TIME FOODS ARE COOKED AND SERVED SERIOUS CITATION ISSUED\" refers to inspector_comment = 'A certified food service manager must be present in all establishments at which potentially hazardous food is prepared or served.FOUND NO CITY OF CHICAGO SANITATION CERTIFICATE POSTED OR VALID DOCUMENTATION DURING THIS INSPECTION.'; inspection category of Personnel 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 T2.inspection_id FROM inspection_point AS T1 INNER JOIN violation AS T2 ON T1.point_id = T2.point_id WHERE T1.category = 'Personnel' AND T2.inspector_comment = 'A certified food service manager must be present in all establishments at which potentially hazardous food is prepared or served.FOUND NO CITY OF CHICAGO SANITATION CERTIFICATE POSTED OR VALID DOCUMENTATION DURING THIS INSPECTION.'"}],"db_id":"food_inspection_2"}20{"prompt":"Provide the number of events that happened on 2016\/5\/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        Provide the number of events that happened on 2016\/5\/6.\n        \n        ### Context:\n        #### Query Context:\n        on 2016\/5\/6 refers to timestamp = '2016\/5\/6 XX:XX:XX';\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 SUBSTR(`timestamp`, 1, 10) = '2016-05-06'"}],"db_id":"talkingdata"}21{"prompt":"What are the full names of the 10 youngest married male production technicians?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 full names of the 10 youngest married male production technicians?\n        \n        ### Context:\n        #### Query Context:\n        youngest refers to latest BirthDate; married refers to MaritalStatus = 'M'; production technician is a JobTitle; 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'], 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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 LIKE 'Production Technician%' AND T1.Gender = 'M' AND T1.MaritalStatus = 'M' ORDER BY T1.BirthDate DESC LIMIT 10"}],"db_id":"works_cycles"}22{"prompt":"Provide the name of the business which had the most number of inspections because of 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        Provide the name of the business which had the most number of inspections because of complaint.\n        \n        ### Context:\n        #### Query Context:\n        the most number of inspections because of complaint refers to type = 'Complaint' where MAX(business_id);\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 T2.name FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.type = 'Complaint' GROUP BY T2.name ORDER BY COUNT(T1.business_id) DESC LIMIT 1"}],"db_id":"food_inspection"}23{"prompt":"Which team(s) has greater than 75% lost among all the games 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) has greater than 75% lost among all the games played.\n        \n        ### Context:\n        #### Query Context:\n        greater than 75% lost refers to Divide(lost, games) > 0.75; team refers to tmID\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'], 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'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 WHERE CAST(lost AS REAL) * 100 \/ games > 75"}],"db_id":"professional_basketball"}24{"prompt":"Among the calls from California, what percentage are priority 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        Among the calls from California, what percentage are priority 1?\n        \n        ### Context:\n        #### Query Context:\n        California refers to state = 'California'; percentage = MULTIPLY(DIVIDE(SUM(priority = 1), COUNT(\"Complaint ID\"), 1.0));\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 CAST(SUM(CASE WHEN T1.priority = 1 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.priority) FROM callcenterlogs AS T1 INNER JOIN client AS T2 ON T1.`rand client` = T2.client_id INNER JOIN district AS T3 ON T2.district_id = T3.district_id INNER JOIN state AS T4 ON T3.state_abbrev = T4.StateCode WHERE T4.State = 'California'"}],"db_id":"retail_complains"}25{"prompt":"In Shakespeare's works between 1600 to 1610, how many of these have a character as a \"Third Servingman\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Shakespeare's works between 1600 to 1610, how many of these have a character as a \"Third Servingman\"?\n        \n        ### Context:\n        #### Query Context:\n        between 1600 to 1610 refers to DATE > = 1600 AND DATE < = 1610; \"Third Servingman\" refers to CharName = 'Third Servingman'\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(DISTINCT T2.work_id) 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.DATE BETWEEN 1600 AND 1610 AND T4.CharName = 'Third Servingman'"}],"db_id":"shakespeare"}26{"prompt":"Provide the last name of the employee involved in the inspection ID 52256.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 last name of the employee involved in the inspection ID 52256.\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 T1.last_name FROM employee AS T1 INNER JOIN inspection AS T2 ON T1.employee_id = T2.employee_id WHERE T2.inspection_id = 52256"}],"db_id":"food_inspection_2"}27{"prompt":"Write down the top ten companies with the most total sales by 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        Write down the top ten companies with the most total sales by amount.\n        \n        ### Context:\n        #### Query Context:\n        most total sales refers to Max(SaleAmount); companies 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 CompanyName FROM `Sales Totals by Amount` ORDER BY SaleAmount DESC LIMIT 10"}],"db_id":"retail_world"}28{"prompt":"How many of the customer addresses are inactive?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 customer addresses are inactive?\n        \n        ### Context:\n        #### Query Context:\n        addresses are inactive refers to address_status = 'Inactive'\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 customer_address AS T1 INNER JOIN address_status AS T2 ON T1.status_id = T2.status_id WHERE T2.address_status = 'Inactive'"}],"db_id":"books"}29{"prompt":"What is the average surface area 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        What is the average surface area of all countries?\n        \n        ### Context:\n        #### Query Context:\n        average surface area = AVG(SurfaceArea);\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 AVG(SurfaceArea) FROM Country"}],"db_id":"world"}30{"prompt":"How much fat does the Raspberry Chiffon Pie 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 much fat does the Raspberry Chiffon Pie have?\n        \n        ### Context:\n        #### Query Context:\n        Raspberry Chiffon Pie 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 T2.total_fat FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id WHERE T1.title = 'Raspberry Chiffon Pie'"}],"db_id":"cookbook"}31{"prompt":"What are the top 5 nations of suppliers with the lowest 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        What are the top 5 nations of suppliers with the lowest account balance?\n        \n        ### Context:\n        #### Query Context:\n        nation refers to n_name; the lowest account balance refers to min(s_acctbal)\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.n_name FROM supplier AS T1 INNER JOIN nation AS T2 ON T1.s_nationkey = T2.n_nationkey ORDER BY T1.s_acctbal LIMIT 1"}],"db_id":"retails"}32{"prompt":"Between 1\/1\/2012 to 12\/31\/2014, which date recorded the hottest temperature in weather station 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        Between 1\/1\/2012 to 12\/31\/2014, which date recorded the hottest temperature in weather station 1?\n        \n        ### Context:\n        #### Query Context:\n        weather station 1 refers to station_nbr = 1; hottest temperature refers to Max(tmax); between 1\/1\/2012 to 12\/31\/2014 refers to SUBSTR(date, 1, 4) between 2012 and 2014\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 `date` FROM weather WHERE station_nbr = 1 AND CAST(SUBSTR(`date`, 1, 4) AS int) BETWEEN 2012 AND 2014 ORDER BY tmax DESC LIMIT 1"}],"db_id":"sales_in_weather"}33{"prompt":"What is the percentage of employees are in Paris 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        What is the percentage of employees are in Paris office?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(employeeNumber) when city = 'Paris'), (COUNT(employeeNumber)) 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(COUNT(CASE WHEN t1.city = 'Paris' THEN t2.employeeNumber ELSE NULL END) AS REAL) * 100 \/ COUNT(t2.employeeNumber) FROM offices AS t1 INNER JOIN employees AS t2 ON t1.officeCode = t2.officeCode"}],"db_id":"car_retails"}34{"prompt":"Among the games published by 10TACLE Studios, how many of them are puzzles?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 games published by 10TACLE Studios, how many of them are puzzles?\n        \n        ### Context:\n        #### Query Context:\n        published by 10TACLE Studios refers to publisher_name = '10TACLE Studios'; puzzle 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 COUNT(T1.id) 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 INNER JOIN genre AS T4 ON T1.genre_id = T4.id WHERE T4.genre_name = 'Puzzle' AND T3.publisher_name = '10TACLE Studios'"}],"db_id":"video_games"}35{"prompt":"Among the low quality product, which product has the highest line total? List the product name and its line 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        Among the low quality product, which product has the highest line total? List the product name and its line total?\n        \n        ### Context:\n        #### Query Context:\n        Low quality refers to the product's quality class, therefore Class = 'L'\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, 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[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, 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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 T1.Name, T2.LineTotal FROM Product AS T1 INNER JOIN PurchaseOrderDetail AS T2 ON T1.ProductID = T2.ProductID WHERE Class = 'L' ORDER BY OrderQty * UnitPrice DESC LIMIT 1"}],"db_id":"works_cycles"}36{"prompt":"What is the name of the most expensive car?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 expensive car?\n        \n        ### Context:\n        #### Query Context:\n        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 ORDER BY T2.price DESC LIMIT 1"}],"db_id":"cars"}37{"prompt":"Please provide the solution id of the respository among the respository that receive 238 forks.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 solution id of the respository among the respository that receive 238 forks.\n        \n        ### Context:\n        #### Query Context:\n        solution id of the repository refers to solution.Id;\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 T2.Id FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T1.Forks = 238"}],"db_id":"codebase_comments"}38{"prompt":"What percentage of businesses in the Northwest US have forecasted annual sales of above 300,000?","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 percentage of businesses in the Northwest US have forecasted annual sales of above 300,000?\n        \n        ### Context:\n        #### Query Context:\n        Northwest refers to Name = 'Northwest'; US refers to CountryRegionCode = 'US'; forecasted annual sales of above 300,000 refers to SalesQuota >300000; Percentage = Divide(Count(TerritoryID(SalesQuota >300000)),Count(TerritoryID))*100\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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'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, 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'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 CAST(SUM(CASE WHEN T1.SalesQuota > 300000 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.BusinessEntityID) FROM SalesPerson AS T1 INNER JOIN SalesTerritory AS T2 ON T1.TerritoryID = T2.TerritoryID WHERE T2.CountryRegionCode = 'US' AND T2.Name = 'Northwest'"}],"db_id":"works_cycles"}39{"prompt":"In which cities beginning with the letter M have the Olympic Games been 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 cities beginning with the letter M have the Olympic Games been held?\n        \n        ### Context:\n        #### Query Context:\n        cities beginning with the letter M refer to city_name LIKE '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 city_name FROM city WHERE city_name LIKE 'M%'"}],"db_id":"olympics"}40{"prompt":"How many employees working in the Engineering Department in 2007 would have their credit cards expired in the 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        How many employees working in the Engineering Department in 2007 would have their credit cards expired in the same year?\n        \n        ### Context:\n        #### Query Context:\n        year(StartDate) 2007; year of credit card expiration refers to ExpYear; ExpYear = 2007;\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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'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'], 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'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 T2.nameGiven, T3.name, T3.year FROM Coaches AS T1 INNER JOIN Master AS T2 ON T2.coachID = T1.coachID INNER JOIN Teams AS T3 ON T1.lgID = T3.lgID WHERE T2.playerID IS NOT NULL AND T2.coachID IS NOT NULL"}],"db_id":"hockey"}42{"prompt":"What is the id of the project that has the highest optional tip? Indicate the names of the resources that were requested.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 project that has the highest optional tip? Indicate the names of the resources that were requested.\n        \n        ### Context:\n        #### Query Context:\n        highest optional tip refers to subtract(total_price_including_optional_support, total_price_excluding_optional_support); names of the resources refers 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 T1.projectid, T1.item_name FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid ORDER BY T2.total_price_including_optional_support - T2.total_price_excluding_optional_support DESC LIMIT 1"}],"db_id":"donor"}43{"prompt":"List the categories of inactive businesses in AZ.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 categories of inactive businesses in AZ.\n        \n        ### Context:\n        #### Query Context:\n        inactive business refers to active = 'FALSE'; 'AZ' is the state; 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 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 'FALSE' AND T1.state LIKE 'AZ'"}],"db_id":"public_review_platform"}44{"prompt":"Calculate the total beat of the crimes reported in a community area in the central side with population of 50,000 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        Calculate the total beat of the crimes reported in a community area in the central side with population of 50,000 and above.\n        \n        ### Context:\n        #### Query Context:\n        the central side refers to side = 'Central'; population of 50,000 and above refers to population > '50000'; total beat = sum(beat) where side = 'Central' and population > '50000'\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 1.0 * SUM(CASE WHEN T1.population > 50000 THEN T2.beat ELSE 0 END) AS sum FROM Community_Area AS T1 INNER JOIN Crime AS T2 ON T2.community_area_no = T1.community_area_no WHERE T1.side = 'Central'"}],"db_id":"chicago_crime"}45{"prompt":"Which region does \"Supplier#000000129\" 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        Which region does \"Supplier#000000129\" belong to?\n        \n        ### Context:\n        #### Query Context:\n        \"Supplier#000000129\" is the name of the supplier which refers to s_name; Which region 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 supplier AS T2 ON T1.n_nationkey = T2.s_nationkey INNER JOIN region AS T3 ON T1.n_regionkey = T3.r_regionkey WHERE T2.s_name = 'Supplier#000000129'"}],"db_id":"retails"}46{"prompt":"In the reviews of September 2014. Which brand of beers obtained the highest star 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        In the reviews of September 2014. Which brand of beers obtained the highest star ratings?\n        \n        ### Context:\n        #### Query Context:\n        September 2014 refers to ReviewDate LIKE '2014-09%'; brand of beers refers to BrandName; highest star ratings refers to MAX(StarRating);\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 AND T2.ReviewDate BETWEEN '2014-09-01' AND '2014-09-30'"}],"db_id":"beer_factory"}47{"prompt":"Who did \"The Tiny Canadian\" play as in the show?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 did \"The Tiny Canadian\" play as in the show?\n        \n        ### Context:\n        #### Query Context:\n        \"The Tiny Canadian\" refers to nickname = 'The Tiny Canadian'; play as in the show refers to role\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 T2.role FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.name = T2.person WHERE T1.nickname = 'The Tiny Canadian';"}],"db_id":"simpson_episodes"}48{"prompt":"List down the keyword and crew member's name for episode id S20-E1.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 keyword and crew member's name for episode id S20-E1.\n        \n        ### Context:\n        #### Query Context:\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 T1.keyword, T2.person FROM Keyword AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id WHERE T1.episode_id = 'S20-E1';"}],"db_id":"simpson_episodes"}49{"prompt":"Please list the names of the movies that have been rated the most times in 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        Please list the names of the movies that have been rated the most times in 2020.\n        \n        ### Context:\n        #### Query Context:\n        in 2020 refers to rating_timestamp_utc = '2020%'; rated the most times refers to Max(Count(movie_title));\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 T1.rating_timestamp_utc LIKE '2020%' GROUP BY T2.movie_title ORDER BY COUNT(T2.movie_title) DESC LIMIT 1"}],"db_id":"movie_platform"}50{"prompt":"How many representatives are there in the state with the highest monthly benefit payments for retired workers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 representatives are there in the state with the highest monthly benefit payments for retired workers?\n        \n        ### Context:\n        #### Query Context:\n        state with highest monthly benefits payment for retired workers refers to Max(monthly_benefits_retired_workers)\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(T3.cognress_rep_id) FROM zip_data AS T1 INNER JOIN state AS T2 ON T1.state = T2.abbreviation INNER JOIN congress AS T3 ON T2.abbreviation = T3.abbreviation ORDER BY T1.monthly_benefits_retired_workers DESC LIMIT 1"}],"db_id":"address"}51{"prompt":"How much money did the customer No.297 pay for the rental which happened at 12:27:27 on 2005\/7\/28?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 did the customer No.297 pay for the rental which happened at 12:27:27 on 2005\/7\/28?\n        \n        ### Context:\n        #### Query Context:\n        customer no. 297 refers to customer_id = 297; at 12:27:27 on 2005\/7\/28 refers to rental_date = '2005-07-28 12:27:27'; money pay for rent refers to amount\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.amount FROM payment AS T1 INNER JOIN rental AS T2 ON T1.rental_id = T2.rental_id WHERE T2.rental_date = '2005-07-28 12:27:27' AND T2.customer_id = 297"}],"db_id":"movie_3"}52{"prompt":"How many players were born 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        How many players were born in 1970?\n        \n        ### Context:\n        #### Query Context:\n        born in 1970 refers to DOB like '1970%';\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_Name) FROM Player WHERE DOB LIKE '1970%'"}],"db_id":"soccer_2016"}53{"prompt":"Please list the full names of all the sanitarians who have inspected the facility Burbank.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 who have inspected the facility Burbank.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name; the facility Burbank refers to dba_name = 'Burbank'\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 T1.first_name, T1.last_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 T3.dba_name = 'Burbank' AND T1.title = 'Sanitarian'"}],"db_id":"food_inspection_2"}54{"prompt":"Which product has the highest total amount of quantity sold? Calculate its overall total 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        Which product has the highest total amount of quantity sold? Calculate its overall total price.\n        \n        ### Context:\n        #### Query Context:\n        highest total amount of quantity refers to MAX(Quantity); overall total price = 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 T1.Name, SUM(T2.Quantity * T1.Price) FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID GROUP BY T1.ProductID, T1.Name ORDER BY SUM(T2.Quantity) DESC LIMIT 1"}],"db_id":"sales"}55{"prompt":"Describe the course level and list of person IDs who taught course ID of 147.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 course level and list of person IDs who taught course ID of 147.\n        \n        ### Context:\n        #### Query Context:\n        person IDs refers to taughtBy.p_id; course ID of 147 refers to course.course_id = 147\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, T1.course_id FROM course AS T1 INNER JOIN taughtBy AS T2 ON T1.course_id = T2.course_id WHERE T2.p_id = 141"}],"db_id":"computer_student"}56{"prompt":"Please list the title of the pages on which the word \"grec\" occurred for over 20 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        Please list the title of the pages on which the word \"grec\" occurred for over 20 times.\n        \n        ### Context:\n        #### Query Context:\n        occurred for over 20 times refers to occurrences > 20;\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 T3.title 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' AND T2.occurrences > 20"}],"db_id":"language_corpus"}57{"prompt":"What percentage of matches played on 2005\/07\/30 belong to the F1 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        What percentage of matches played on 2005\/07\/30 belong to the F1 division?\n        \n        ### Context:\n        #### Query Context:\n        Division refers to Div; DIVIDE(COUNT(Div = 'F1', Date = '2005\/07\/30'), COUNT(Div, Date = '2005\/07\/30')) 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 Div = 'F1' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(Div) FROM matchs WHERE Date = '2005-07-30' "}],"db_id":"european_football_1"}58{"prompt":"Which professor advised Faina Mallinar to become a research assistant? Please give his or 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        Which professor advised Faina Mallinar to become a research assistant? Please give his or her full name.\n        \n        ### Context:\n        #### Query Context:\n        research assistant refers to the student who serves for research where the abbreviation is RA; full name refers to f_name and l_name;\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.first_name, T1.last_name FROM prof AS T1 INNER JOIN RA AS T2 ON T1.prof_id = T2.prof_id INNER JOIN student AS T3 ON T2.student_id = T3.student_id WHERE T3.f_name = 'Faina' AND T3.l_name = 'Mallinar'"}],"db_id":"cs_semester"}59{"prompt":"How many units of item no.5 were sold in store no.3 on the day the temperature range was the biggest?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 no.5 were sold in store no.3 on the day the temperature range was the biggest?\n        \n        ### Context:\n        #### Query Context:\n        item no. 5 refers to item_nbr = 5; store no.3 refers to store_nbr = 3; when the temperature range was the biggest refers to Max(Subtract(tmax, tmin))\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 t2.units FROM relation AS T1 INNER JOIN sales_in_weather AS T2 ON T1.store_nbr = T2.store_nbr INNER JOIN weather AS T3 ON T1.station_nbr = T3.station_nbr WHERE T2.store_nbr = 3 AND T2.item_nbr = 5 ORDER BY t3.tmax - t3.tmin DESC LIMIT 1"}],"db_id":"sales_in_weather"}60{"prompt":"List the movie that has been rated most by 25 years old 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 the movie that has been rated most by 25 years old users.\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.movieid FROM users AS T1 INNER JOIN u2base AS T2 ON T1.userid = T2.userid WHERE T1.age = 25 GROUP BY T2.movieid ORDER BY COUNT(T1.userid) DESC LIMIT 1"}],"db_id":"movielens"}61{"prompt":"What's the royalty for the bestseller 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's the royalty for the bestseller book?\n        \n        ### Context:\n        #### Query Context:\n        qty is abbreviation for quantity; bestseller means with the most sales quantity; MAX(qty)\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 royalty FROM titles ORDER BY ytd_sales DESC LIMIT 1"}],"db_id":"book_publishing_company"}62{"prompt":"Among all attribute names, list down the ID and attribute name which start with \"music\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 attribute names, list down the ID and attribute name which start with \"music\".\n        \n        ### Context:\n        #### Query Context:\n        attribute name which start with \"music\" refers to attribute_name LIKE 'music%'\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 attribute_id, attribute_name FROM Attributes WHERE attribute_name LIKE 'music%'"}],"db_id":"public_review_platform"}63{"prompt":"For the root beer brand with the most 5 star ratings, what is the name of the brewery?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 root beer brand with the most 5 star ratings, what is the name of the brewery?\n        \n        ### Context:\n        #### Query Context:\n        most 5 star ratings refers to MAX(COUNT(StarRating = 5)); name of the brewery refers to BreweryName;\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.BreweryName FROM rootbeerbrand AS T1 INNER JOIN rootbeerreview AS T2 ON T1.BrandID = T2.BrandID WHERE T2.StarRating = 5 GROUP BY T1.BrandID ORDER BY COUNT(T2.StarRating) DESC LIMIT 1"}],"db_id":"beer_factory"}64{"prompt":"How many users who started yelping since 2012 have sent a high number of funny 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        How many users who started yelping since 2012 have sent a high number of funny votes?\n        \n        ### Context:\n        #### Query Context:\n        users who started yelping in 2012 refers to user_yelping_since_year = '2012'; high number of funny votes refers to user_votes_funny = '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(user_id) FROM Users WHERE user_yelping_since_year = 2012 AND user_votes_funny LIKE 'High'"}],"db_id":"public_review_platform"}65{"prompt":"Calculate the average number of criterias among \"Times Higher Education World University Ranking\",\"Shanghai Ranking\" and \"Center for World University Rankings\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 criterias among \"Times Higher Education World University Ranking\",\"Shanghai Ranking\" and \"Center for World University Rankings\".\n        \n        ### Context:\n        #### Query Context:\n        average number of criterias refers to DIVIDE(SUM(id), 3); \"Times Higher Education World University Ranking\", \"Shanghai Ranking\" and \"Center for World University Rankings\" refers to system_name IN ('Times Higher Education World University Ranking', 'Shanghai Ranking', 'Center for World University Rankings');\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(CASE WHEN T1.system_name = 'Center for World University Rankings' THEN 1 ELSE 0 END) + SUM(CASE WHEN T1.system_name = 'Shanghai Ranking' THEN 1 ELSE 0 END) + SUM(CASE WHEN T1.system_name = 'Times Higher Education World University Ranking' THEN 1 ELSE 0 END)) \/ 3 FROM ranking_system AS T1 INNER JOIN ranking_criteria AS T2 ON T1.id = T2.ranking_system_id"}],"db_id":"university"}66{"prompt":"When did restaurant John Schaller has its first inspection in 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        When did restaurant John Schaller has its first inspection in 2010?\n        \n        ### Context:\n        #### Query Context:\n        John Schaller refers to dba_name = 'JOHN SCHALLER'; first inspection refers to min(inspection_date); in 2010 refers to inspection_date like '2010%'\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 MIN(T2.inspection_date) FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T1.dba_name = 'JOHN SCHALLER' AND strftime('%Y', T2.inspection_date) = '2010'"}],"db_id":"food_inspection_2"}67{"prompt":"How many schools in urban area requested for books 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        How many schools in urban area requested for books resources?\n        \n        ### Context:\n        #### Query Context:\n        urban area refers to school_metro = 'urban'; books resources refers to project_resource_type = 'Books'\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 WHERE T2.resource_type = 'Books' AND T2.school_metro = 'urban'"}],"db_id":"donor"}68{"prompt":"Who among the players in season 2000-2001 has committed the highest rule violations or penalty 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        Who among the players in season 2000-2001 has committed the highest rule violations or penalty minutes?\n        \n        ### Context:\n        #### Query Context:\n        committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001'\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 SeasonStatus AS T1 INNER JOIN PlayerInfo AS T2 ON T1.ELITEID = T2.ELITEID WHERE T1.SEASON = '2000-2001' ORDER BY T1.PIM DESC LIMIT 1"}],"db_id":"ice_hockey_draft"}69{"prompt":"For all the products, list the product name and its corresponding start date for the current 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        For all the products, list the product name and its corresponding start date for the current standard cost.\n        \n        ### Context:\n        #### Query Context:\n        The current standard cost refers to EndDate 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', '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 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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.Name, T2.StartDate FROM Product AS T1 INNER JOIN ProductCostHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T2.EndDate IS NULL"}],"db_id":"works_cycles"}70{"prompt":"What is the name of the product that is most sold by sale person id 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        What is the name of the product that is most sold by sale person id 20?\n        \n        ### Context:\n        #### Query Context:\n        most sold refers to MAX(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 T1.Name FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T2.SalesPersonID = 20 ORDER BY T2.Quantity DESC LIMIT 1"}],"db_id":"sales"}71{"prompt":"How many games include the word 'Box' in their 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        How many games include the word 'Box' in their name?\n        \n        ### Context:\n        #### Query Context:\n        games include the word 'Box' in their name refers to game_name = '%Box%';\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(*) FROM ( SELECT T.game_name FROM game AS T WHERE T.game_name LIKE '%Box%' )"}],"db_id":"video_games"}72{"prompt":"What are the names of the three nations where the longest river that empties into the Atlantic Ocean stretches 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 are the names of the three nations where the longest river that empties into the Atlantic Ocean stretches to?\n        \n        ### Context:\n        #### Query Context:\n        Empties into the Atlantic Ocean = flows to the Atlantic Ocean\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 DISTINCT T1.Country 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 T3.Name = ( SELECT Name FROM river WHERE Sea = 'Atlantic Ocean' ORDER BY Length DESC LIMIT 1 )"}],"db_id":"mondial_geo"}73{"prompt":"How many students in the Air 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        How many students in the Air Force?\n        \n        ### Context:\n        #### Query Context:\n        Air Force refers to organ = 'air_force';\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 = 'air_force'"}],"db_id":"student_loan"}74{"prompt":"What's different average revenue status for director who directed the movie that got the most 1 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        What's different average revenue status for director who directed the movie that got the most 1 ratings?\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 DISTINCT T1.avg_revenue FROM directors AS T1 INNER JOIN movies2directors AS T2 ON T1.directorid = T2.directorid WHERE T1.d_quality = 5"}],"db_id":"movielens"}75{"prompt":"Provide match ID which had the extra type of penalty.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 match ID which had the extra type of penalty.\n        \n        ### Context:\n        #### Query Context:\n        extra type of penalty refers to Extra_Name = 'penalty';\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.Match_Id FROM Extra_Runs AS T1 INNER JOIN Extra_Type AS T2 ON T1.Extra_Type_Id = T2.Extra_Id WHERE T2.Extra_Name = 'penalty'"}],"db_id":"soccer_2016"}76{"prompt":"Please list the IDs of the teachers who have advised more than 4 others to teach.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 teachers who have advised more than 4 others to teach.\n        \n        ### Context:\n        #### Query Context:\n        teachers refers to p_id_dummy; have advised more than 4 others refers to count(advisedBy.p_id) > 4\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_dummy FROM advisedBy GROUP BY p_id_dummy HAVING COUNT(p_id_dummy) > 4"}],"db_id":"computer_student"}77{"prompt":"Which customer ordered 'Global High-Back Leather Tilter, Burgundy' on 10\/13\/2013 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        Which customer ordered 'Global High-Back Leather Tilter, Burgundy' on 10\/13\/2013 in the East region?\n        \n        ### Context:\n        #### Query Context:\n        'Global High-Back Leather Tilter, Burgundy' is the \"Product Name\"; on 10\/13\/2013 refers to \"Order Date\" = Date('2013-10-13'); 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 DISTINCT T2.`Customer 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 T3.`Product Name` = 'Global High-Back Leather Tilter, Burgundy' AND T1.`Order Date` = '2013-10-13' AND T1.Region = 'East'"}],"db_id":"superstore"}78{"prompt":"What is the average population for all cities location at Baltic 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        What is the average population for all cities location at Baltic Sea?\n        \n        ### Context:\n        #### Query Context:\n        Baltic Sea is a sea located in Northern Europe\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 AVG(T1.Population) FROM city AS T1 INNER JOIN located AS T2 ON T1.Name = T2.City INNER JOIN sea AS T3 ON T3.Name = T2.Sea WHERE T3.Name = 'Baltic Sea'"}],"db_id":"mondial_geo"}79{"prompt":"What type of transaction was made with the only yellow product, size 62 and with a minimum inventory stock of 500 units?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 type of transaction was made with the only yellow product, size 62 and with a minimum inventory stock of 500 units?\n        \n        ### Context:\n        #### Query Context:\n        yellow product refers to Color = 'Yellow'; minimum inventory stock of 500 units refers to SafetyStockLevel = 500\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', 'datetime', 'text', 'datetime', 'integer', 'text', 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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 DISTINCT T2.TransactionType FROM Product AS T1 INNER JOIN TransactionHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Size = 62 AND T1.Color = 'Yellow' AND T1.SafetyStockLevel = 500"}],"db_id":"works_cycles"}80{"prompt":"List out female students that enrolled in occ school and ulca?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 female students that enrolled in occ school and ulca?\n        \n        ### Context:\n        #### Query Context:\n        female students refers to enrolled.name who are NOT in male.name; occ school and ulca refers to school IN('occ', 'ulca');\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 enrolled WHERE school IN ('occ', 'ulca') AND name NOT IN ( SELECT name FROM male )"}],"db_id":"student_loan"}81{"prompt":"Provide the names of competitors who received a gold medal.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 names of competitors who received a gold medal.\n        \n        ### Context:\n        #### Query Context:\n        names of competitors refer to full_name; gold medal refers to medal_name = 'Gold';\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 DISTINCT T1.full_name FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id INNER JOIN competitor_event AS T3 ON T2.id = T3.competitor_id INNER JOIN medal AS T4 ON T3.medal_id = T4.id WHERE T4.medal_name = 'Gold'"}],"db_id":"olympics"}82{"prompt":"What is the tokenized name of the solution whose path is \"maravillas_linq-to-delicious\\tasty.sln\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 tokenized name of the solution whose path is \"maravillas_linq-to-delicious\\tasty.sln\"?\n        \n        ### Context:\n        #### Query Context:\n        tokenized name refers to NameTokenized\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.NameTokenized FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T1.Path = 'maravillas_linq-to-delicious'"}],"db_id":"codebase_comments"}83{"prompt":"How many matches did team Kings XI Punjab win in season year 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        How many matches did team Kings XI Punjab win in season year 2008?\n        \n        ### Context:\n        #### Query Context:\n        in season year 2008 refers to Season_Year = 2008; team Kings XI Punjab refers to Team_Name = 'Kings XI Punjab'\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(DISTINCT T2.Match_Id) 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 Season AS T4 ON T2.Season_Id = T4.Season_Id WHERE T1.Team_Name = 'Kings XI Punjab' AND T4.Season_Year = 2008"}],"db_id":"soccer_2016"}84{"prompt":"Show the solution path for the method \"Mosa.Platform.x86.Instructions.IMul.EmitLegacy\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 solution path for the method \"Mosa.Platform.x86.Instructions.IMul.EmitLegacy\"?\n        \n        ### Context:\n        #### Query Context:\n        solution path refers to Path; method refers to Name; Name = 'Mosa.Platform.x86.Instructions.IMul.EmitLegacy';\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 = 'Mosa.Platform.x86.Instructions.IMul.EmitLegacy'"}],"db_id":"codebase_comments"}85{"prompt":"Among the cars from Asia, list the IDs of cars that were introduced in 1979.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 from Asia, list the IDs of cars that were introduced in 1979.\n        \n        ### Context:\n        #### Query Context:\n        from Asia refers to country = 'Japan'; introduced in 1979 refers to model_year = 1979\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.ID FROM production AS T1 INNER JOIN country AS T2 ON T1.country = T2.origin WHERE T2.country = 'Japan' AND T1.model_year = 1979"}],"db_id":"cars"}86{"prompt":"Name the Chilliwack Chiefs players who have scored 100 points or more in the NHL.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Chilliwack Chiefs players who have scored 100 points or more in the NHL.\n        \n        ### Context:\n        #### Query Context:\n        name of the player refers to PlayerName; Chilliwack Chiefs refers to TEAM = 'Chilliwack Chiefs'; scored 100 points or more in the NHL refers to P > 100;\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 SeasonStatus AS T1 INNER JOIN PlayerInfo AS T2 ON T1.ELITEID = T2.ELITEID WHERE T1.TEAM = 'Chilliwack Chiefs' AND T1.P >= 100"}],"db_id":"ice_hockey_draft"}87{"prompt":"Write the title and all the keywords of the episode that was aired on 3\/22\/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        Write the title and all the keywords of the episode that was aired on 3\/22\/2009.\n        \n        ### Context:\n        #### Query Context:\n        aired on 3\/22\/2009 refers to air_date = '2009-03-22'\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.title, T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.air_date = '2009-03-22';"}],"db_id":"simpson_episodes"}88{"prompt":"How many bicycle trip were made within San Jose city during August 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        How many bicycle trip were made within San Jose city during August 2013?\n        \n        ### Context:\n        #### Query Context:\n        during August 2013 refers to start_date like '8\/%\/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 COUNT(T2.id) FROM station AS T1 INNER JOIN trip AS T2 ON T2.start_station_name = T1.name WHERE T1.city = 'San Jose' AND T2.start_date LIKE '8\/%\/2013%' AND T2.start_station_name LIKE 'San Jose%' AND T2.end_station_name LIKE 'San Jose%'"}],"db_id":"bike_share_1"}89{"prompt":"Count all of the living patients that had a stroke.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 all of the living patients that had a stroke.\n        \n        ### Context:\n        #### Query Context:\n        if deathdate is null, it means this patient is still alive or living; stroke refers to conditions where DESCRIPTION = 'Stroke';\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 conditions AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T1.description = 'Stroke' AND T2.deathdate IS NULL"}],"db_id":"synthea"}90{"prompt":"What are the optional ingredients for Warm Chinese Chicken Salad?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 optional ingredients for Warm Chinese Chicken Salad?\n        \n        ### Context:\n        #### Query Context:\n        optional refers to optional = 'TRUE'; Warm Chinese Chicken Salad 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 T3.name 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 WHERE T1.title = 'Warm Chinese Chicken Salad' AND T2.optional = 'TRUE'"}],"db_id":"cookbook"}91{"prompt":"List all cities in the Northern California 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 all cities in the Northern California Region.\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 city FROM geographic WHERE region = 'northern california'"}],"db_id":"restaurant"}92{"prompt":"List all product names under Confections.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 product names under Confections.\n        \n        ### Context:\n        #### Query Context:\n        'Confections' is a 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 T1.ProductName FROM Products AS T1 INNER JOIN Categories AS T2 ON T1.CategoryID = T2.CategoryID WHERE T2.CategoryName = 'Confections'"}],"db_id":"retail_world"}93{"prompt":"What is the average monthly circulating supply for Frozen 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        What is the average monthly circulating supply for Frozen in 2014.\n        \n        ### Context:\n        #### Query Context:\n        average monthly circulating supply = AVG(circulating_supply); in 2017 refers to date BETWEEN '2017-01-01' AND '2017-12-31'\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 CAST(SUM(T2.circulating_supply) AS REAL) \/ 12 FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T1.name = 'Frozen' AND STRFTIME('%Y', T2.date) = '2014'"}],"db_id":"coinmarketcap"}94{"prompt":"Calculate the total number of sales in North America.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of sales in North America.\n        \n        ### Context:\n        #### Query Context:\n        total number of sales = MULTIPLY(SUM(num_sales), 100000); North America refers to region_name = 'North America';\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(T2.num_sales) * 100000 AS nums FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id WHERE T1.region_name = 'North America'"}],"db_id":"video_games"}95{"prompt":"Among the countries with a population of over 10000000, how many of them have a GDP of over 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        Among the countries with a population of over 10000000, how many of them have a GDP of over 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 COUNT(T1.Name) FROM country AS T1 INNER JOIN economy AS T2 ON T1.Code = T2.Country WHERE T2.GDP > 500000 AND T1.Population > 10000000"}],"db_id":"mondial_geo"}96{"prompt":"Please give the url of the repository whose files are contained in solution ID 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        Please give the url of the repository whose files are contained in solution ID 9?\n        \n        ### Context:\n        #### Query Context:\n        \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.Url FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T2.Id = 9"}],"db_id":"codebase_comments"}97{"prompt":"List the chapter ID of the works with a year greater than the 89% of average year of all listed works 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        List the chapter ID of the works with a year greater than the 89% of average year of all listed works of Shakespeare.\n        \n        ### Context:\n        #### Query Context:\n        a year greater than the 89% of average year refers to DATE > multiply(divide(SUM(DATE) , COUNT(DATE)), 0.89)\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.id FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.DATE > ( SELECT AVG(DATE) FROM works ) * 0.89"}],"db_id":"shakespeare"}98{"prompt":"What is the last name of driver who transported shipment id 1088?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 last name of driver who transported shipment id 1088?\n        \n        ### Context:\n        #### Query Context:\n        shipment id 1088 refers to ship_id = 1088\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.last_name FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id WHERE T1.ship_id = '1088'"}],"db_id":"shipping"}99{"prompt":"How many French customers shipped 2 orders which have been cancelled?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 French customers shipped 2 orders which have been cancelled?\n        \n        ### Context:\n        #### Query Context:\n        French is a nationality of country = 'France'; cancelled orders refers to status = 'Cancelled';\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(T2.country) FROM orders AS T1 INNER JOIN customers AS T2 ON T1.customerNumber = T2.customerNumber WHERE T1.status = 'Shipped' AND T2.country = 'France' GROUP BY T2.customerNumber HAVING COUNT(T1.status) = 2"}],"db_id":"car_retails"}100{"prompt":"How many Summer games are there that were held in Paris?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Summer games are there that were held in Paris?\n        \n        ### Context:\n        #### Query Context:\n        Summer games refer to games_id where season = 'Summer'; Paris refers to city_name = 'Paris';\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(T3.id) 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 T2.city_name = 'Paris' AND T3.season = 'Summer'"}],"db_id":"olympics"}101{"prompt":"How many free or gift products 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 free or gift products are there?\n        \n        ### Context:\n        #### Query Context:\n        free gift refers to Price = 0;\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(ProductID) FROM Products WHERE Price = 0"}],"db_id":"sales"}102{"prompt":"What are the keywords of the episode which has title as Dangerous Curves?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 keywords of the episode which has title as Dangerous Curves?\n        \n        ### Context:\n        #### Query Context:\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 T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Dangerous Curves';"}],"db_id":"simpson_episodes"}103{"prompt":"List any five female students' names who enlisted for the air 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        List any five female students' names who enlisted for the air force.\n        \n        ### Context:\n        #### Query Context:\n        female students refers to enlist.name who are NOT in male.name; air force refers to organ = 'air_force';\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 enlist AS T1 LEFT JOIN male AS T2 ON T2.name = T1.name WHERE T2.name IS NULL AND T1.organ = 'air_force' LIMIT 5"}],"db_id":"student_loan"}104{"prompt":"What was the first job position that the company needed, and who was hired? Indicate 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 was the first job position that the company needed, and who was hired? Indicate his\/her full name.\n        \n        ### Context:\n        #### Query Context:\n        job position and job title 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 T1.JobTitle, T2.FirstName, T2.MiddleName, T2.LastName FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID ORDER BY T1.HireDate LIMIT 1"}],"db_id":"works_cycles"}105{"prompt":"Which region has the highest number of games sold on all platforms?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of games sold on all platforms?\n        \n        ### Context:\n        #### Query Context:\n        which region refers to region_name; highest number of games sold on all platforms refers to MAX(SUM(num_sales));\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.region_name FROM ( SELECT T2.region_name, SUM(T1.num_sales) FROM region_sales AS T1 INNER JOIN region AS T2 ON T1.region_id = T2.id INNER JOIN game_platform AS T3 ON T1.game_platform_id = T3.id INNER JOIN platform AS T4 ON T3.platform_id = T4.id GROUP BY T4.platform_name ORDER BY SUM(T1.num_sales) DESC LIMIT 1 ) t"}],"db_id":"video_games"}106{"prompt":"Of the projects whose resources are provided by the vendor Lakeshore Learning Materials, the school of which project has the highest cost of labor fulfillment? Please give its school 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        Of the projects whose resources are provided by the vendor Lakeshore Learning Materials, the school of which project has the highest cost of labor fulfillment? Please give its school ID.\n        \n        ### Context:\n        #### Query Context:\n        Lakeshore Learning Materials is vendor_name; highest cost of labor fulfillment refers to Max(fulfillment_labor_materials);\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 = 'Lakeshore Learning Materials' ORDER BY T2.fulfillment_labor_materials DESC LIMIT 1"}],"db_id":"donor"}107{"prompt":"What is the scheduled local departure time and the actual departure time of the flight from Philadelphia to Harrisburg with the plane's tail number N627AE on the 13th of August 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        What is the scheduled local departure time and the actual departure time of the flight from Philadelphia to Harrisburg with the plane's tail number N627AE on the 13th of August 2018?\n        \n        ### Context:\n        #### Query Context:\n        scheduled local departure time refers to CRS_DEP_TIME; actual departure time refers to DEP_TIME; from Philadelphia refers to ORIGIN = 'PHL'; to Harrisburg refers to DEST = 'MDT'; tail number refers to TAIL_NUM; TAIL_NUM = 'N627AE'; on the 13th of August 2018 refers to FL_DATE = '2018\/8\/13';\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 CRS_DEP_TIME, DEP_TIME FROM Airlines WHERE ORIGIN = 'PHL' AND DEST = 'MDT' AND TAIL_NUM = 'N627AE' AND FL_DATE = '2018\/8\/13'"}],"db_id":"airline"}108{"prompt":"What is\/are the ids of the tallest patient\/s?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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\/are the ids of the tallest patient\/s?\n        \n        ### Context:\n        #### Query Context:\n        id of the tallest patient\/s refers to PATIENT from observations where MAX(DESCRIPTION = 'Body Height');\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 PATIENT FROM observations WHERE DESCRIPTION = 'Body Height' AND UNITS = 'cm' ORDER BY VALUE DESC LIMIT 1"}],"db_id":"synthea"}109{"prompt":"What is the student staff ratio of the university with the highest student staff ratio 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 student staff ratio of the university with the highest student staff ratio of all time?\n        \n        ### Context:\n        #### Query Context:\n        highest student staff ratio refers to max(student_staff_ratio)\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 MAX(student_staff_ratio) FROM university_year WHERE student_staff_ratio = ( SELECT MAX(student_staff_ratio) FROM university_year )"}],"db_id":"university"}110{"prompt":"Write down the award ID, award name and winner for character named \"Homer simpson 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        Write down the award ID, award name and winner for character named \"Homer simpson 20\".\n        \n        ### Context:\n        #### Query Context:\n        \"Homer simpson 20\" is the character; \"Winner\" is the result; award name refers to award\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.award_id, T1.award, T1.person FROM Award AS T1 INNER JOIN Character_Award AS T2 ON T1.award_id = T2.award_id WHERE T2.character = 'Homer Simpson' AND T1.result = 'Winner';"}],"db_id":"simpson_episodes"}111{"prompt":"How many products with a thumpnail 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        How many products with a thumpnail photo?\n        \n        ### Context:\n        #### Query Context:\n        products with a thumbnail photo refers to ProductPhotoID ! = 1 ;\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 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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'], [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', 'datetime', 'text', 'datetime', 'integer', 'text', 'datetime', 'integer', 'text', 'text', 'text', 'datetime', 'integer', '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 COUNT(ProductID) FROM ProductProductPhoto WHERE ProductPhotoID != 1"}],"db_id":"works_cycles"}112{"prompt":"How many games in the database belong to the genre of 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        How many games in the database belong to the genre of sports?\n        \n        ### Context:\n        #### Query Context:\n        the genre of sports refers to genre_name = 'Sports'\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 = 'Sports'"}],"db_id":"video_games"}113{"prompt":"Among the facilities that have undergone at least one inspection in 2010, how many of them are restaurants or cafeterias?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 facilities that have undergone at least one inspection in 2010, how many of them are restaurants or cafeterias?\n        \n        ### Context:\n        #### Query Context:\n        in 2010 refers to inspection_date like '2010%'; restaurant or cafeteria refers to facility_type = 'Restaurant'\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 inspection AS T1 INNER JOIN establishment AS T2 ON T1.license_no = T2.license_no WHERE strftime('%Y', T1.inspection_date) = '2010' AND T2.facility_type = 'Restaurant'"}],"db_id":"food_inspection_2"}114{"prompt":"Among the films starred by Reese West, what is the difference between the films that have store ID of 1 and store ID 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        Among the films starred by Reese West, what is the difference between the films that have store ID of 1 and store ID of 2?\n        \n        ### Context:\n        #### Query Context:\n        result = subtract(count(film_id where store_id = 1), count(film_id where store_id = 2))\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(IIF(T4.film_id = 1, 1, 0)) - SUM(IIF(T4.film_id = 2, 1, 0)) AS diff FROM film_actor AS T1 INNER JOIN actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T1.film_id = T3.film_id INNER JOIN inventory AS T4 ON T3.film_id = T4.film_id WHERE T2.first_name = 'Reese' AND T2.last_name = 'West'"}],"db_id":"movie_3"}115{"prompt":"Sum up the number sales ids handled by employees called Morningstar, Heather and Dean.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Sum up the number sales ids handled by employees called Morningstar, Heather and Dean.\n        \n        ### Context:\n        #### Query Context:\n        SUM = ADD(SUM(SalesID WHERE FirstName = 'Morningstar'), SUM(SalesID WHERE FirstName = 'Heather'), SUM(SalesID WHERE FirstName = 'Dean'));\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(IIF(T2.FirstName = 'Morningstar', 1, 0)) + SUM(IIF(T2.FirstName = 'Heather', 1, 0)) + SUM(IIF(T2.FirstName = 'Dean', 1, 0)) AS num FROM Sales AS T1 INNER JOIN Employees AS T2 ON T1.SalesPersonID = T2.EmployeeID"}],"db_id":"sales"}116{"prompt":"How many no.9 items from store no.11 were sold on 2012\/12\/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        How many no.9 items from store no.11 were sold on 2012\/12\/7?\n        \n        ### Context:\n        #### Query Context:\n        no. 9 item refers to item_nbr = 9; store no.11 refers to store_nbr = 11; sold on 2012\/12\/7 refers to date = '2012-12-07'\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 units FROM sales_in_weather WHERE `date` = '2012-12-07' AND store_nbr = 11 AND item_nbr = 9"}],"db_id":"sales_in_weather"}117{"prompt":"What is the cost and the product number of product with the id \"888\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 and the product number of product with the id \"888\"?\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, '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, 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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.StandardCost, T2.ProductNumber FROM ProductCostHistory AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ProductID = 888"}],"db_id":"works_cycles"}118{"prompt":"What is the horsepower and model year of the car named Subaru Dl?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 horsepower and model year of the car named Subaru Dl?\n        \n        ### Context:\n        #### Query Context:\n        the car named Subaru Dl refers to car_name = 'subaru dl'\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.horsepower, T2.model_year FROM data AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID WHERE T1.car_name = 'subaru dl'"}],"db_id":"cars"}119{"prompt":"Please list the dates on which the temperature of station no.2 was above the 30-year normal.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 dates on which the temperature of station no.2 was above the 30-year normal.\n        \n        ### Context:\n        #### Query Context:\n        temperature above the 30-year normal refers to depart > 0; station no.2 refers to station_nbr = 2\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 `date` FROM weather WHERE station_nbr = 2 AND depart > 0"}],"db_id":"sales_in_weather"}120{"prompt":"Please calculate the average temperature of those trips that started at Market at 4th 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        Please calculate the average temperature of those trips that started at Market at 4th in 2013.\n        \n        ### Context:\n        #### Query Context:\n        started at refers to start_station_name; start station_name = 'Market at 4th'; in 2013 refers to start_date like '%2013%'; temperature refers to mean_temperature_f; average temperature = DIVIDE(SUM(mean_temperature_f), COUNT(mean_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 AVG(T2.mean_temperature_f) 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 T1.start_station_name = 'Market at 4th'"}],"db_id":"bike_share_1"}121{"prompt":"For the Ligue 2 game that made the most goals, who is the winner of that 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        For the Ligue 2 game that made the most goals, who is the winner of that game?\n        \n        ### Context:\n        #### Query Context:\n        Ligue 2 is the name of division; the most goals refer to MAX(SUM(FTHG, FTAG)) which are short names for Final-time Home-team Goals and Final-time Away-team Goals; winner refers to FTR = 'A';\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 T1.FTR = 'H' THEN T1.HomeTeam ELSE T1.AwayTeam END WINNER FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.name = 'Ligue 2' ORDER BY T1.FTAG + T1.FTHG DESC LIMIT 1"}],"db_id":"european_football_1"}122{"prompt":"What is the percentage of the units of item no.5 sold among all units of items sold in store no.3 on the day with the highest max temperature 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        What is the percentage of the units of item no.5 sold among all units of items sold in store no.3 on the day with the highest max temperature in 2012?\n        \n        ### Context:\n        #### Query Context:\n        item no. 5 refers to item_nbr = 5; store no.3 refers to store_nbr = 3; highest max temperature refers to Max(tmax); in 2012 refers to SUBSTR(date, 1, 4) = '2012';\u00a0 Percentage = Divide (Sum(units where item_nbr = 5), Sum(units)) * 100\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(CASE WHEN T1.item_nbr = 5 THEN units * 1 ELSE 0 END) AS REAL) * 100 \/ SUM(units) 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 T1.store_nbr = 3 AND T1.`date` LIKE '%2012%' AND T3.tmax = ( SELECT MAX(T3.tmax) 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 T1.store_nbr = 3 AND T1.`date` LIKE '%2012%' )"}],"db_id":"sales_in_weather"}123{"prompt":"Which street has the most restaurants?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 street has the most restaurants?\n        \n        ### Context:\n        #### Query Context:\n        street refers to street_name; the most restaurants refers to max(count(street_name))\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 street_name FROM location GROUP BY street_name ORDER BY COUNT(street_name) DESC LIMIT 1"}],"db_id":"restaurant"}124{"prompt":"Who is the vendor of the resources needed by the project that had the highest cost of optional tip?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 vendor of the resources needed by the project that had the highest cost of optional tip?\n        \n        ### Context:\n        #### Query Context:\n        Highest cost of the optional tip refers to Max(total_price_including_optional_support - total_price_excluding_optional_support);\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.vendor_name FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid ORDER BY T2.total_price_including_optional_support - T2.total_price_including_optional_support DESC LIMIT 1"}],"db_id":"donor"}125{"prompt":"How many territories are there in the Eastern 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 territories are there in the Eastern region?\n        \n        ### Context:\n        #### Query Context:\n        \"Eastern\" is the RegionDescription\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.RegionID) FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID WHERE T2.RegionDescription = 'Eastern'"}],"db_id":"retail_world"}126{"prompt":"Which city has the most businesses whose attribute is full_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        Which city has the most businesses whose attribute is full_bar?\n        \n        ### Context:\n        #### Query Context:\n        most business refers to MAX(business_id); full_bar refers to attribute_value = 'full_bar';\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 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[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 Business_Attributes AS T2 ON T1.business_id = T2.business_id WHERE T2.attribute_value LIKE 'full_bar' GROUP BY T1.city"}],"db_id":"public_review_platform"}127{"prompt":"Who are the co-authors for Jei Keon Chae and what is the title of paper written by 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        Who are the co-authors for Jei Keon Chae and what is the title of paper written by them?\n        \n        ### Context:\n        #### Query Context:\n        'Jei Keon Chee' 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 T2.AuthorId, T1.Title FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId WHERE T2.Name = 'Jei Keon Chae'"}],"db_id":"authors"}128{"prompt":"If a married employee has a western name style, what is the probability of him or her working as a store 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        If a married employee has a western name style, what is the probability of him or her working as a store contact?\n        \n        ### Context:\n        #### Query Context:\n        married refers to MaritalStatus = 'M'; western name style refers to NameStyle = 0; store contact refers to PersonType = 'SC'; probability = Divide (Count (BusinessEntityID( PersonType = 'SC' & MaritalStatus = 'M')), Count (BusinessEntityID ( PersonType) & MariatlStatus = 'M'))\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, 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'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, 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'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, 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'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', 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'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 CAST(COUNT(IIF(T1.PersonType = 'SC', T1.PersonType, NULL)) AS REAL) \/ COUNT(T1.PersonType) FROM Person AS T1 INNER JOIN Employee AS T2 WHERE T1.PersonType = 'SC' AND T1.NameStyle = 0 AND T2.MaritalStatus = 'M'"}],"db_id":"works_cycles"}129{"prompt":"What is the language of the book titled Zorro?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 language of the book titled Zorro?\n        \n        ### Context:\n        #### Query Context:\n        \"Zorro\" is the title of the book; langauge refers to language_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.language_name FROM book AS T1 INNER JOIN book_language AS T2 ON T1.language_id = T2.language_id WHERE T1.title = 'Zorro'"}],"db_id":"books"}130{"prompt":"In the players, how many were out by hit wicket?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 players, how many were out by hit wicket?\n        \n        ### Context:\n        #### Query Context:\n        out by hit wicket refers to Out_Name = 'hit wicket'\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_Out FROM Wicket_Taken AS T1 INNER JOIN Out_Type AS T2 ON T1.Kind_Out = T2.Out_Id WHERE Out_Name = 'hit wicket'"}],"db_id":"soccer_2016"}131{"prompt":"What is the average weight of players who have height greater than 72 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        What is the average weight of players who have height greater than 72 inches.\n        \n        ### Context:\n        #### Query Context:\n        average weight refers to AVG(weight); height greater 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', 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[290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT AVG(weight) FROM Master WHERE height > 72"}],"db_id":"hockey"}132{"prompt":"Please list the directions in which the trains with 4 short cars run.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 directions in which the trains with 4 short cars run.\n        \n        ### Context:\n        #### Query Context:\n        short refers to len = 'short'; 4 cars run 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 T2.direction FROM cars AS T1 INNER JOIN trains AS T2 ON T1.train_id = T2.id WHERE T1.len = 'short' AND T1.position = 4"}],"db_id":"trains"}133{"prompt":"How many quantities of Advantus plastic paper clips were ordered 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 quantities of Advantus plastic paper clips were ordered overall?\n        \n        ### Context:\n        #### Query Context:\n        Advantus plastic paper clips 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 SUM(T1.Quantity) FROM central_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T2.`Product Name` = 'Advantus Plastic Paper Clips'"}],"db_id":"superstore"}134{"prompt":"How many Yelp businesses are opened 24 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        How many Yelp businesses are opened 24 hours?\n        \n        ### Context:\n        #### Query Context:\n        open 24 hours refers to attribute_name = 'Open 24 Hours' 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 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 LIKE 'Open 24 Hours' AND T2.attribute_value LIKE 'TRUE'"}],"db_id":"public_review_platform"}135{"prompt":"How many clients who live in New York City have the complaint outcome as \"AGENT\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 New York City have the complaint outcome as \"AGENT\"?\n        \n        ### Context:\n        #### Query Context:\n        New York City refers to city = 'New York City'\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(T2.`rand client`) FROM client AS T1 INNER JOIN callcenterlogs AS T2 ON T1.client_id = T2.`rand client` WHERE T1.city = 'New York City' AND T2.outcome = 'AGENT'"}],"db_id":"retail_complains"}136{"prompt":"Please list the countries that share the shortest border.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 countries that share the shortest border.\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 borders AS T2 ON T1.Code = T2.Country1 ORDER BY T2.Length ASC LIMIT 1"}],"db_id":"mondial_geo"}137{"prompt":"Who are the actors that act in the ACADEMY DINOSAUR 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        Who are the actors that act in the ACADEMY DINOSAUR film?\n        \n        ### Context:\n        #### Query Context:\n        Who are the actors refers to full name; full name refers to first_name, last_name; 'ACADEMY DINOSAUR' is a title of a film\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.first_name, T1.last_name 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 T3.title = 'ACADEMY DINOSAUR'"}],"db_id":"movie_3"}138{"prompt":"Please name any three products that have been discontinued in the meat or poultry 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        Please name any three products that have been discontinued in the meat or poultry category.\n        \n        ### Context:\n        #### Query Context:\n        three products that have been discontinued refer to ProductName LIMIT 3 where Discontinued = 1; CategoryName = 'Meat\/Poultry';\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.ProductName FROM Categories AS T1 INNER JOIN Products AS T2 ON T1.CategoryID = T2.CategoryID WHERE T2.Discontinued = 1 AND T1.CategoryName = 'Meat\/Poultry' LIMIT 3"}],"db_id":"retail_world"}139{"prompt":"Name all countries in which have border with Bulgaria.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 countries in which have border with Bulgaria.\n        \n        ### Context:\n        #### Query Context:\n        Bulgaria is a country name\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 country AS T1 INNER JOIN borders AS T2 ON T1.Code = T2.Country1 INNER JOIN country AS T3 ON T3.Code = T2.Country2 WHERE T1.Name = 'Bulgaria'"}],"db_id":"mondial_geo"}140{"prompt":"Among the products that are no longer in continuous production, how many of them have their supplier 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        Among the products that are no longer in continuous production, how many of them have their supplier in the USA?\n        \n        ### Context:\n        #### Query Context:\n        no longer continuous refers to Discontinued = 1; USA refers to Country = 'USA';\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 T2.Country = 'USA' AND T1.Discontinued = 1"}],"db_id":"retail_world"}141{"prompt":"Show the id of game platform which makes the most sales in Japan.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 id of game platform which makes the most sales in Japan.\n        \n        ### Context:\n        #### Query Context:\n        id of game platform refers to game_platform_id; the most sales refers to max(num_sales); in Japan refers to region_name = 'Japan'\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_platform_id FROM ( SELECT T2.game_platform_id, MAX(T2.num_sales) FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id WHERE T1.region_name = 'Japan' ) t"}],"db_id":"video_games"}142{"prompt":"Other than \"stage directions\", what is the name of the character that appeared 5 times in \"the sea-coast\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Other than \"stage directions\", what is the name of the character that appeared 5 times in \"the sea-coast\"?\n        \n        ### Context:\n        #### Query Context:\n        Other than \"stage directions\" refers to CharName ! = '(stage directions)'; name of the character refers to CharName; appeared 5 times in \"the sea-coast\" refers to chapters.Description = 'The sea-coast.' and count(character_id) = 5\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 T.CharName FROM ( SELECT T3.CharName, COUNT(T3.id) AS num FROM paragraphs AS T1 INNER JOIN chapters AS T2 ON T1.chapter_id = T2.id INNER JOIN characters AS T3 ON T1.character_id = T3.id WHERE T2.Description = 'The sea-coast.' AND T3.CharName != '(stage directions)' AND T1.chapter_id = 18709 GROUP BY T3.id, T3.CharName ) AS T WHERE T.num = 5"}],"db_id":"shakespeare"}143{"prompt":"There was only one tip that user No. 69722 gave to the Yelp business, what was the ratings of that 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        There was only one tip that user No. 69722 gave to the Yelp business, what was the ratings of that business?\n        \n        ### Context:\n        #### Query Context:\n        short tip refers to tip_lenghth = 'short'; user No. refers to user_id; ratings refers to stars; stars = 5 means great experience; stars = 4 means good experience; stars = 3 means average experience; stars = 2 means bad experience; stars = 1 means terrible experience;\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.stars FROM Tips AS T1 INNER JOIN Business AS T2 ON T1.business_id = T2.business_id WHERE T1.user_id = 69722"}],"db_id":"public_review_platform"}144{"prompt":"How many free gifts have customer with id 11782 received?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 free gifts have customer with id 11782 received?\n        \n        ### Context:\n        #### Query Context:\n        free gifts refers to Price = 0;\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 Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T2.CustomerID = 11782 AND T1.Price = 0"}],"db_id":"sales"}145{"prompt":"Among the flights on 2018\/8\/1, how many of them were scheduled to depart from John F. Kennedy International in 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        Among the flights on 2018\/8\/1, how many of them were scheduled to depart from John F. Kennedy International in New York?\n        \n        ### Context:\n        #### Query Context:\n        on 2018\/8\/1 refers to FL_DATE = '2018\/8\/1'; depart from refers to ORIGIN; John F. Kennedy International in New York refers to Description = 'New York, NY: John F. Kennedy 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 COUNT(T1.Code) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T2.FL_DATE = '2018\/8\/1' AND T1.Description = 'New York, NY: John F. Kennedy International'"}],"db_id":"airline"}146{"prompt":"What are the opening and closing time of business id 1 for day 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 are the opening and closing time of business id 1 for day id 2?\n        \n        ### Context:\n        #### Query Context:\n        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 opening_time, closing_time FROM Business_Hours WHERE business_id = 1 AND day_id = 2"}],"db_id":"public_review_platform"}147{"prompt":"Which teams have winning rate less than 50%?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 teams have winning rate less than 50%?\n        \n        ### Context:\n        #### Query Context:\n        team with winning rate less than 50% refers to Divide (won, Sum(won, lost)) < 0.5\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 WHERE CAST(won AS REAL) * 100 \/ (won + lost) < 50"}],"db_id":"professional_basketball"}148{"prompt":"List the title, price and publication date for all sales with 'ON invoice' payment 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        List the title, price and publication date for all sales with 'ON invoice' payment terms.\n        \n        ### Context:\n        #### Query Context:\n        publication date refers to pubdate; payment terms refers to payterms; payterms = 'ON invoice'\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.title, T2.price, T2.pubdate FROM sales AS T1 INNER JOIN titles AS T2 ON T1.title_id = T2.title_id WHERE T1.payterms = 'ON invoice'"}],"db_id":"book_publishing_company"}149{"prompt":"For what grade was the project \"Too Close for Comfort\" 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        For what grade was the project \"Too Close for Comfort\" for?\n        \n        ### Context:\n        #### Query Context:\n        project \"Too Close for Comfort\" refers to title = 'Too Close for Comfort'; grade refers to grade_level\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.grade_level FROM projects AS T1 INNER JOIN essays AS T2 ON T1.projectid = T2.projectid WHERE T2.title LIKE 'Too Close for Comfort'"}],"db_id":"donor"}150{"prompt":"List down the menu page IDs for menu sponsored by Occidental & Oriental?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 menu page IDs for menu sponsored by Occidental & Oriental?\n        \n        ### Context:\n        #### Query Context:\n        menu page IDs refers to MenuPage.id; sponsored by Occidental & Oriental refers to sponsor = 'Occidental & Oriental';\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.id FROM MenuPage AS T1 INNER JOIN Menu AS T2 ON T2.id = T1.menu_id WHERE T2.sponsor = 'Occidental & Oriental'"}],"db_id":"menu"}151{"prompt":"How many matches did Team 10 play 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 matches did Team 10 play in 2012?\n        \n        ### Context:\n        #### Query Context:\n        Team 10 refers to Team_1 = 10 OR Team_2 = 10; in 2012 refers to SUBSTR(Match_Date, 1, 4) = '2012'\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 Team_1 = 10 OR Team_2 = 10 THEN 1 ELSE 0 END) FROM `Match` WHERE SUBSTR(Match_Date, 1, 4) = '2012'"}],"db_id":"soccer_2016"}152{"prompt":"Name the university and country which had the highest number of international students 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 university and country which had the highest number of international students in 2015.\n        \n        ### Context:\n        #### Query Context:\n        highest number of international students refers to MAX(DIVIDE(MULTIPLY(num_students, pct_international_students), 100)); in 2015 refers to year = 2015; 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 T1.university_name, T3.country_name FROM university AS T1 INNER JOIN university_year AS T2 ON T1.id = T2.university_id INNER JOIN country AS T3 ON T3.id = T1.country_id WHERE T2.year = 2015 ORDER BY T2.num_students DESC LIMIT 1"}],"db_id":"university"}153{"prompt":"Who is the youngest player and which city 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 youngest player and which city did he\/she come from?\n        \n        ### Context:\n        #### Query Context:\n        youngest player refers to MIN(DOB); city refers to City_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 T3.City_Name FROM Player AS T1 INNER JOIN Country AS T2 ON T1.Country_Name = T2.Country_Id INNER JOIN City AS T3 ON T2.Country_Id = T3.Country_Id ORDER BY T1.DOB LIMIT 1"}],"db_id":"soccer_2016"}154{"prompt":"Had Bitcoin's price increased or decreased on 2013\/5\/5 compared with the price 7 days before?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Had Bitcoin's price increased or decreased on 2013\/5\/5 compared with the price 7 days before?\n        \n        ### Context:\n        #### Query Context:\n        price increased refers to percent_change_7d>0; decreased refers percent_change_7d<0; on 2013\/5\/5 refers to date = '2013-05-05'\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 (CASE WHEN T2.percent_change_7d > 0 THEN 'INCREASED' ELSE 'DECREASED' END) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T2.date = '2013-05-05' AND T1.name = 'Bitcoin'"}],"db_id":"coinmarketcap"}155{"prompt":"Is the donor who donated to school \"d4af834b1d3fc8061e1ee1b3f1a77b85\" a teacher?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the donor who donated to school \"d4af834b1d3fc8061e1ee1b3f1a77b85\" a teacher?\n        \n        ### Context:\n        #### Query Context:\n        school \"d4af834b1d3fc8061e1ee1b3f1a77b85\" refers to schoolid = 'd4af834b1d3fc8061e1ee1b3f1a77b85'; 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 T2.is_teacher_acct FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.schoolid = 'd4af834b1d3fc8061e1ee1b3f1a77b85'"}],"db_id":"donor"}156{"prompt":"Count the number of books written by Orson Scott Card.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 books written by Orson Scott Card.\n        \n        ### Context:\n        #### Query Context:\n        \"Orson Scott Card\" 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 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 = 'Orson Scott Card'"}],"db_id":"books"}157{"prompt":"State the number of public magnet schools in New York Manhattan.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 public magnet schools in New York Manhattan.\n        \n        ### Context:\n        #### Query Context:\n        public magnet school refers to school_magnet = 't'; in New York Manhattan refers to school_country = 'New York(Manhattan)';\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(schoolid) FROM projects WHERE school_county = 'New York (Manhattan)' AND school_magnet = 't'"}],"db_id":"donor"}158{"prompt":"How many crews were born 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        How many crews were born in the USA?\n        \n        ### Context:\n        #### Query Context:\n        born in the USA refers to birth_country = 'USA'\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 birth_country = 'USA';"}],"db_id":"simpson_episodes"}159{"prompt":"How many stores belong to the station with the highest recorded heat 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        How many stores belong to the station with the highest recorded heat of all time?\n        \n        ### Context:\n        #### Query Context:\n        highest recorded heat refers to Max(heat); station refers to station_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 COUNT(T2.store_nbr) FROM ( SELECT station_nbr FROM weather ORDER BY heat DESC LIMIT 1 ) AS T1 INNER JOIN relation AS T2 ON T1.station_nbr = T2.station_nbr"}],"db_id":"sales_in_weather"}160{"prompt":"Among the users who gave a rating of 5, 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 users who gave a rating of 5, how many of them are male?\n        \n        ### Context:\n        #### Query Context:\n        Male users mean that u_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 COUNT(T1.userid) FROM u2base AS T1 INNER JOIN users AS T2 ON T1.userid = T2.userid WHERE T1.rating = 5 AND T2.u_gender = 'M'"}],"db_id":"movielens"}161{"prompt":"What is the language of the pair of words numbered 1 and 616?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 language of the pair of words numbered 1 and 616?\n        \n        ### Context:\n        #### Query Context:\n        Pair is a relationship of two words: w1st and w2nd, where w1st is word id of the first word and w2nd is a word id of the second word; w1st = 1; w2nd = 616;\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.lang FROM biwords AS T1 INNER JOIN langs AS T2 ON T1.lid = T2.lid WHERE T1.w1st = 1 AND T1.w2nd = 616"}],"db_id":"language_corpus"}162{"prompt":"How many times is the sodium content in Lasagne-Spinach Spirals to Beef and Spinach Pita Pockets?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 sodium content in Lasagne-Spinach Spirals to Beef and Spinach Pita Pockets?\n        \n        ### Context:\n        #### Query Context:\n        sodium is a name of an ingredient; calculation = DIVIDE(SUM(title = 'Lasagne-Spinach Spirals' THEN sodium), SUM(title = 'Beef and Spinach Pita Pockets' THEN sodium))\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 T1.title = 'Lasagne-Spinach Spirals' THEN T2.sodium ELSE 0 END) AS REAL) * 100 \/ SUM(CASE WHEN T1.title = 'Beef and Spinach Pita Pockets' THEN T2.sodium ELSE 0 END) FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id"}],"db_id":"cookbook"}163{"prompt":"List down the enrolled schools and duration of student214.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 enrolled schools and duration of student214.\n        \n        ### Context:\n        #### Query Context:\n        enrolled schools refers to school; duration refers to month; student214 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 school, month FROM enrolled WHERE name = 'student214'"}],"db_id":"student_loan"}164{"prompt":"Which countries have notes on the indicator BX.KLT.DINV.CD.WD?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 countries have notes on the indicator BX.KLT.DINV.CD.WD?\n        \n        ### Context:\n        #### Query Context:\n        indicator BX.KLT.DINV.CD.WD refers to Seriescode = 'BX.KLT.DINV.CD.WD'\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.ShortName FROM Country AS T1 INNER JOIN CountryNotes AS T2 ON T1.CountryCode = T2.Countrycode INNER JOIN Series AS T3 ON T2.Seriescode = T3.SeriesCode WHERE T3.Seriescode = 'BX.KLT.DINV.CD.WD'"}],"db_id":"world_development_indicators"}165{"prompt":"How many publishers in Japan released a game on X360 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 publishers in Japan released a game on X360 in 2011?\n        \n        ### Context:\n        #### Query Context:\n        in Japan refers to region_name = 'Japan'; on X360 refers to platform_name = 'X360'; in 2011 refers to release_year = 2011\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(T3.game_publisher_id) FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id INNER JOIN game_platform AS T3 ON T2.game_platform_id = T3.id INNER JOIN platform AS T4 ON T3.platform_id = T4.id WHERE T4.platform_name = 'X360' AND T3.release_year = 2011 AND T1.region_name = 'Japan'"}],"db_id":"video_games"}166{"prompt":"Name the object element that is described as being scattered on image no. 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        Name the object element that is described as being scattered on image no. 10.\n        \n        ### Context:\n        #### Query Context:\n        Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10\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.OBJ_CLASS FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID INNER JOIN IMG_OBJ_ATT AS T3 ON T1.IMG_ID = T3.IMG_ID INNER JOIN ATT_CLASSES AS T4 ON T3.ATT_CLASS_ID = T4.ATT_CLASS_ID WHERE T4.ATT_CLASS = 'scattered' AND T1.IMG_ID = 10 GROUP BY T2.OBJ_CLASS"}],"db_id":"image_and_language"}167{"prompt":"Who is the customer that is active and lives at 1795 Santiago de Compostela Way, Texas?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 customer that is active and lives at 1795 Santiago de Compostela Way, Texas?\n        \n        ### Context:\n        #### Query Context:\n        active refers to active = 1; '1795 Santiago de Compostela Way' is an address; Texas is a district; who 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 T1.first_name, T1.last_name FROM customer AS T1 INNER JOIN address AS T2 ON T1.address_id = T2.address_id WHERE T2.address = '1795 Santiago de Compostela Way' AND T1.active = 1"}],"db_id":"movie_3"}168{"prompt":"What is the title of the animated films that have the shortest length?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 animated films that have the shortest length?\n        \n        ### Context:\n        #### Query Context:\n        animated film means animation; animation is a name of a category\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 ORDER BY T1.length LIMIT 1"}],"db_id":"movie_3"}169{"prompt":"How many users have joined Yelp since the year 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 users have joined Yelp since the year 2012?\n        \n        ### Context:\n        #### Query Context:\n        since year 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 COUNT(user_id) FROM Users WHERE user_yelping_since_year = 2012"}],"db_id":"public_review_platform"}170{"prompt":"How many of the countries do not have special notes? List the long 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        How many of the countries do not have special notes? List the long name.\n        \n        ### Context:\n        #### Query Context:\n        do not have special notes implies SpecialNotes are NULL;\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(LongName) FROM Country WHERE SpecialNotes = '' UNION SELECT longname FROM country WHERE specialnotes = ''"}],"db_id":"world_development_indicators"}171{"prompt":"How many flights of Alaska Airlines were delayed on 2018\/8\/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 flights of Alaska Airlines were delayed on 2018\/8\/2?\n        \n        ### Context:\n        #### Query Context:\n        Alaska Airlines refers to Description = 'Alaska Airlines Inc.: AS'; delayed refers to DEP_DELAY > 0; on 2018\/8\/2 refers to FL_DATE = '2018\/8\/2';\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 COUNT(*) FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.FL_DATE = '2018\/8\/2' AND T2.Description = 'Alaska Airlines Inc.: AS' AND T1.DEP_DELAY > 0"}],"db_id":"airline"}172{"prompt":"Give the full address of the office of the highest paid 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        Give the full address of the office of the highest paid manager.\n        \n        ### Context:\n        #### Query Context:\n        the highest paid refers to MAX(salary); manager is a position title\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.address FROM employee AS T1  INNER JOIN location AS T2  ON T1.locationID = T2.locationID INNER JOIN position AS T3  ON T3.positionID = T1.positionID WHERE T3.positiontitle = 'Manager' ORDER BY T1.salary DESC LIMIT 1"}],"db_id":"human_resources"}173{"prompt":"How many distinct movies in English stars a male actor who acts the best?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 distinct movies in English stars a male actor who acts the best?\n        \n        ### Context:\n        #### Query Context:\n        Male actors mean that a_gender = 'M'; isEnglish = 'T' means movies in English\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(DISTINCT T1.actorid) FROM actors AS T1 INNER JOIN movies2actors AS T2 ON T1.actorid = T2.actorid INNER JOIN movies AS T3 ON T2.movieid = T3.movieid WHERE T3.isEnglish = 'T' AND T1.a_gender = 'M' AND T1.a_quality = 5"}],"db_id":"movielens"}174{"prompt":"What are the keywords for episode 426 of the 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        What are the keywords for episode 426 of the series?\n        \n        ### Context:\n        #### Query Context:\n        episode 426 refers to number_in_series = 426\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.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.number_in_series = 426;"}],"db_id":"simpson_episodes"}175{"prompt":"What is the difference between average glucose reading for patients in the 20s and 50s?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 between average glucose reading for patients in the 20s and 50s?\n        \n        ### Context:\n        #### Query Context:\n        sum(case when t2.DATE-t1.birthdate between 20 and 29 then t2.VALUE else 0 end)\/count(case when t2.DATE-t1.birthdate between 20 and 29 then t2.PATIENT else null end)-sum(case when t2.DATE-t1.birthdate between 50 and 59 then t2.VALUE else 0 end)\/count(case when t2.DATE-t1.birthdate between 50 and 59 then t2.PATIENT else null end)\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 SUM(CASE WHEN ROUND((strftime('%J', T2.DATE) - strftime('%J', T1.birthdate)) \/ 365) BETWEEN 20 AND 30 THEN T2.VALUE ELSE 0 END) \/ COUNT(CASE WHEN ROUND((strftime('%J', T2.DATE) - strftime('%J', T1.birthdate)) \/ 365) BETWEEN 20 AND 30 THEN T2.PATIENT END) - SUM(CASE WHEN ROUND((strftime('%J', T2.DATE) - strftime('%J', T1.birthdate)) \/ 365) BETWEEN 50 AND 60 THEN T2.VALUE ELSE 0 END) \/ COUNT(CASE WHEN ROUND((strftime('%J', T2.DATE) - strftime('%J', T1.birthdate)) \/ 365) BETWEEN 50 AND 60 THEN T2.PATIENT END) AS difference FROM patients AS T1 INNER JOIN observations AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'Glucose'"}],"db_id":"synthea"}176{"prompt":"How many poems did Shakespeare 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 poems did Shakespeare write?\n        \n        ### Context:\n        #### Query Context:\n        poems refers to GenreType = 'Poem'\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 works WHERE GenreType = 'Poem'"}],"db_id":"shakespeare"}177{"prompt":"What is the party of the oldest legislator?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 party of the oldest legislator?\n        \n        ### Context:\n        #### Query Context:\n        oldest legislator refers to MIN(birthday_bio);\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.party FROM `historical-terms` AS T1 INNER JOIN historical AS T2 ON T2.bioguide_id = T1.bioguide ORDER BY T2.birthday_bio LIMIT 1"}],"db_id":"legislator"}178{"prompt":"What is the name of the client who has the largest quantity of rented material without returning 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        What is the name of the client who has the largest quantity of rented material without returning it?\n        \n        ### Context:\n        #### Query Context:\n        name refers to first_name, last_name; without returning a rented material refers to return_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 T.first_name FROM ( SELECT T2.first_name, COUNT(T1.rental_date) AS num FROM rental AS T1 INNER JOIN customer AS T2 ON T1.customer_id = T2.customer_id GROUP BY T2.first_name ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"movie_3"}179{"prompt":"Give the address location of Heather Morris.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 address location of Heather Morris.\n        \n        ### Context:\n        #### Query Context:\n        address location refers to address\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 = 'HEATHER' AND T2.last_name = 'MORRIS'"}],"db_id":"movie_3"}180{"prompt":"For the business which got the most number of violations, how many inspections did 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        For the business which got the most number of violations, how many inspections did it have?\n        \n        ### Context:\n        #### Query Context:\n        \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(T2.business_id) FROM violations AS T1 INNER JOIN inspections AS T2 ON T1.business_id = T2.business_id GROUP BY T1.business_id ORDER BY COUNT(T1.business_id) DESC LIMIT 1"}],"db_id":"food_inspection"}181{"prompt":"List the university ID of the university that scored 100 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 university ID of the university that scored 100 in 2011.\n        \n        ### Context:\n        #### Query Context:\n        in 2011 refers to year = 2011; 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 university_id FROM university_ranking_year WHERE score = 100 AND year = 2011"}],"db_id":"university"}182{"prompt":"Which customer come from a place with more inhabitants, customer no.0 or customer 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        Which customer come from a place with more inhabitants, customer no.0 or customer no.1?\n        \n        ### Context:\n        #### Query Context:\n        place with more inhabitants refers to GEOID where ID = 0 OR ID = 1 and MAX(NHABITANTS_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.ID FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.ID = 0 OR T1.ID = 1 ORDER BY INHABITANTS_K DESC LIMIT 1"}],"db_id":"software_company"}183{"prompt":"List by name all the games released in the year 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        List by name all the games released in the year 2010.\n        \n        ### Context:\n        #### Query Context:\n        name of the games refers to game_name; released in the year 2010 refers to release_year = 2010;\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 T1.game_name FROM game AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.game_id INNER JOIN game_platform AS T3 ON T2.id = T3.game_publisher_id WHERE T3.release_year = '2010'"}],"db_id":"video_games"}184{"prompt":"List all the businesses that closed at 8PM.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 businesses that closed at 8PM.\n        \n        ### Context:\n        #### Query Context:\n        closed at 8PM refers to closing_time = '8PM';\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 business_id FROM Business_Hours WHERE closing_time = '8PM'"}],"db_id":"public_review_platform"}185{"prompt":"How many episodes were aired between October and November 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        How many episodes were aired between October and November 2008?\n        \n        ### Context:\n        #### Query Context:\n        between October and November 2008 refers to air_date BETWEEN '2008-10-01' and '2008-11-30'\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(episode_id) FROM Episode WHERE air_date LIKE '2008-10%' OR air_date LIKE '2008-11%';"}],"db_id":"simpson_episodes"}186{"prompt":"Which product ID do not have any work order 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 product ID do not have any work order ID?\n        \n        ### Context:\n        #### Query Context:\n        Do not have any work order ID means WorkOrderID 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', '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, 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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 ProductID FROM Product WHERE ProductID NOT IN ( SELECT T1.ProductID FROM Product AS T1 INNER JOIN WorkOrder AS T2 ON T1.ProductID = T2.ProductID )"}],"db_id":"works_cycles"}187{"prompt":"For the weather station which recorded the highest temperature above the 30-year normal, how many stores does 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        For the weather station which recorded the highest temperature above the 30-year normal, how many stores does it have?\n        \n        ### Context:\n        #### Query Context:\n        highest temperature above the 30-year normal refers to Max(depart)\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 store_nbr FROM relation WHERE station_nbr = ( SELECT station_nbr FROM weather ORDER BY depart DESC LIMIT 1 )"}],"db_id":"sales_in_weather"}188{"prompt":"What is the title of corpus with most 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        What is the title of corpus with most words?\n        \n        ### Context:\n        #### Query Context:\n        most words refers to max(words)\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 title FROM pages WHERE words = ( SELECT MAX(words) FROM pages )"}],"db_id":"language_corpus"}189{"prompt":"What is the average supply cost for the part \"hot spring dodger 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        What is the average supply cost for the part \"hot spring dodger dim light\"?\n        \n        ### Context:\n        #### Query Context:\n        average supply cost refers to avg(ps_supplycost); part \"hot spring dodger dim light\" refers to p_name = 'hot spring dodger dim light'\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(T1.ps_supplycost) FROM partsupp AS T1 INNER JOIN supplier AS T2 ON T1.ps_suppkey = T2.s_suppkey INNER JOIN part AS T3 ON T1.ps_partkey = T3.p_partkey WHERE T3.p_name = 'hot spring dodger dim light'"}],"db_id":"retails"}190{"prompt":"What is the full name of the customer who rented movies for 7 consecutive 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        What is the full name of the customer who rented movies for 7 consecutive days?\n        \n        ### Context:\n        #### Query Context:\n        rented for 7 consecutive days refers to Subtract(return_date, rental_date) = 7; 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 T1.first_name, T1.last_name FROM customer AS T1 INNER JOIN ( SELECT customer_id, COUNT(*) AS num_days FROM ( SELECT *, date(days, '-' || rn || ' day') AS results FROM ( SELECT customer_id, days, row_number() OVER (PARTITION BY customer_id ORDER BY days) AS rn FROM ( SELECT DISTINCT customer_id, date(rental_date) AS days FROM rental ) ) ) GROUP BY customer_id, results HAVING num_days = 7 ) AS T2 ON T1.customer_id = T2.customer_id"}],"db_id":"movie_3"}191{"prompt":"List all of the credit cards that had expired by 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        List all of the credit cards that had expired by 2007.\n        \n        ### Context:\n        #### Query Context:\n        card that expires in 2007 refers to ExpYear = 2007;\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'], 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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 CardNumber FROM CreditCard WHERE ExpYear < 2007"}],"db_id":"works_cycles"}192{"prompt":"Among all the customers in Brazil, how many of them have an account balance of 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        Among all the customers in Brazil, how many of them have an account balance of less than 1000?\n        \n        ### Context:\n        #### Query Context:\n        customers refer to c_custkey; Brazil is the name of the nation which refers to n_name = 'BRAZIL'; account balance of less than 1000 refers to c_acctbal < 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 COUNT(T1.c_custkey) FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T2.n_name = 'BRAZIL' AND T1.c_acctbal < 1000"}],"db_id":"retails"}193{"prompt":"How many students enlisted in the navy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 navy?\n        \n        ### Context:\n        #### Query Context:\n        navy 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"}194{"prompt":"What is the capital of the country that has the Licancabur Mountain?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 capital of the country that has the Licancabur Mountain?\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 T4.Capital FROM mountain AS T1 INNER JOIN geo_mountain AS T2 ON T1.Name = T2.Mountain INNER JOIN province AS T3 ON T3.Name = T2.Province INNER JOIN country AS T4 ON T4.Province = T3.Name WHERE T1.Name = 'Licancabur'"}],"db_id":"mondial_geo"}195{"prompt":"Among all the customers who have purchased ML Bottom Bracket, identify the percentage of sales by Albert I. Ringer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 customers who have purchased ML Bottom Bracket, identify the percentage of sales by Albert I. Ringer?\n        \n        ### Context:\n        #### Query Context:\n        ML Bottom Bracket' is name of product; percentage = MULTIPLY(DIVIDE(SUM(CustomerID WHERE FirstName = 'Albert' AND MiddleInitial = 'I' AND LastName = 'Ringer'), COUNT(CustomerID)), 1.0);\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 CAST(SUM(IIF(T3.FirstName = 'Albert' AND T3.MiddleInitial = 'I' AND T3.LastName = 'Ringer', 1, 0)) AS REAL) * 100 \/ COUNT(T2.CustomerID) FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID INNER JOIN Employees AS T3 ON T2.SalesPersonID = T3.EmployeeID WHERE T1.Name = 'ML Bottom Bracket'"}],"db_id":"sales"}196{"prompt":"Name all person in the individual retail whose last name is 'Anderson'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 person in the individual retail whose last name is 'Anderson'.\n        \n        ### Context:\n        #### Query Context:\n        person in the individual retail refers to PersonType = 'IN'\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', '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 BusinessEntityID FROM Person WHERE FirstName = 'Emma' AND LastName = 'Harris'"}],"db_id":"works_cycles"}199{"prompt":"How many events were there on 30th April, 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 were there on 30th April, 2016?\n        \n        ### Context:\n        #### Query Context:\n        on 30th April, 2016 refers to `timestamp` LIKE '2016-04-30%';\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(event_id) FROM events WHERE SUBSTR(`timestamp`, 1, 10) = '2016-04-30'"}],"db_id":"talkingdata"}200{"prompt":"Among the active businesses located at Goodyear, AZ, list the category and atrributes of 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 active businesses located at Goodyear, AZ, list the category and atrributes of business with a high review count.\n        \n        ### Context:\n        #### Query Context:\n        active business ID refers to active = 'true'; Goodyear is a city; AZ is a state; 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 T3.category_name, T5.attribute_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 INNER JOIN Business_Attributes AS T4 ON T1.business_id = T4.business_id INNER JOIN Attributes AS T5 ON T4.attribute_id = T5.attribute_id WHERE T1.review_count = 'High' AND T1.city = 'Goodyear' AND T1.state = 'AZ' AND T1.active = 'true'"}],"db_id":"public_review_platform"}201{"prompt":"What is the first name of the driver who transported shipment id 1028?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 first name of the driver who transported shipment id 1028?\n        \n        ### Context:\n        #### Query Context:\n        shipment id 1028 refers to ship_id = 1028\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.first_name, T2.last_name FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id WHERE T1.ship_id = 1028"}],"db_id":"shipping"}202{"prompt":"Customer No.129301 made an order on 1996\/7\/27, what was the delivery time for the first part of that 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        Customer No.129301 made an order on 1996\/7\/27, what was the delivery time for the first part of that order?\n        \n        ### Context:\n        #### Query Context:\n        SUBTRACT(l_receiptdate, l_commitdate) WHERE o_orderdate = '1996-07-27' AND o_custkey = '129301';\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) FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T1.o_custkey = '129301' AND T1.o_orderdate = '1996-07-27'"}],"db_id":"retails"}203{"prompt":"What is the full comment on the method whose solution path is \"bmatzelle_nini\\Source\\Nini.sln\" with a tokenized name of \"alias text add 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        What is the full comment on the method whose solution path is \"bmatzelle_nini\\Source\\Nini.sln\" with a tokenized name of \"alias text add alias\"?\n        \n        ### Context:\n        #### Query Context:\n        \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 T2.FullComment FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T1.Path = 'bmatzelle_niniSourceNini.sln' AND T2.NameTokenized = 'alias text add alias'"}],"db_id":"codebase_comments"}204{"prompt":"What is the average number of inspections done by the top 5 employees with the highest salary? List the names of the said 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        What is the average number of inspections done by the top 5 employees with the highest salary? List the names of the said employees.\n        \n        ### Context:\n        #### Query Context:\n        the highest salary refers to max(salary); sanitarian refers to title = 'Sanitarian'; name refers to first_name, last_name; average number = divide(sum(inspection_id), 5)\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(DISTINCT T2.inspection_id) AS REAL) \/ 5, T1.first_name, T1.last_name FROM employee AS T1 INNER JOIN inspection AS T2 ON T1.employee_id = T2.employee_id WHERE T1.title = 'Sanitarian' ORDER BY T1.salary DESC LIMIT 5"}],"db_id":"food_inspection_2"}205{"prompt":"What award did the episode that aired on 11\/30\/2008 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 award did the episode that aired on 11\/30\/2008 win?\n        \n        ### Context:\n        #### Query Context:\n        aired on 11\/30\/2008 refers to air_date = '11\/30\/2008'; win refers to result = 'Winner'\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.award FROM Award AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T1.result = 'Winner' AND T2.air_date = '2008-11-30';"}],"db_id":"simpson_episodes"}206{"prompt":"How many more \"Chinese\" than \"Filipino\" 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        How many more \"Chinese\" than \"Filipino\" Yelp businesses?\n        \n        ### Context:\n        #### Query Context:\n        Chinese refers to category_name = 'Chinese'; Filipino refers to category_name = 'Filipino'; how many more = SUBTRACT(SUM(category_name = 'Chinese'), SUM(category_name = 'Filipino'));\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 SUM(CASE WHEN T1.category_name LIKE 'Chinese' THEN 1 ELSE 0 END) - SUM(CASE WHEN T1.category_name LIKE 'Filipino' THEN 1 ELSE 0 END) FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id"}],"db_id":"public_review_platform"}207{"prompt":"Which island is city Balikpapan located on? How big is the island?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 island is city Balikpapan located on? How big is the island?\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, T3.Area FROM city AS T1 INNER JOIN locatedOn AS T2 ON T1.Name = T2.City INNER JOIN island AS T3 ON T3.Name = T2.Island WHERE T1.Name = 'Balikpapan'"}],"db_id":"mondial_geo"}208{"prompt":"What is the percentage of users who are in the same behavior category as \"Academic Information\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 users who are in the same behavior category as \"Academic Information\"?\n        \n        ### Context:\n        #### Query Context:\n        percentage = MULTIPLY(DIVIDE(SUM(category = 'Academic Information'), COUNT(app_id)), 1.0); behavior category refers to category; category = 'Academic Information';\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 SUM(IIF(T1.category = 'Academic Information', 1.0, 0)) \/ COUNT(T2.app_id) AS per FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id"}],"db_id":"talkingdata"}209{"prompt":"Among the Independent senators that started their term in 2011 and onwards, what are the official full names of the senators that caucused with the Democrat?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Independent senators that started their term in 2011 and onwards, what are the official full names of the senators that caucused with the Democrat?\n        \n        ### Context:\n        #### Query Context:\n        Independent refers to party = 'Independent'; senators refers to type = 'sen'; started the term in 2011 and onwards refers to strftime('%Y', start) > = '2011'; caucused with the Democrat refers to caucus = 'Democrat';\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 = 'Independent' AND strftime('%Y', T2.start) >= '2011' AND T2.type = 'sen' AND T2.caucus = 'Democrat'"}],"db_id":"legislator"}210{"prompt":"What time does the company's night shift begin? Indicate the answer in regular 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        What time does the company's night shift begin? Indicate the answer in regular form.\n        \n        ### Context:\n        #### Query Context:\n        Night shift refers to Name = 'Night';\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', 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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 StartTime FROM Shift WHERE Name = 'Night'"}],"db_id":"works_cycles"}211{"prompt":"Name the top 5 highest-grossing Disney movies adjusted for inflation. Identify the percentage they contributed to the total of Disney's current gross.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 5 highest-grossing Disney movies adjusted for inflation. Identify the percentage they contributed to the total of Disney's current gross.\n        \n        ### Context:\n        #### Query Context:\n        The top 5 highest-grossing movies adjusted for inflation refer to MAX(inflation_adjusted_gross)LIMIT 5; DIVIDE(SUM(MAX(inflation_adjusted_gross LIMIT 5)), SUM(inflation_adjusted_gross)) as percentage;\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 CAST(REPLACE(trim(inflation_adjusted_gross, '$'), ',', '') AS REAL) > 1236035515 THEN CAST(REPLACE(trim(inflation_adjusted_gross, '$'), ',', '') AS REAL) ELSE 0 END) * 100 \/ SUM(CAST(REPLACE(trim(inflation_adjusted_gross, '$'), ',', '') AS REAL)) FROM movies_total_gross"}],"db_id":"disney"}212{"prompt":"Which country has the most players 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        Which country has the most players in the Hall of Fame?\n        \n        ### Context:\n        #### Query Context:\n        country refers to birthCountry\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.birthCountry FROM Master AS T1 INNER JOIN HOF AS T2 ON T1.hofID = T2.hofID GROUP BY T1.birthCountry ORDER BY COUNT(T1.playerID) DESC LIMIT 1"}],"db_id":"hockey"}213{"prompt":"Which market segment does the customer with the highest amount of debt 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 market segment does the customer with the highest amount of debt belongs to?\n        \n        ### Context:\n        #### Query Context:\n        market segment refers to c_mktsegment; the highest amount of debt refers to max(c_acctbal)\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 c_mktsegment FROM customer WHERE c_acctbal = ( SELECT MIN(c_acctbal) FROM customer )"}],"db_id":"retails"}214{"prompt":"What is the full official name of the current legislator that has served for the most number of 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        What is the full official name of the current legislator that has served for the most number of terms?\n        \n        ### Context:\n        #### Query Context:\n        served for the most number of terms refers to MAX(COUNT(bioguide));\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 GROUP BY T1.official_full_name, T2.bioguide ORDER BY COUNT(T2.bioguide) DESC LIMIT 1"}],"db_id":"legislator"}215{"prompt":"How many transactions were made in Sac State Union using the American Express credit card 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 transactions were made in Sac State Union using the American Express credit card in 2014?\n        \n        ### Context:\n        #### Query Context:\n        Sac State Union refers to LocationName = 'Sac State Union'; American Express credit card refers to CreditCardType = 'American Express'; in 2014 refers to TransactionDate LIKE '2014%';\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' AND T1.CreditCardType = 'American Express' AND T1.TransactionDate BETWEEN '2014-01-01' AND '2014-12-31'"}],"db_id":"beer_factory"}216{"prompt":"At which age did A Lamusi participate in 2012 Summer?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        At which age did A Lamusi participate in 2012 Summer?\n        \n        ### Context:\n        #### Query Context:\n        2012 Summer refers to games_name = '2012 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.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 T1.games_name = '2012 Summer' AND T3.full_name = 'A Lamusi'"}],"db_id":"olympics"}217{"prompt":"Between 2014 to 2016, what is the average inpsection score of the establishment owned by Yiu Tim Chan in 808 Pacific Ave, 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        Between 2014 to 2016, what is the average inpsection score of the establishment owned by Yiu Tim Chan in 808 Pacific Ave, San Francisco?\n        \n        ### Context:\n        #### Query Context:\n        average inspection score refers to avg(score); establishment owned by Yiu Tim Chan refers to business_id where owner_name = 'Yiu Tim Chan'; Between 2014 to 2016 refers to year(date) between 2014 and 2016; address = '808 Pacific Ave'; city = 'San Francisco';\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 AVG(T1.score) FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE STRFTIME('%Y', T1.`date`) BETWEEN '2014' AND '2016' AND T2.owner_name = 'Yiu Tim Chan' AND T2.address = '808 Pacific Ave' AND T2.city = 'San Francisco'"}],"db_id":"food_inspection"}218{"prompt":"What is the customer's address for the shipment with ship ID 1117?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 customer's address for the shipment with ship ID 1117?\n        \n        ### Context:\n        #### Query Context:\n        \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 customer AS T2 ON T1.cust_id = T2.cust_id WHERE T1.ship_id = '1117'"}],"db_id":"shipping"}219{"prompt":"How many times have coaches who were from CHI been awarded as NBA Coach 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        How many times have coaches who were from CHI been awarded as NBA Coach of the Year?\n        \n        ### Context:\n        #### Query Context:\n        CHI refers to tmID = 'CHI'; awarded Coach of the Year refers to award = 'Coach of the Year'; NBA refers to lgID = 'NBA'\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 T2.coachID) FROM coaches AS T1 INNER JOIN awards_coaches AS T2 ON T1.coachID = T2.coachID WHERE T1.tmID = 'CHI' AND T2.award = 'NBA Coach of the Year'"}],"db_id":"professional_basketball"}220{"prompt":"What is the difference between the hottest temperature and the coldest temperature in in Fahrenheit on 8\/29\/2013 for the area where the zip code is 94107?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 between the hottest temperature and the coldest temperature in in Fahrenheit on 8\/29\/2013 for the area where the zip code is 94107?\n        \n        ### Context:\n        #### Query Context:\n        hottest temperature refers to max_temperatutre_f; coldest temperature refers to min_temperature_f; difference = SUBTRACT(max_temperature_f, min_temperature_f); 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 SUM(IIF(zip_code = 94107 AND date = '8\/29\/2013', max_temperature_f - min_temperature_f, 0)) FROM weather"}],"db_id":"bike_share_1"}221{"prompt":"Among all the teams that made the playoffs in the 2007-2008 season, identify the percentage that played over 20 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        Among all the teams that made the playoffs in the 2007-2008 season, identify the percentage that played over 20 games.\n        \n        ### Context:\n        #### Query Context:\n        playoffs refers to GAMETYPE = 'Playoffs'; percentage = MULTIPLY(DIVIDE(SUM(GP > 20), COUNT(ELITEID)), 100); played over 20 games refers to GP > 20; 2007-2008 season refers to SEASON = '2007-2008';\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 GP > 20 THEN TEAM ELSE NULL END) AS REAL) * 100 \/ COUNT(TEAM) FROM SeasonStatus WHERE SEASON = '2007-2008' AND GAMETYPE = 'Playoffs'"}],"db_id":"ice_hockey_draft"}222{"prompt":"How many Japanese cars weigh more than 2000 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        How many Japanese cars weigh more than 2000 lbs?\n        \n        ### Context:\n        #### Query Context:\n        Japanese car refers to country = 'Japan'; weigh more than 2000 lbs refers to weight > 2000\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 INNER JOIN country AS T3 ON T3.origin = T2.country WHERE T3.country = 'Japan' AND T1.weight > 2000"}],"db_id":"cars"}223{"prompt":"Which film has the longest duration?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 film has the longest duration?\n        \n        ### Context:\n        #### Query Context:\n        film refers to the title; the longest duration refers to MAX(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 title FROM film WHERE length = ( SELECT MAX(length) FROM film )"}],"db_id":"movie_3"}224{"prompt":"Please give the personal information of the married employee who has the highest 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        Please give the personal information of the married employee who has the highest pay rate.\n        \n        ### Context:\n        #### Query Context:\n        married refers to MaritalStatus = 'M'; Highest pay rate refers to Max(Rate)\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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[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.Demographics FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN EmployeePayHistory AS T3 ON T2.BusinessEntityID = T3.BusinessEntityID WHERE T1.MaritalStatus = 'M' ORDER BY T3.Rate DESC LIMIT 1"}],"db_id":"works_cycles"}225{"prompt":"How many businesses in AZ state do not open on Thursday?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 AZ state do not open on Thursday?\n        \n        ### Context:\n        #### Query Context:\n        do not open on Thursday refers to day_of_week = 'Thursday' AND label_time_4 = 'None';\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 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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 AS T1 INNER JOIN Checkins AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T2.label_time_4 LIKE 'None' AND T1.state LIKE 'AZ' AND T3.day_of_week LIKE 'Thursday'"}],"db_id":"public_review_platform"}226{"prompt":"What is the student staff ratio at the university with the greatest student staff ratio 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 student staff ratio at the university with the greatest student staff ratio of all time?\n        \n        ### Context:\n        #### Query Context:\n        greatest student staff ratio of all time refers to max(student_staff_ratio)\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 MAX(student_staff_ratio) FROM university_year ORDER BY student_staff_ratio DESC LIMIT 1"}],"db_id":"university"}227{"prompt":"How many Yelp_Business close after 8PM on Mondays?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Yelp_Business close after 8PM on Mondays?\n        \n        ### Context:\n        #### Query Context:\n        close after 8PM refers to closing_time = '9PM' or closing_time = '10PM' closing_time = '11PM' closing_time = '12PM'; Mondays refers to day_of_week = 'Monday'\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(T1.business_id) FROM Business_Hours AS T1 INNER JOIN Days AS T2 ON T1.day_id = T2.day_id WHERE T2.day_of_week LIKE 'Monday' AND T1.closing_time > '8PM'"}],"db_id":"public_review_platform"}228{"prompt":"Which genre contains the greatest number of non-English films?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 genre contains the greatest number of non-English films?\n        \n        ### Context:\n        #### Query Context:\n        isEnglish = 'F' means non-English\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.genre FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid WHERE T1.isEnglish = 'F' GROUP BY T2.genre ORDER BY COUNT(T1.movieid) DESC LIMIT 1"}],"db_id":"movielens"}229{"prompt":"What is the position of the 9th oldest hockey 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        What is the position of the 9th oldest hockey player?\n        \n        ### Context:\n        #### Query Context:\n        position refers to pos; date of birth refers to birthDay + birthMon + birthYear\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'], 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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, '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', 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'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 pos FROM Master WHERE birthYear IS NOT NULL ORDER BY birthYear, birthMon, birthDay LIMIT 8"}],"db_id":"hockey"}230{"prompt":"Which phone number should I call if I want to reach Nancy Davolio's 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 phone number should I call if I want to reach Nancy Davolio's home?\n        \n        ### Context:\n        #### Query Context:\n        phone number of Nancy Davolio's home refers to HomePhone WHERE FirstName = 'Nancy' AND LastName = 'Davolio';\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 HomePhone FROM Employees WHERE LastName = 'Davolio' AND FirstName = 'Nancy'"}],"db_id":"retail_world"}231{"prompt":"What is the birth place of the cast or crew member who won the Best Voice-Over Performance in Online Film & Television Association 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        What is the birth place of the cast or crew member who won the Best Voice-Over Performance in Online Film & Television Association in 2009?\n        \n        ### Context:\n        #### Query Context:\n        won refers to result = 'Winner'; 'Best Voice-Over Performance' is the award; ' Online Film & Television Association' is the organization; 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 T1.birth_place FROM Person AS T1 INNER JOIN Award AS T2 ON T1.name = T2.person WHERE T2.award = 'Best Voice-Over Performance' AND T2.organization = 'Online Film & Television Association' AND T2.year = 2009;"}],"db_id":"simpson_episodes"}232{"prompt":"How many reviews have the user whose ID is 3 posted?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 have the user whose ID is 3 posted?\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 COUNT(review_length) FROM Reviews WHERE user_id = 3"}],"db_id":"public_review_platform"}233{"prompt":"How many players are older than Gurkeerat Singh 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        How many players are older than Gurkeerat Singh player?\n        \n        ### Context:\n        #### Query Context:\n        older than Gurkeerat Singh player refers to DOB ! = 'Gurkeerat Singh' and DOB < '1990-06-29'\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 DOB < '1990-06-29' THEN 1 ELSE 0 END) FROM Player WHERE Player_Name != 'Gurkeerat Singh'"}],"db_id":"soccer_2016"}234{"prompt":"Trains that run in which direction have more rectangle-shaped cars 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        Trains that run in which direction have more rectangle-shaped cars in total?\n        \n        ### Context:\n        #### Query Context:\n        more rectangle-shaped cars refers to MAX(rectCarsNum)\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 T1.direction FROM trains AS T1 INNER JOIN ( SELECT train_id, COUNT(id) AS rectCarsNum FROM cars WHERE shape = 'rectangle' GROUP BY train_id ) AS T2 ON T1.id = T2.train_id ORDER BY T2.rectCarsNum DESC"}],"db_id":"trains"}235{"prompt":"How many users belong to \"game-Art Style\" 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 users belong to \"game-Art Style\" category?\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.app_id) FROM app_labels AS T1 INNER JOIN label_categories AS T2 ON T1.label_id = T2.label_id WHERE T2.category = 'game-Art Style'"}],"db_id":"talkingdata"}236{"prompt":"Among the crimes in all the districts in Chicago, what is the percentage of them happening in the Central 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        Among the crimes in all the districts in Chicago, what is the percentage of them happening in the Central district?\n        \n        ### Context:\n        #### Query Context:\n        the Central district refers to district_name = 'Central'; percentage = divide(count(case_number where district_name = 'Central'), count(case_number)) * 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.district_name = 'Central' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.case_number) FROM Crime AS T1 INNER JOIN District AS T2 ON T1.district_no = T2.district_no"}],"db_id":"chicago_crime"}237{"prompt":"Provide the order comments for at least 5 orders made by customers in the furniture 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        Provide the order comments for at least 5 orders made by customers in the furniture segment.\n        \n        ### Context:\n        #### Query Context:\n        order comments refer to o_comment; c_mktsegment = 'Furniture';\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.o_comment FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey WHERE T2.c_mktsegment = 'Furniture' LIMIT 5"}],"db_id":"retails"}238{"prompt":"Please list the team names which have at least 5 players were born in the same 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        Please list the team names which have at least 5 players were born in the same state.\n        \n        ### Context:\n        #### Query Context:\n        team name refers to teams.name; state that a player is born refers to birthState\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 name FROM teams WHERE tmID IN ( SELECT tmID FROM players_teams AS T1 INNER JOIN players AS T2 ON T1.playerID = T2.playerID WHERE T2.birthState IS NOT NULL GROUP BY T1.tmID, T2.birthState HAVING COUNT(*) > 5 )"}],"db_id":"professional_basketball"}239{"prompt":"Who are the officers involved in cases that are voted as 'No Bill'. List their last name 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        Who are the officers involved in cases that are voted as 'No Bill'. List their last name and gender.\n        \n        ### Context:\n        #### Query Context:\n        voted as 'No Bill' refers to grand_jury_disposition = 'No Bill'\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 T2.last_name, T2.gender FROM incidents AS T1 INNER JOIN officers AS T2 ON T1.case_number = T2.case_number WHERE T1.grand_jury_disposition = 'No Bill'"}],"db_id":"shooting"}240{"prompt":"Which trip id had the longest duration and the start station is in Redwood 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        Which trip id had the longest duration and the start station is in Redwood City?\n        \n        ### Context:\n        #### Query Context:\n        longest duration refers to MAX(duration); start station refers to start_station_name;\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.id FROM trip AS T1 LEFT JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.city = 'Redwood City' AND T1.duration = ( SELECT MAX(T1.duration) FROM trip AS T1 LEFT JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.city = 'Redwood City' )"}],"db_id":"bike_share_1"}241{"prompt":"How many crew members who were born in the USA were nominated for the Outstanding Animated Program (For Programming Less Than One Hour) award 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 crew members who were born in the USA were nominated for the Outstanding Animated Program (For Programming Less Than One Hour) award in 2009?\n        \n        ### Context:\n        #### Query Context:\n        born in USA refers to birth_country = 'USA'; were nominated refers to result = 'Nominee'; 'Outstanding Animated Program (For Programming Less Than One Hour)' is the award; 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 T1.birth_country = 'USA' AND T2.result = 'Nominee' AND T2.award = 'Outstanding Animated Program (For Programming Less Than One Hour)' AND T2.year = 2009;"}],"db_id":"simpson_episodes"}242{"prompt":"List down all of the winning teams' IDs that played in St George's Park.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 winning teams' IDs that played in St George's Park.\n        \n        ### Context:\n        #### Query Context:\n        winning teams' refers to Match_Winner; played in St George's Park refers to Venue_Name like 'St George%'\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.Match_Winner FROM Venue AS T1 INNER JOIN Match AS T2 ON T1.Venue_Id = T2.Venue_Id WHERE T1.Venue_Name LIKE 'St George%'"}],"db_id":"soccer_2016"}243{"prompt":"How many films did a customer named Francis Sikes rent?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 did a customer named Francis Sikes rent?\n        \n        ### Context:\n        #### Query Context:\n        'Francis Sikes' is a full name of a 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 COUNT(T1.customer_id) FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id WHERE T1.first_name = 'FRANCIS' AND T1.last_name = 'SIKES'"}],"db_id":"movie_3"}244{"prompt":"How many businesses are with 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        How many businesses are with high review count?\n        \n        ### Context:\n        #### Query Context:\n        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 LIKE 'High'"}],"db_id":"public_review_platform"}245{"prompt":"How many users liked the movie \"A Way of Life\" to the highest extent?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 liked the movie \"A Way of Life\" to the highest extent?\n        \n        ### Context:\n        #### Query Context:\n        like the movie highest to the extent refers to rating_score = 5; A Way of Life refers to movie_title;\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.user_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_score = 5"}],"db_id":"movie_platform"}246{"prompt":"What is the surface area and GNP of the country where Namibe district 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 surface area and GNP of the country where Namibe district belongs?\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 T2.SurfaceArea, T2.GNP FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T1.District = 'Namibe'"}],"db_id":"world"}247{"prompt":"What are the names of the ingredients that need to be cook in beef broth?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 ingredients that need to be cook in beef broth?\n        \n        ### Context:\n        #### Query Context:\n        'cook in beef broth' refers to a preparation\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 WHERE T2.preparation = 'cooked in beef broth'"}],"db_id":"cookbook"}248{"prompt":"Who made the purchase order with the greatest total due before freight? Indicate her employee ID and calculate for his\/her age when he\/she was hired.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 made the purchase order with the greatest total due before freight? Indicate her employee ID and calculate for his\/her age when he\/she was hired.\n        \n        ### Context:\n        #### Query Context:\n        total due before freight = SUBTRACT(TotalDue, Freight); age at the time an employee was hired = SUBTRACT(HireDate, 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, 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'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.BusinessEntityID, STRFTIME('%Y', T2.HireDate) - STRFTIME('%Y', T2.BirthDate) FROM PurchaseOrderHeader AS T1 INNER JOIN Employee AS T2 ON T1.EmployeeID = T2.BusinessEntityID ORDER BY T1.TotalDue DESC LIMIT 1"}],"db_id":"works_cycles"}249{"prompt":"What is the full address of the office where the employee who is a sales representative for the customer whose business is located in the city of New York 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        What is the full address of the office where the employee who is a sales representative for the customer whose business is located in the city of New York works?\n        \n        ### Context:\n        #### Query Context:\n        full address = addressLine1 + addressLine2; NYC is a shortname of New York City.\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 T2.addressLine1, T2.addressLine2 FROM employees AS T1 INNER JOIN customers AS T2 ON T1.employeeNumber = T2.salesRepEmployeeNumber INNER JOIN offices AS T3 ON T1.officeCode = T3.officeCode WHERE T2.city = 'NYC' AND T1.jobTitle = 'Sales Rep'"}],"db_id":"car_retails"}250{"prompt":"What's the occurrence of the biwords pair whose first word is \"\u00e0bac\" and second word is \"xin\u00e8s\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 occurrence of the biwords pair whose first word is \"\u00e0bac\" and second word is \"xin\u00e8s\"?\n        \n        ### Context:\n        #### Query Context:\n        \u00e0bac refers to word = '\u00e0bac'; xin\u00e8s refers to word = 'xin\u00e8s'; occurrence refers to 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 occurrences FROM biwords WHERE w1st = ( SELECT wid FROM words WHERE word = '\u00e0bac' ) AND w2nd = ( SELECT wid FROM words WHERE word = 'xin\u00e8s' )"}],"db_id":"language_corpus"}251{"prompt":"Provide the ranking system of the ranking criteria named Quality of Education Rank.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 ranking system of the ranking criteria named Quality of Education Rank.\n        \n        ### Context:\n        #### Query Context:\n        criteria named Quality of Education Rank refers to criteria_name = 'Quality of Education Rank'; ranking system refers to system_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 T1.system_name FROM ranking_system AS T1 INNER JOIN ranking_criteria AS T2 ON T1.id = T2.ranking_system_id WHERE T2.criteria_name = 'Quality of Education Rank'"}],"db_id":"university"}252{"prompt":"Which customer has the highest subtotal amount of sales orders whose assigned to the salesperson with the highest bonus?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the highest subtotal amount of sales orders whose assigned to the salesperson with the highest bonus?\n        \n        ### Context:\n        #### Query Context:\n        highest subtotal amount of sales order refers to max(SubTotal);\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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[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.CustomerID FROM SalesOrderHeader AS T1 INNER JOIN SalesPerson AS T2 ON T1.SalesPersonID = T2.BusinessEntityID ORDER BY T1.SubTotal DESC LIMIT 1"}],"db_id":"works_cycles"}253{"prompt":"Which university had the most students in 2011? Show its 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 university had the most students in 2011? Show its name.\n        \n        ### Context:\n        #### Query Context:\n        in 2011 refers to year 2011; the most students refers to MAX(num_students); which 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 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.num_students DESC LIMIT 1"}],"db_id":"university"}254{"prompt":"State the number of addresses in the Nordrhein-Westfalen 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        State the number of addresses in the Nordrhein-Westfalen district.\n        \n        ### Context:\n        #### Query Context:\n        number of address refers to address_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(address_id) FROM address WHERE district = 'Nordrhein-Westfalen'"}],"db_id":"movie_3"}255{"prompt":"List the title of movies in Latin released between 1\/01\/1990 and 12\/31\/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        List the title of movies in Latin released between 1\/01\/1990 and 12\/31\/1995.\n        \n        ### Context:\n        #### Query Context:\n        movies in Latin refers to language_name = 'Latin'; released between 1\/01\/1990 and 12\/31\/1995 refers to release_date BETWEEN '1990-01-01' AND '1995-12-31'\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 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 = 'Latin' AND T1.release_date BETWEEN '1990-01-01' AND '1995-12-31'"}],"db_id":"movies_4"}256{"prompt":"What is the religion with the most occurrrence of the current legislators?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 religion with the most occurrrence of the current legislators?\n        \n        ### Context:\n        #### Query Context:\n        religion with the most occurrrence of the current legislators refers to MAX(count(religion_bio));\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 religion_bio FROM current GROUP BY religion_bio ORDER BY COUNT(religion_bio) DESC LIMIT 1"}],"db_id":"legislator"}257{"prompt":"How many schools in the West New York School District have the highest poverty level?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the West New York School District have the highest poverty level?\n        \n        ### Context:\n        #### Query Context:\n        the highest poverty level refers to poverty_level = 'highest poverty'\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(poverty_level) FROM projects WHERE school_district = 'West New York School District' AND poverty_level = 'highest poverty'"}],"db_id":"donor"}258{"prompt":"Who voiced the villain in \"The Rescuers\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 voiced the villain in \"The Rescuers\"?\n        \n        ### Context:\n        #### Query Context:\n        The Rescuers refers to movie_title = 'The Rescuers'; who voiced refers to voice-actor;\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.`voice-actor` FROM `voice-actors` AS T1 INNER JOIN characters AS T2 ON T2.movie_title = T1.movie WHERE T2.movie_title = 'The Rescuers' AND T1.character = T2.villian"}],"db_id":"disney"}259{"prompt":"Which nation has the lowest proportion of people who speak an African language? Please state the nation's 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        Which nation has the lowest proportion of people who speak an African language? Please state the nation's full name.\n        \n        ### Context:\n        #### Query Context:\n        Nation and country share the same meaning. Proportion refers to percentage\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 ethnicGroup AS T2 ON T1.Code = T2.Country WHERE T2.Name = 'African' ORDER BY T2.Percentage ASC LIMIT 1"}],"db_id":"mondial_geo"}260{"prompt":"Of the players drafted in NBA between 1990 and 2000, who has the most points in all-star? List the player's first name and 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        Of the players drafted in NBA between 1990 and 2000, who has the most points in all-star? List the player's first name and last name.\n        \n        ### Context:\n        #### Query Context:\n        NBA refers to lgID = 'NBA'; between 1990 and 2000 refers to year between 1990 and 2000; the most points refers to max(points)\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 T3.firstname, T3.lastname FROM player_allstar AS T1 INNER JOIN awards_players AS T2 ON T1.playerID = T2.playerID INNER JOIN draft AS T3 ON T1.playerID = T3.playerID WHERE T2.year BETWEEN 1990 AND 2000 ORDER BY T1.points DESC LIMIT 1"}],"db_id":"professional_basketball"}261{"prompt":"For the flight with the tail number 'N702SK', which air carrier does it 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        For the flight with the tail number 'N702SK', which air carrier does it belong to?\n        \n        ### Context:\n        #### Query Context:\n        tail number refers to TAIL_NUM; TAIL_NUM = 'N702SK';\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.TAIL_NUM = 'N702SK' GROUP BY T2.Description"}],"db_id":"airline"}262{"prompt":"Which year did Europe produce the most 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        Which year did Europe produce the most cars?\n        \n        ### Context:\n        #### Query Context:\n        year refers to model_year; Europe refers to country = 'Europe'; the most cars refers to max(model_year)\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.model_year FROM production AS T1 INNER JOIN country AS T2 ON T1.country = T2.origin WHERE T2.country = 'Europe' GROUP BY T1.model_year ORDER BY COUNT(T1.model_year) DESC LIMIT 1"}],"db_id":"cars"}263{"prompt":"How many unemployed disabled students have been absent for 8 months 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 unemployed disabled students have been absent for 8 months from school?\n        \n        ### Context:\n        #### Query Context:\n        absent for 8 months refers to month = 8;\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 longest_absense_from_school AS T1 INNER JOIN unemployed AS T2 ON T1.name = T2.name INNER JOIN disabled AS T3 ON T2.name = T3.name WHERE T1.month = 8"}],"db_id":"student_loan"}264{"prompt":"Where are the ship countries of orders placed by Janet Leverling?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are the ship countries of orders placed by Janet Leverling?\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 DISTINCT T2.ShipCountry FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T1.FirstName = 'Janet' AND T1.LastName = 'Leverling'"}],"db_id":"retail_world"}265{"prompt":"What is the date of the match that has the highest wager on the final result of a 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        What is the date of the match that has the highest wager on the final result of a game?\n        \n        ### Context:\n        #### Query Context:\n        date of the match refers to Match_Date; highest wager refers to max(Win_Margin)\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 Match_Date FROM `Match` ORDER BY Win_Margin DESC LIMIT 1"}],"db_id":"soccer_2016"}266{"prompt":"Calculate the percentage of Black students in all private for profit 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        Calculate the percentage of Black students in all private for profit institutions.\n        \n        ### Context:\n        #### Query Context:\n        Black students refers to race = 'B'; private for profit refers to control = 'Private for-profit'; percentage = MULTIPLY(DIVIDE(SUM(race = 'B'), SUM(grad_cohort)), 100.0);\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 CAST(SUM(CASE WHEN T2.race = 'B' THEN 1 ELSE 0 END) AS REAL) * 100 \/ SUM(T2.grad_cohort) FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T1.unitid = T2.unitid WHERE T2.race = 'B' AND T1.control = 'Private for-profit'"}],"db_id":"college_completion"}267{"prompt":"How many Account Representatives are there in Illinois with satisfying performance?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Account Representatives are there in Illinois with satisfying performance?\n        \n        ### Context:\n        #### Query Context:\n        Account Representatives is a position title; satisfying performance mostly refers togood performance\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 INNER JOIN position AS T3  ON T3.positionID = T1.positionID WHERE T3.positiontitle = 'Account Representative' AND T1.performance = 'Good' AND T2.state = 'IL'"}],"db_id":"human_resources"}268{"prompt":"List out the number of female students who enlisted in the air 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        List out the number of female students who enlisted in the air force.\n        \n        ### Context:\n        #### Query Context:\n        enlisted in the air force refers to organ = 'air_force';\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 = 'air_force' AND name NOT IN ( SELECT name FROM male )"}],"db_id":"student_loan"}269{"prompt":"Which city has the most bad aliases?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 city has the most bad aliases?\n        \n        ### Context:\n        #### Query Context:\n        the most bad aliases refer to MAX(COUNT(bad_alias));\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 FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T1.bad_alias ORDER BY COUNT(T1.zip_code) DESC LIMIT 1"}],"db_id":"address"}270{"prompt":"Indicate the release year of the game with more than 200000 sales in Japan.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 release year of the game with more than 200000 sales in Japan.\n        \n        ### Context:\n        #### Query Context:\n        more than 200000 sales refers to SUM(num_sales) > 2; Japan refers to region_name = 'Japan';\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 T3.release_year FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id INNER JOIN game_platform AS T3 ON T2.game_platform_id = T3.id WHERE T2.num_sales * 100000 > 200000 AND T1.region_name = 'Japan'"}],"db_id":"video_games"}271{"prompt":"Among the crimes happened in the neighborhood called \"Avalon Park\", what is the percentage of crimes that happened inside the house?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 crimes happened in the neighborhood called \"Avalon Park\", what is the percentage of crimes that happened inside the house?\n        \n        ### Context:\n        #### Query Context:\n        \"Avalon Park\" is the neghborhood_name; happened inside the house refers to location_description = 'HOUSE'; percentage = Divide (Count(location_description = 'HOUSE'), Count(location_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(SUM(CASE WHEN T2.location_description = 'HOUSE' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.location_description) AS persent FROM Community_Area AS T1 INNER JOIN Crime AS T2 ON T1.community_area_no = T2.community_area_no INNER JOIN Neighborhood AS T3 ON T2.community_area_no = T3.community_area_no WHERE T3.neighborhood_name = 'Avalon Park'"}],"db_id":"chicago_crime"}272{"prompt":"Who was taking charge of orders from Morristown?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 taking charge of orders from Morristown?\n        \n        ### Context:\n        #### Query Context:\n        Morristown refers to TerritoryDescription = 'Morristown'\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 FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID WHERE T3.TerritoryDescription = 'Morristown'"}],"db_id":"retail_world"}273{"prompt":"Write down the title, summary, and air date of the episode that garnered 72 10-star 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        Write down the title, summary, and air date of the episode that garnered 72 10-star votes.\n        \n        ### Context:\n        #### Query Context:\n        72 10-star votes refers to stars = 10 and votes = 72\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.title, T2.summary, T2.air_date FROM Vote AS T1 INNER JOIN Episode AS T2 ON T2.episode_id = T1.episode_id WHERE T1.stars = 10 AND T1.votes = 72"}],"db_id":"law_episode"}274{"prompt":"What are the names of the universities that got 98 in teaching 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 are the names of the universities that got 98 in teaching in 2011?\n        \n        ### Context:\n        #### Query Context:\n        in 2011 refers to year 2011; that got 98 refers to score = 98; in teaching refers to criteria_name = 'Teaching'; 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 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 = 'Teaching' AND T2.year = 2011 AND T2.score = 98"}],"db_id":"university"}275{"prompt":"What is the percentage of male students in the navy 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        What is the percentage of male students in the navy department?\n        \n        ### Context:\n        #### Query Context:\n        in the navy department refers to organ = 'navy'; percentage refers to DIVIDE(COUNT(name where organ = 'navy'), 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 CAST(COUNT(T2.name) AS REAL) * 100 \/ COUNT(T1.name) FROM enlist AS T1 LEFT JOIN male AS T2 ON T1.`name` = T2.`name` WHERE T1.organ = 'navy'"}],"db_id":"student_loan"}276{"prompt":"How many sales orders were processed by the store located in Chandler in 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        How many sales orders were processed by the store located in Chandler in 2020?\n        \n        ### Context:\n        #### Query Context:\n        \"Chandler\" is the City Name; orders refers to OrderDate; in 2020 refers to Substr(OrderDate, -2) = '20'\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 T2.`City Name` = 'Chandler' AND T1.OrderDate LIKE '%\/%\/20' 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"}277{"prompt":"Which country is Mountain Cerro Chirripo located in? 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 Mountain Cerro Chirripo located in? 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 DISTINCT T1.Name FROM country AS T1 INNER JOIN geo_mountain AS T2 ON T1.Code = T2.Country WHERE T2.Mountain = 'Cerro Chirripo'"}],"db_id":"mondial_geo"}278{"prompt":"Among the students with less than four intelligence, list the full name and phone number of students with a greater than 3 GPA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 with less than four intelligence, list the full name and phone number of students with a greater than 3 GPA.\n        \n        ### Context:\n        #### Query Context:\n        intelligence < 4; full name = f_name, l_name; 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 f_name, l_name, phone_number FROM student WHERE gpa > 3 AND intelligence < 4"}],"db_id":"cs_semester"}279{"prompt":"Who is the customer with an order shipped on March 5, 2013, in the eastern 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        Who is the customer with an order shipped on March 5, 2013, in the eastern region?\n        \n        ### Context:\n        #### Query Context:\n        Who is the customer refers to Customer Name; shipped on March 5, 2013 refers to \"Ship Date\" = '2013-03-05'; eastern region refers to 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 DISTINCT T2.`Customer Name` FROM east_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` WHERE T1.`Ship Date` = '2013-03-05'"}],"db_id":"superstore"}280{"prompt":"Which method has the summary \"Write a command to the log\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 method has the summary \"Write a command to the log\"?\n        \n        ### Context:\n        #### Query Context:\n        \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 Name FROM Method WHERE Summary = 'Write a command to the log'"}],"db_id":"codebase_comments"}281{"prompt":"List the movie titles and character names by Bill Thompson.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 movie titles and character names by Bill Thompson.\n        \n        ### Context:\n        #### Query Context:\n        Bill Thompson refers to voice-actor = 'Bill Thompson';\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 movie, character FROM `voice-actors` WHERE 'voice-actor' = 'Bill Thompson'"}],"db_id":"disney"}282{"prompt":"Among all patients who sought medical attention in 2010 due to contact dermatitis, identify the percentage of 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 all patients who sought medical attention in 2010 due to contact dermatitis, identify the percentage of females.\n        \n        ### Context:\n        #### Query Context:\n        in 2010 refers to substr(encounters.DATE, 1, 4) = '2010'; contact dermatitis refers to encounters.REASONDESCRIPTION = 'Contact dermatitis'; percentage = MULTIPLY(DIVIDE(COUNT(encounters.PATIENT WHERE gender = 'F'), count(encounters.PATIENT) WHERE encounters.REASONDESCRIPTION = 'Contact dermatitis'), 1.0) females refers to gender = 'F';\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' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.PATIENT) FROM encounters AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE strftime('%Y', T1.DATE) = '2010' AND T1.REASONDESCRIPTION = 'Contact dermatitis'"}],"db_id":"synthea"}283{"prompt":"What is the district address associated with the case JB107731?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 district address associated with the case JB107731?\n        \n        ### Context:\n        #### Query Context:\n        case JB107731 refers to case_number = 'JB107731'\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.address FROM District AS T1 INNER JOIN Crime AS T2 ON T2.district_no = T1.district_no WHERE T2.case_number = 'JB107731'"}],"db_id":"chicago_crime"}284{"prompt":"Indicate the name of all the games published for the 'SCD' platform.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 name of all the games published for the 'SCD' platform.\n        \n        ### Context:\n        #### Query Context:\n        name of games refers to game_name;\u00a0 'SCD' platform refers to platform_name = 'SCD';\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 T1.game_name FROM game AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.game_id INNER JOIN game_platform AS T3 ON T2.id = T3.game_publisher_id INNER JOIN platform AS T4 ON T3.platform_id = T4.id WHERE T4.platform_name = 'SCD'"}],"db_id":"video_games"}285{"prompt":"What is the average price of model 70 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        What is the average price of model 70 cars?\n        \n        ### Context:\n        #### Query Context:\n        model 70 refers to model = 70; average price = avg(price) where model = 70\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.model = 70"}],"db_id":"cars"}286{"prompt":"How many goals were scored while the goalkeeper was on the ice in the 1924 WCHL by the goalie with the most goals scored?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 goals were scored while the goalkeeper was on the ice in the 1924 WCHL by the goalie with the most goals scored?\n        \n        ### Context:\n        #### Query Context:\n        WCHL is the league's abbreviated name which refers to lgID = 'WCHL'; most goals scored referst to MAX(PostGA); GA is abbreviation for Post Against which means number of goals recorded while the goalie is on the ice. Include all goals against during regulation and overtime play.\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 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[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 SUM(PostGA) FROM Goalies WHERE lgID = 'WCHL' AND year = '1924' GROUP BY playerID ORDER BY SUM(PostGA) DESC LIMIT 1"}],"db_id":"hockey"}287{"prompt":"How many employees who are living in Australia and have the credit limit under 200000? State their email address and countries where they are working.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 who are living in Australia and have the credit limit under 200000? State their email address and countries where they are working.\n        \n        ### Context:\n        #### Query Context:\n        Australia is a country; creditLimit < 20000;\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 T2.email, T3.country FROM customers AS T1 INNER JOIN employees AS T2 ON T1.salesRepEmployeeNumber = T2.employeeNumber INNER JOIN offices AS T3 ON T2.officeCode = T3.officeCode WHERE T3.country = 'Australia' AND T1.creditLimit < 200000 AND T2.jobTitle = 'Sales Rep'"}],"db_id":"car_retails"}288{"prompt":"List the race of institutions in Alabama with number of students greater than the 90% of average number of students of all 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        List the race of institutions in Alabama with number of students greater than the 90% of average number of students of all institutions?\n        \n        ### Context:\n        #### Query Context:\n        Alabama refers to state = 'Alabama'; number of students greater than the 90% of average = MULTIPLY(AVG(student_count), 90%) < student_count;\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 T2.race FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.student_count > ( SELECT AVG(T1.student_count) * 0.9 FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.state = 'Alabama' ) AND T1.state = 'Alabama'"}],"db_id":"college_completion"}289{"prompt":"How many students have been absent above 2 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        How many students have been absent above 2 months?\n        \n        ### Context:\n        #### Query Context:\n        absent above 2 months refers to month > 2;\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 > 2"}],"db_id":"student_loan"}290{"prompt":"Please list the order number of the customer whose credit card has a limit of 45300.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 order number of the customer whose credit card has a limit of 45300.\n        \n        ### Context:\n        #### Query Context:\n        Credit card does not have a limit refers to creditLimit = 45300;\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.orderNumber FROM orders AS t1 INNER JOIN customers AS t2 ON t1.customerNumber = t2.customerNumber WHERE t2.creditLimit = 45300"}],"db_id":"car_retails"}291{"prompt":"List all the territories where Laura Callahan is in charge.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 territories where Laura Callahan is in charge.\n        \n        ### Context:\n        #### Query Context:\n        territories refers to TerritoryDescription; Laura Callahan is the full name of an employee; 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 T3.TerritoryDescription FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID WHERE T1.FirstName = 'Laura' AND T1.LastName = 'Callahan'"}],"db_id":"retail_world"}292{"prompt":"For Peter Kohnke, show the name of his\/her 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        For Peter Kohnke, show the name of his\/her region.\n        \n        ### Context:\n        #### Query Context:\n        name of his\/her region refers to region_name;\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.region_name 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.full_name = 'Peter Kohnke'"}],"db_id":"olympics"}293{"prompt":"Which company placed the order with the id 10257?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 placed the order with the id 10257?\n        \n        ### Context:\n        #### Query Context:\n        \"10257\" is the OrderID; company 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 T1.CompanyName FROM Customers AS T1 INNER JOIN Orders AS T2 ON T1.CustomerID = T2.CustomerID WHERE T2.OrderID = 10257"}],"db_id":"retail_world"}294{"prompt":"How many bottles of beer have been bought by Jim Breech?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 bottles of beer have been bought by Jim Breech?\n        \n        ### Context:\n        #### Query Context:\n        bottles refers to ContainerType = 'Bottle';\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(T3.ContainerType) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T2.CustomerID = T1.CustomerID INNER JOIN rootbeer AS T3 ON T3.RootBeerID = T2.RootBeerID WHERE T3.ContainerType = 'Bottle' AND T1.First = 'Jim' AND T1.Last = 'Breech'"}],"db_id":"beer_factory"}295{"prompt":"What is the percentage of respositories that receive more than 2,000 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        What is the percentage of respositories that receive more than 2,000 stars?\n        \n        ### Context:\n        #### Query Context:\n        stars>2000; percentage = DIVIDE(MULTIPLY(COUNT(Stars>2000), 100), COUNT(Id));\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 Stars > 2000 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(Stars) FROM Repo"}],"db_id":"codebase_comments"}296{"prompt":"On average, how many community areas are there in a side?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 average, how many community areas are there in a side?\n        \n        ### Context:\n        #### Query Context:\n        average = Divide(Count(ward_no), Count(side))\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.ward_no) AS REAL) \/ COUNT(DISTINCT T3.side) FROM Ward AS T1 INNER JOIN Crime AS T2 ON T2.ward_no = T1.ward_no INNER JOIN Community_Area AS T3 ON T3.community_area_no = T2.community_area_no"}],"db_id":"chicago_crime"}297{"prompt":"Which non capital city has the most people of all?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 non capital city has the most people of all?\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 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 <> T1.Capital ORDER BY T3.Population DESC LIMIT 1"}],"db_id":"mondial_geo"}298{"prompt":"From 1980 to 2000, which artist had the most disco releases?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 1980 to 2000, which artist had the most disco releases?\n        \n        ### Context:\n        #### Query Context:\n        From 1980 to 2000 refers to groupYear between 1980 and 2000; tag = 'disco'; the most releases refer to MAX(COUNT(id));\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.groupYear BETWEEN 1980 AND 2000 AND T2.tag LIKE 'disco' GROUP BY T1.artist ORDER BY COUNT(T2.tag) DESC LIMIT 1"}],"db_id":"music_tracker"}299{"prompt":"How many of the patients born in 1920s had pneumonia?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 patients born in 1920s had pneumonia?\n        \n        ### Context:\n        #### Query Context:\n        patients born in 1920s refer to patient where birthdate like '192%'; pneumonia refers to DESCRIPTION = 'Pneumonia' from conditions;\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 conditions AS T2 ON T1.patient = T2.PATIENT WHERE DESCRIPTION = 'Pneumonia' AND strftime('%Y', T1.birthdate) LIKE '192%'"}],"db_id":"synthea"}300{"prompt":"What is the Twitter ID of Emanuel Cleaver?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Twitter ID of Emanuel Cleaver?\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 T1.twitter_id FROM `social-media` AS T1 INNER JOIN current AS T2 ON T2.bioguide_id = T1.bioguide WHERE T2.first_name = 'Emanuel' AND T2.last_name = 'Cleaver'"}],"db_id":"legislator"}301{"prompt":"Lists the last name of all clients who made a PS-type complaint and were served by TOVA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Lists the last name of all clients who made a PS-type complaint and were served by TOVA.\n        \n        ### Context:\n        #### Query Context:\n        PS-type complaint refers to type = 'PS'; served by refers to server;\n\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 t1.last FROM client AS T1 INNER JOIN callcenterlogs AS T2 ON T1.client_id = T2.`rand client` WHERE T2.type = 'PS' AND T2.server = 'TOVA'"}],"db_id":"retail_complains"}302{"prompt":"Provide the google entity ID of the senators in 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        Provide the google entity ID of the senators in New York.\n        \n        ### Context:\n        #### Query Context:\n        google entity ID refers to google_entity_id_id; senators refers to type = 'sen'; New York refers to state = 'NY';\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.google_entity_id_id FROM historical AS T1 INNER JOIN `historical-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.type = 'sen' AND T2.state = 'NY'"}],"db_id":"legislator"}303{"prompt":"How much was a Bitcoin on 2013\/4\/28?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 was a Bitcoin on 2013\/4\/28?\n        \n        ### Context:\n        #### Query Context:\n        how much 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.price 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"}304{"prompt":"What is the weight in kg of Tony Martensson?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 kg of Tony Martensson?\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 T2.weight_in_kg FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id WHERE T1.PlayerName = 'Tony Martensson'"}],"db_id":"ice_hockey_draft"}305{"prompt":"List down the labels' IDs and categories of the app ID \"5758400314709850000\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 labels' IDs and categories of the app ID \"5758400314709850000\".\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 T1.label_id, T2.category FROM app_labels AS T1 INNER JOIN label_categories AS T2 ON T1.label_id = T2.label_id WHERE T1.app_id = 5758400314709850000"}],"db_id":"talkingdata"}306{"prompt":"How many players, in games played in 1990, achieved 50% or less of oRebounds than dRebounds.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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, in games played in 1990, achieved 50% or less of oRebounds than dRebounds.\n        \n        ### Context:\n        #### Query Context:\n        in 1990 refers to season_id = 1990; 50% or less of oRebounds than dRebounds refers to o_rebounds < = multiply(d_rebounds, 0.5)\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(playerID) FROM players_teams WHERE CAST(oRebounds AS REAL) * 100 \/ dRebounds <= 50 AND year = 1990"}],"db_id":"professional_basketball"}307{"prompt":"How many images have \"vegetable\" and \"fruits\" as their object 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        How many images have \"vegetable\" and \"fruits\" as their object classes?\n        \n        ### Context:\n        #### Query Context:\n        images refer to IMG_ID; \"vegetables\" and \"fruits\" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits';\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(T1.IMG_ID) FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T2.OBJ_CLASS = 'vegetables' OR T2.OBJ_CLASS = 'fruits'"}],"db_id":"image_and_language"}308{"prompt":"Where's Foon Yue Tseng's office located at? Give the detailed address.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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's Foon Yue Tseng's office located at? Give the detailed address.\n        \n        ### Context:\n        #### Query Context:\n        Detailed address comprises addressLine1 and addressLine2;\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.addressLine1, T1.addressLine2 FROM offices AS T1 INNER JOIN employees AS T2 ON T1.officeCode = T2.officeCode WHERE T2.firstName = 'Foon Yue' AND T2.lastName = 'Tseng'"}],"db_id":"car_retails"}309{"prompt":"How many sales teams are there in the Midwest?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 teams are there in the Midwest?\n        \n        ### Context:\n        #### Query Context:\n        \"Midwest\" is 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 = 'Midwest' THEN 1 ELSE 0 END) FROM `Sales Team`"}],"db_id":"regional_sales"}310{"prompt":"Calculate the average players out in the first innings per match. How many of them were out by the leg before wicket?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 players out in the first innings per match. How many of them were out by the leg before wicket?\n        \n        ### Context:\n        #### Query Context:\n        out by the leg refers to Out_Name = 'lbw'; out in the first innings refers to Innings_No = 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 CAST(COUNT(T1.Player_Out) AS REAL) \/ COUNT(T1.Match_Id), SUM(CASE WHEN T2.Out_Name = 'lbw' THEN 1 ELSE 0 END) FROM Wicket_Taken AS T1 INNER JOIN Out_Type AS T2 ON T1.Kind_Out = T2.Out_Id WHERE T1.Innings_No = 2"}],"db_id":"soccer_2016"}311{"prompt":"How many orders were returned in the 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        How many orders were returned in the year 2020?\n        \n        ### Context:\n        #### Query Context:\n        returned refers to status_value = 'Returned'; in the year 2020 refers to status_date LIKE '%2020%'\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_status AS T1 INNER JOIN order_history AS T2 ON T1.status_id = T2.status_id WHERE T1.status_value = 'Returned' AND STRFTIME('%Y', T2.status_date) = '2020'"}],"db_id":"books"}312{"prompt":"How much more votes for episode 1 than for episode 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 much more votes for episode 1 than for episode 5?\n        \n        ### Context:\n        #### Query Context:\n        more votes refers to SUBTRACT(SUM(votes when episode = 1), SUM(votes when episode = 5))\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 SUM(CASE WHEN T1.episode = 1 THEN T2.votes ELSE 0 END) - SUM(CASE WHEN T1.episode = 5 THEN T2.votes ELSE 0 END) AS diff FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id;"}],"db_id":"simpson_episodes"}313{"prompt":"Give the location of the university ID 112.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 location of the university ID 112.\n        \n        ### Context:\n        #### Query Context:\n        location 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.id = 112"}],"db_id":"university"}314{"prompt":"Name the performer who won Emmy Award for Outstanding Voice-Over Performance by playing Homer simpson 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        Name the performer who won Emmy Award for Outstanding Voice-Over Performance by playing Homer simpson 20.\n        \n        ### Context:\n        #### Query Context:\n        Outstanding Voice-Over Performance refers to award = 'Outstanding Voice-Over Performance'; who won refers to result = 'Winner'; Emmy refers to organization = 'Primetime Emmy Awards'; playing Homer simpson 20 refers to character = 'Homer simpson 20'\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.person FROM Award AS T1 INNER JOIN Character_Award AS T2 ON T1.award_id = T2.award_id WHERE T2.character = 'Homer simpson 20' AND T1.organization = 'Primetime Emmy Awards' AND T1.award = 'Outstanding Voice-Over Performance' AND T1.result = 'Winner';"}],"db_id":"simpson_episodes"}315{"prompt":"Which product got the most five stars, and how many?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 got the most five stars, and how many?\n        \n        ### Context:\n        #### Query Context:\n        most five stars refers to MAX(COUNT(stars = 5));\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 T.Product, MAX(T.num) FROM ( SELECT Product, COUNT(Stars) AS num FROM reviews WHERE Stars = 5 GROUP BY Product ) T"}],"db_id":"retail_complains"}316{"prompt":"What is the number of female graduates between 2011 to 2013 from the state where 'Gateway Community College' 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 number of female graduates between 2011 to 2013 from the state where 'Gateway Community College' is located?\n        \n        ### Context:\n        #### Query Context:\n        female refers to gender = 'F'; graduates refers to grad_cohort; between 2011 to 2013 refers to year BETWEEN 2011 AND 2013; Gateway Community College refers to chronname = 'Gateway 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(T2.grad_cohort) FROM institution_details AS T1 INNER JOIN state_sector_grads AS T2 ON T1.state = T2.state WHERE T2.year BETWEEN 2011 AND 2013 AND T1.chronname = 'Gateway Community College' AND T2.gender = 'F'"}],"db_id":"college_completion"}317{"prompt":"List all the seas with which the deepest sea merges.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 seas with which the deepest sea merges.\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.Sea2 FROM sea AS T1 INNER JOIN mergesWith AS T2 ON T1.Name = T2.Sea1 WHERE T1.Name = ( SELECT Name FROM sea ORDER BY Depth DESC LIMIT 1 )"}],"db_id":"mondial_geo"}318{"prompt":"Please list the names of the courses taken by Laughton Antonio.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 courses taken by Laughton Antonio.\n        \n        ### Context:\n        #### Query Context:\n        \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.f_name = 'Laughton' AND T1.l_name = 'Antonio'"}],"db_id":"cs_semester"}319{"prompt":"Among the students with a capability below 3, what is the difference of undergraduate students from research 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        Among the students with a capability below 3, what is the difference of undergraduate students from research postgraduate students?\n        \n        ### Context:\n        #### Query Context:\n        capability < 3; difference = subtract(count(type = 'UG')), (count(type = 'RPG')); undergraduate students refers to type = 'UG'; research postgraduate students refers to type = 'RPG';\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 SUM(CASE WHEN T2.type = 'UG' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.type = 'RPG' THEN 1 ELSE 0 END) FROM RA AS T1 INNER JOIN student AS T2 ON T1.student_id = T2.student_id WHERE T1.capability < 3"}],"db_id":"cs_semester"}320{"prompt":"Name the first movie released by Disney.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 first movie released by Disney.\n        \n        ### Context:\n        #### Query Context:\n        The first movie released refers to movie_title where substr(release_date, length(release_date) - 1, length(release_date)) asc limit 1;\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 movie_title FROM characters ORDER BY SUBSTR(release_date, LENGTH(release_date) - 1, LENGTH(release_date)) ASC LIMIT 1"}],"db_id":"disney"}321{"prompt":"Who is the sponsor of menu with ID 12463?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 sponsor of menu with ID 12463?\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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 sponsor FROM Menu WHERE id = 12463"}],"db_id":"menu"}322{"prompt":"Find the location of businesses that has business hours from 7 am to 7 pm every Wednesday.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 location of businesses that has business hours from 7 am to 7 pm every Wednesday.\n        \n        ### Context:\n        #### Query Context:\n        location of business refers to city; business hours from 7am to 7pm refers to opening_time = '7AM' AND closing_time = '7PM'; Wednesday refers to day_of_week = 'Wednesday'\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 T1.city 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 T2.opening_time = '7AM' AND T2.closing_time = '7PM' AND T3.day_of_week = 'Wednesday'"}],"db_id":"public_review_platform"}323{"prompt":"Compute the average score of the university located in 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        Compute the average score of the university located in Brazil.\n        \n        ### Context:\n        #### Query Context:\n        average score refers to avg(score); located in Brazil refers to country_name = 'Brazil';\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 university AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.university_id INNER JOIN country AS T3 ON T3.id = T1.country_id WHERE T3.country_name = 'Brazil'"}],"db_id":"university"}324{"prompt":"What were the locations of the events on 8th 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        What were the locations of the events on 8th May, 2016?\n        \n        ### Context:\n        #### Query Context:\n        location = longitude, latitude; on 8th May, 2016 refers to `timestamp` LIKE '2016-05-08%';\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 SUBSTR(`timestamp`, 1, 10) = '2016-05-08'"}],"db_id":"talkingdata"}325{"prompt":"What are the years that \"WiiU\" got a new 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        What are the years that \"WiiU\" got a new game?\n        \n        ### Context:\n        #### Query Context:\n        year refers to release_year; \"WiiU\" refers to platform_name = 'WiiU'\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.release_year FROM platform AS T1 INNER JOIN game_platform AS T2 ON T1.id = T2.platform_id WHERE T1.platform_name = 'WiiU' ORDER BY T2.release_year DESC LIMIT 1"}],"db_id":"video_games"}326{"prompt":"To which predicted relation class does the self-relation of the object sample in image no.5 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 predicted relation class does the self-relation of the object sample in image no.5 belong?\n        \n        ### Context:\n        #### Query Context:\n        predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5\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 T1.PRED_CLASS FROM PRED_CLASSES AS T1 INNER JOIN IMG_REL AS T2 ON T1.PRED_CLASS_ID = T2.PRED_CLASS_ID WHERE T2.IMG_ID = 5 AND T2.OBJ1_SAMPLE_ID = T2.OBJ2_SAMPLE_ID"}],"db_id":"image_and_language"}327{"prompt":"How many wins did the Philadelphia Flyers have over the Boston Bruins in 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 wins did the Philadelphia Flyers have over the Boston Bruins in 1985?\n        \n        ### Context:\n        #### Query Context:\n        Philadelphia Flyers is name of team playing; Boston Bruins is name of opposing team where oppID = 'BOS'; year = 1985; wins refer 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'], [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 T1.W FROM TeamVsTeam AS T1 INNER JOIN Teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.year = 1985 AND T1.tmID = ( SELECT DISTINCT tmID FROM Teams WHERE name = 'Philadelphia Flyers' ) AND T1.oppID = ( SELECT DISTINCT tmID FROM Teams WHERE name = 'Boston Bruins' )"}],"db_id":"hockey"}328{"prompt":"What are the content ratings for the apps that have \"gr8\" in their comments?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 content ratings for the apps that have \"gr8\" in their comments?\n        \n        ### Context:\n        #### Query Context:\n        app with gr8 in their comments refers to Translated_Review LIKE '%gr8%';\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.`Content Rating` FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Translated_Review LIKE '%gr8%'"}],"db_id":"app_store"}329{"prompt":"How many customers have a credit limit of not more than 100,000 and which customer made the highest total payment amount for the year 2004?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 have a credit limit of not more than 100,000 and which customer made the highest total payment amount for the year 2004?\n        \n        ### Context:\n        #### Query Context:\n        creditLimit < = 100000; total payment amount refers to amount; highest total payment amount refers to MAX(amount); year(paymentDate) = '2004';\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 ( SELECT COUNT(customerNumber) FROM customers WHERE creditLimit <= 100000 AND customerNumber IN ( SELECT customerNumber FROM payments WHERE STRFTIME('%Y', paymentDate) = '2004' ) ), T1.customerName FROM customers AS T1 INNER JOIN payments AS T2 ON T1.customerNumber = T2.customerNumber WHERE T1.creditLimit <= 100000 AND STRFTIME('%Y', T2.paymentDate) = '2004' GROUP BY T1.customerNumber, T1.customerName ORDER BY SUM(T2.amount) DESC LIMIT 1"}],"db_id":"car_retails"}330{"prompt":"How many sales values have been created by sales representative and which sales representative have the highest 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        How many sales values have been created by sales representative and which sales representative have the highest sales?\n        \n        ### Context:\n        #### Query Context:\n        sales representative refers to Title = 'Sales Representative'; sales values refers to MULTIPLY(Quantity, UnitPrice); the highest sales refers to max(MULTIPLY(Quantity, 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 SUM(T3.UnitPrice * T3.Quantity) FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN `Order Details` AS T3 ON T2.OrderID = T3.OrderID WHERE T1.Title = 'Sales Representative' ORDER BY SUM(T3.UnitPrice * T3.Quantity)"}],"db_id":"retail_world"}331{"prompt":"Who were the owners of eateries which had highest health hazard by improper cooking time or temperatures?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 were the owners of eateries which had highest health hazard by improper cooking time or temperatures?\n        \n        ### Context:\n        #### Query Context:\n        owners of eateries refer to owner_name; highest health hazard by improper cooking time or temperatures refers to risk_category = 'High Risk' and description = 'Improper cooking time or temperatures';\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 T2.owner_name FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.risk_category = 'High Risk' AND T1.description = 'Improper cooking time or temperatures'"}],"db_id":"food_inspection"}332{"prompt":"How many part supplies were nearly out of 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        How many part supplies were nearly out of stock?\n        \n        ### Context:\n        #### Query Context:\n        supplies nearly out of stock refer to ps_partkey where ps_availqty < 10;\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(ps_suppkey) FROM partsupp WHERE ps_availqty < 10"}],"db_id":"retails"}333{"prompt":"How many orders in 1998 had a total price under 950?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 in 1998 had a total price under 950?\n        \n        ### Context:\n        #### Query Context:\n        1998 refers to year(o_orderdate) = '1998'; a total price under 950 refers to o_totalprice < 950\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(o_orderkey) AS countorders FROM orders WHERE STRFTIME('%Y', o_orderdate) = '1998' AND o_totalprice < 950"}],"db_id":"retails"}334{"prompt":"List the character names played by Catherine Deneuve.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 played by Catherine Deneuve.\n        \n        ### Context:\n        #### Query Context:\n        \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 person AS T1 INNER JOIN movie_cast AS T2 ON T1.person_id = T2.person_id WHERE T1.person_name = 'Catherine Deneuve'"}],"db_id":"movies_4"}335{"prompt":"List down the movies produced by Lucasfilm.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 movies produced by Lucasfilm.\n        \n        ### Context:\n        #### Query Context:\n        movies refers to title; produced by Lucasfil refers to company_name = 'Lucasfilm'\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 = 'Lucasfilm'"}],"db_id":"movies_4"}336{"prompt":"State the documentary film titles with longest length.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 documentary film titles with longest length.\n        \n        ### Context:\n        #### Query Context:\n        documentary film refers to name = 'documentary'; longest length refers to max(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 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 T3.category_id = T2.category_id WHERE T3.name = 'documentary' ORDER BY T1.length DESC LIMIT 1"}],"db_id":"movie_3"}337{"prompt":"What province did the river Klaeaelv travel through and how long is the river?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 province did the river Klaeaelv travel through and how long is the river?\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.Province 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 T3.Name = 'Klaraelv'"}],"db_id":"mondial_geo"}338{"prompt":"What is the country and state of the city named Dalton?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 country and state of the city named Dalton?\n        \n        ### Context:\n        #### Query Context:\n        Dalton 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 T2.county FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state INNER JOIN zip_data AS T3 ON T2.zip_code = T3.zip_code WHERE T3.city = 'Dalton' GROUP BY T2.county"}],"db_id":"address"}339{"prompt":"Indicate the time frame and details of the most recent care plan suggested to Jacquelyn Shanahan.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 time frame and details of the most recent care plan suggested to Jacquelyn Shanahan.\n        \n        ### Context:\n        #### Query Context:\n        time frame = SUBTRACT(JULIANDAY(careplans.STOP), JULIANDAY(careplans.START)); details of care plan refers to careplans.DESCRIPTION; most recent care plan refers to MIN(careplans.STAR);\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 strftime('%J', T2.STOP) - strftime('%J', T2.START) AS timeFrame , T2.DESCRIPTION FROM patients AS T1 INNER JOIN careplans AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Jacquelyn' AND T1.last = 'Shanahan' ORDER BY T2.START DESC LIMIT 1"}],"db_id":"synthea"}340{"prompt":"How many films in the horror category were included in PG-13-rated?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 in the horror category were included in PG-13-rated?\n        \n        ### Context:\n        #### Query Context:\n        \"Horror\" is the name of category; PG-13 rated 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 = 'Horror' AND T1.rating = 'PG-13'"}],"db_id":"movie_3"}341{"prompt":"How many of Gary Trousdale's movies are adventure 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        How many of Gary Trousdale's movies are adventure movies?\n        \n        ### Context:\n        #### Query Context:\n        Gary Trousdale refers director = 'Gary Trousdale'; the 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(T.name) FROM ( SELECT T1.name FROM director AS T1 INNER JOIN movies_total_gross AS T2 ON T1.name = T2.movie_title WHERE T1.director = 'Gary Trousdale' AND T2.genre = 'Adventure' GROUP BY T1.name ) T"}],"db_id":"disney"}342{"prompt":"Provide the average number of papers that are published in the journal named 'Information Sciences' annually.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 average number of papers that are published in the journal named 'Information Sciences' annually.\n        \n        ### Context:\n        #### Query Context:\n        'Information Sciences' is the FullName of journal; average = DIVIDE(COUNT(JournalId = 48), COUNT(Years))\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(COUNT(T2.JournalId) AS REAL) \/ COUNT(DISTINCT T2.Year) FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T1.FullName = 'Information Sciences'"}],"db_id":"authors"}343{"prompt":"How many orders were shipped via Federal Shipping?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 were shipped via Federal Shipping?\n        \n        ### Context:\n        #### Query Context:\n        'Federal Shipping' is a CompanyName; orders refers to OrderID\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.OrderID) FROM Orders AS T1 INNER JOIN Shippers AS T2 ON T1.ShipVia = T2.ShipperID WHERE T2.CompanyName = 'Federal Shipping' AND T1.ShipVia = 3"}],"db_id":"retail_world"}344{"prompt":"How many awards did the \"Agony\" 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        How many awards did the \"Agony\" win?\n        \n        ### Context:\n        #### Query Context:\n        the \"Agony\" refers to title = 'Agony'; win refers to result = 'Winner'\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(T2.award) FROM Episode AS T1 INNER JOIN Award AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Agony' AND T2.result = 'Winner'"}],"db_id":"law_episode"}345{"prompt":"How many female athletes are from the Philippines?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 athletes are from the Philippines?\n        \n        ### Context:\n        #### Query Context:\n        female athletes refer to id where gender = 'F'; from the Philippines region_name = 'Philippines';\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 = 'Philippines' AND T3.gender = 'F'"}],"db_id":"olympics"}346{"prompt":"How many businesses are there in Scottsdale city under the category of \"Beauty & Spas\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are there in Scottsdale city under the category of \"Beauty & Spas\"?\n        \n        ### Context:\n        #### Query Context:\n        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 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.city LIKE 'Scottsdale' AND T1.category_name LIKE 'Beauty & Spas'"}],"db_id":"public_review_platform"}347{"prompt":"Among all the users who use a vivo device, what is the percentage of the users in the M23-26 user 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        Among all the users who use a vivo device, what is the percentage of the users in the M23-26 user group?\n        \n        ### Context:\n        #### Query Context:\n        vivo device refers to phone_brand = 'vivo'; percentage = MULTIPLY(DIVIDE(COUNT(phone_brand = 'vivo WHERE group = 'M23-26), COUNT(phone_brand = 'vivo)), 100); M23-26 user group refers to group = 'M23-26';\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 SUM(IIF(T1.`group` = 'M23-26', 1.0, 0)) \/ COUNT(T1.device_id) AS per FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T2.phone_brand = 'vivo'"}],"db_id":"talkingdata"}348{"prompt":"Calculate the 2016 gap between the average revenue for Indian and American films.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 2016 gap between the average revenue for Indian and American films.\n        \n        ### Context:\n        #### Query Context:\n        2016 refers to release_date LIKE '2016%'; Indian and American films refers to country_name = 'India' and country_name = 'United States of America'; gap between the average revenue refers to subtract(divide(sum(revenue), count(movie_id)) when country_name = 'United States of America', divide(sum(revenue), count(movie_id)) when country_name = 'India')\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 AVG(CASE WHEN T3.COUNTry_name = 'United States of America' THEN T1.revenue END) - AVG(CASE WHEN T3.COUNTry_name = 'India' THEN T1.revenue END) AS CALCULATE 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 WHERE CAST(STRFTIME('%Y', T1.release_date) AS INT) = 2016"}],"db_id":"movies_4"}349{"prompt":"Please list the names of all the neighborhoods in the community area with the most 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        Please list the names of all the neighborhoods in the community area with the most population.\n        \n        ### Context:\n        #### Query Context:\n        name of neighborhood refers to neighborhood_name; the most population refers to max(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 T1.neighborhood_name FROM Neighborhood AS T1 INNER JOIN Community_Area AS T2 ON T2.community_area_no = T2.community_area_no ORDER BY T2.population DESC LIMIT 1"}],"db_id":"chicago_crime"}350{"prompt":"State the ID number for the attribute named \"Accepts Insurance\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 ID number for the attribute named \"Accepts Insurance\"?\n        \n        ### Context:\n        #### Query Context:\n        ID number refers to attribute_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 attribute_id FROM Attributes WHERE attribute_name = 'Accepts Insurance'"}],"db_id":"public_review_platform"}351{"prompt":"List the top five most costly products in 1998.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 top five most costly products in 1998.\n        \n        ### Context:\n        #### Query Context:\n        most costly refers to max(add(unit price , Freight)); in 1998 refers to OrderDate > = '1998-01-01 00:00:00' AND OrderDate < '1999-01-01 00:00:00'\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 T3.ProductName 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 T1.OrderDate LIKE '1998%' ORDER BY T3.UnitPrice + T1.Freight DESC LIMIT 5"}],"db_id":"retail_world"}352{"prompt":"What is the total number of cities that Japan 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 total number of cities that Japan have?\n        \n        ### Context:\n        #### Query Context:\n        Japan is a 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 COUNT(T3.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 T1.Name = 'Japan'"}],"db_id":"mondial_geo"}353{"prompt":"List the address in Texas in the ascending order of city 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        List the address in Texas in the ascending order of city id.\n        \n        ### Context:\n        #### Query Context:\n        'Texas' is a district\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 address FROM address WHERE district = 'Texas' AND city_id = ( SELECT MIN(city_id) FROM address WHERE district = 'Texas' )"}],"db_id":"movie_3"}354{"prompt":"What were the products supplied by the company in Spain?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 were the products supplied by the company in Spain?\n        \n        ### Context:\n        #### Query Context:\n        company in Spain refers to Country = 'Spain'; product supplied refers 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.Country = 'Spain'"}],"db_id":"retail_world"}355{"prompt":"List the names of the top five coins traded on January 1, 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        List the names of the top five coins traded on January 1, 2014.\n        \n        ### Context:\n        #### Query Context:\n        the top five coins refers to cmc_rank< = 5; on January 1, 2014 refers to date = '2014-01-01'\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 FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T2.date = '2014-01-01' AND T2.cmc_rank <= 5"}],"db_id":"coinmarketcap"}356{"prompt":"How many hall of famers both played and coached in the 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 hall of famers both played and coached in the league?\n        \n        ### Context:\n        #### Query Context:\n        hall of famers refers to hofID; both played and coached means playerID is not NULL and coachID is not 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, 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[290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT COUNT(playerID) FROM Master WHERE hofID IS NOT NULL AND playerID IS NOT NULL AND coachID IS NOT NULL"}],"db_id":"hockey"}357{"prompt":"Among the review votes of funny and cool hit uber with long review length, describe the business ID, active status, user ID and user year of joining Yelp.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 review votes of funny and cool hit uber with long review length, describe the business ID, active status, user ID and user year of joining Yelp.\n        \n        ### Context:\n        #### Query Context:\n        review votes of funny refers to review_votes_funny = 'Uber'; cool hit uber refers to review_votes_cool = 'Uber'; user year of joining Yelp refers to 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 T1.business_id, T1.active, T3.user_id, T3.user_yelping_since_year FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id INNER JOIN Users AS T3 ON T2.user_id = T3.user_id WHERE T2.review_votes_cool = 'Uber' AND T2.review_votes_funny = 'Uber' AND T2.review_length = 'Long'"}],"db_id":"public_review_platform"}358{"prompt":"Which flight carrier operator has the most 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        Which flight carrier operator has the most cancelled flights?\n        \n        ### Context:\n        #### Query Context:\n        flight carrier operator refers to OP_CARRIER_AIRLINE_ID; most cancelled flights refers to MAX(COUNT(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 ORDER BY T2.CANCELLED DESC LIMIT 1"}],"db_id":"airline"}359{"prompt":"Who had to take Clopidogrel 75 MG Oral Tablet for over 10 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        Who had to take Clopidogrel 75 MG Oral Tablet for over 10 years?\n        \n        ### Context:\n        #### Query Context:\n        Who implies the full name of the patient which refers to first, last; Clopidogrel 75 MG Oral Tablet refers to medications where DESCRIPTION = 'Clopidogrel 75 MG Oral Tablet'; for over 10 years refers to SUBTRACT(strftime('%Y', STOP), strftime('%Y', START)) > 10;\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 medications AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'Clopidogrel 75 MG Oral Tablet' AND strftime('%Y', T2.STOP) - strftime('%Y', T2.START) > 10"}],"db_id":"synthea"}360{"prompt":"How many legislators have an Instagram account?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 legislators have an Instagram account?\n        \n        ### Context:\n        #### Query Context:\n        have an Instagram account refers to instagram is NOT null and instagram <>''\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 `social-media` WHERE instagram IS NOT NULL AND instagram <> ''"}],"db_id":"legislator"}361{"prompt":"What is the percentage of female athletes below 20s who participated in the 2002 Winter Olympic?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 female athletes below 20s who participated in the 2002 Winter Olympic?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(person_id where gender = 'F' and age < 20), COUNT(person_id)) as percentage where games_name = '2002 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 CAST(COUNT(CASE WHEN T3.gender = 'F' THEN 1 ELSE NULL END) AS REAL) * 100 \/ COUNT(T2.person_id) 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 T1.games_name = '2002 Winter' AND T2.age < 20"}],"db_id":"olympics"}362{"prompt":"How many legislators are not 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        How many legislators are not senator?\n        \n        ### Context:\n        #### Query Context:\n        not senator refers to class is 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 COUNT(bioguide) FROM `current-terms` WHERE class IS NULL"}],"db_id":"legislator"}363{"prompt":"What is the name of the player with the highest number of outstanding player awards in a particular match?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 player with the highest number of outstanding player awards in a particular match?\n        \n        ### Context:\n        #### Query Context:\n        name of the player refers to Player_Name; the highest number of outstanding player awards refers to max(count(Man_of_the_Match))\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 Match AS T2 ON T1.Player_Id = T2.Man_of_the_Match GROUP BY T2.Man_of_the_Match ORDER BY COUNT(T2.Man_of_the_Match) DESC LIMIT 1"}],"db_id":"soccer_2016"}364{"prompt":"What is the language ID of the movie \"Walk the 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        What is the language ID of the movie \"Walk the Line\"?\n        \n        ### Context:\n        #### Query Context:\n        movie \"Walk the Line\" refers to title = 'Walk the Line'\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.language_id FROM movie AS T1 INNER JOIN movie_languages AS T2 ON T1.movie_id = T2.movie_id WHERE T1.title = 'Walk the Line'"}],"db_id":"movies_4"}365{"prompt":"Among students that gave satisfaction of value 4 for the course named \"Statistical Learning\", how many of them have a gpa of 3.8?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 students that gave satisfaction of value 4 for the course named \"Statistical Learning\", how many of them have a gpa of 3.8?\n        \n        ### Context:\n        #### Query Context:\n        satisfaction refers to sat;\n\nsat = 4; gpa = 3.8 \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 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 = 'Statistical learning' AND T2.sat = 4 AND T1.gpa = 3.8"}],"db_id":"cs_semester"}366{"prompt":"What's the title of the episode that got the most 7-star votes in star 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's the title of the episode that got the most 7-star votes in star score?\n        \n        ### Context:\n        #### Query Context:\n        7-stars vote refers to stars = 7;\u00a0 most refers to Max(votes)\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.title FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE T2.stars = 7 ORDER BY T2.votes DESC LIMIT 1;"}],"db_id":"simpson_episodes"}367{"prompt":"What is the full name of the customer who owns the \"aalleburtonkc@yellowbook.com\" e-mail address?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 owns the \"aalleburtonkc@yellowbook.com\" e-mail address?\n        \n        ### Context:\n        #### Query Context:\n        \"aalleburtonkc@yellowbook.com\" is the email of customer; 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 first_name, last_name FROM customer WHERE email = 'aalleburtonkc@yellowbook.com'"}],"db_id":"books"}368{"prompt":"Which city hosted the least number of no-result 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        Which city hosted the least number of no-result matches?\n        \n        ### Context:\n        #### Query Context:\n        city refers to City_Name; no-result matches refers to Win_type = 'NoResult'; least number refers to min(count(Win_type = 'NoResult'))\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 T4.City_Name FROM `Match` AS T1 INNER JOIN Win_By AS T2 ON T1.Win_Type = T2.Win_Id INNER JOIN Venue AS T3 ON T1.Venue_Id = T3.Venue_Id INNER JOIN City AS T4 ON T3.City_Id = T4.City_Id WHERE T2.Win_Type = 'NO Result' GROUP BY T4.City_Id ORDER BY COUNT(T2.Win_Type) ASC LIMIT 1"}],"db_id":"soccer_2016"}369{"prompt":"What is the most expensive 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        What is the most expensive product?\n        \n        ### Context:\n        #### Query Context:\n        product refers to ProductName; 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 ProductName FROM Products WHERE UnitPrice = ( SELECT MAX(UnitPrice) FROM Products )"}],"db_id":"retail_world"}370{"prompt":"What is the title of the most expensive 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 title of the most expensive book?\n        \n        ### Context:\n        #### Query Context:\n        most expensive book 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 T1.title FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id ORDER BY T2.price DESC LIMIT 1"}],"db_id":"books"}371{"prompt":"On what date were the most events logged on devices for 40-year-old male 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        On what date were the most events logged on devices for 40-year-old male users?\n        \n        ### Context:\n        #### Query Context:\n        date refers to timestamp; most events refers to MAX(COUNT(event_id)); 40-year-old\u00a0 refers to age = 40; male refers to gender = 'M';\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.timestamp FROM ( SELECT T2.timestamp, COUNT(T2.event_id) AS num FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE T1.gender = 'M' AND T1.age = 40 GROUP BY T2.timestamp ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"talkingdata"}372{"prompt":"How many countries belong to the Algeria 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 belong to the Algeria region?\n        \n        ### Context:\n        #### Query Context:\n        the algeria region refers to r_name = 'ALGERIA'\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.r_name) FROM region AS T1 INNER JOIN nation AS T2 ON T1.r_regionkey = T2.n_regionkey WHERE T2.n_name = 'ALGERIA'"}],"db_id":"retails"}373{"prompt":"Calculate the ratio between unemployed students and 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        Calculate the ratio between unemployed students and disabled students.\n        \n        ### Context:\n        #### Query Context:\n        ratio refers to DIVIDE(COUNT(name from unemployed), COUNT(name from disabled))\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(( SELECT COUNT(name) FROM unemployed ) AS REAL ) \/ ( SELECT COUNT(name) FROM disabled )"}],"db_id":"student_loan"}374{"prompt":"Is student281 disabled and which school is the student enrolled 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        Is student281 disabled and which school is the student enrolled in?\n        \n        ### Context:\n        #### Query Context:\n        \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.name, T1.school FROM enrolled AS T1 INNER JOIN disabled AS T2 ON T1.`name` = T2.`name` WHERE T1.name = 'student281'"}],"db_id":"student_loan"}375{"prompt":"Find out the yearly income of geographic ID when the customer is female and occupation as 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        Find out the yearly income of geographic ID when the customer is female and occupation as sales.\n        \n        ### Context:\n        #### Query Context:\n        yearly income of geographic ID refers to GEOID where MULTIPLY(INHABITANTS_K, INCOME_K, 12); SEX = 'Female';\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 * T2.INCOME_K * 12 FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.SEX = 'Female' AND T1.OCCUPATION = 'Sales'"}],"db_id":"software_company"}376{"prompt":"In which year was the film with the highest replacement cost 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 was the film with the highest replacement cost released?\n        \n        ### Context:\n        #### Query Context:\n        highest replacement_cost refers to Max (replacement_cost); year refers to release_year\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 DISTINCT release_year FROM film WHERE replacement_cost = ( SELECT MAX(replacement_cost) FROM film )"}],"db_id":"movie_3"}377{"prompt":"How many emplyees have a good job performance?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 emplyees have a good job performance?\n        \n        ### Context:\n        #### Query Context:\n        good job performance refers to performance = 'Good'\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 performance = 'Good'"}],"db_id":"human_resources"}378{"prompt":"How many products using \"roadster_black_small.gif\" as the thumbnail 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        How many products using \"roadster_black_small.gif\" as the thumbnail photo?\n        \n        ### Context:\n        #### Query Context:\n        products using roadster_black_small.gif as the thumbnail photo refers to ThumbnailPhotoFileName = 'roadster_black_small.gif';\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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[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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'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 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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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'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 COUNT(DISTINCT T2.ProductID) FROM ProductPhoto AS T1 INNER JOIN ProductProductPhoto AS T2 ON T1.ProductPhotoID = T2.ProductPhotoID WHERE T1.LargePhotoFileName = 'roadster_black_large.gif'"}],"db_id":"works_cycles"}379{"prompt":"What is the role of K Goel in the match ID 335992?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 K Goel in the match ID 335992?\n        \n        ### Context:\n        #### Query Context:\n        role refers to Role_Desc; K Goel refers to Player_Name = 'K Goel'; match ID 335992 refers to Match_Id = 335992\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.Role_Desc FROM Player AS T1 INNER JOIN Player_Match AS T2 ON T2.Player_Id = T1.Player_Id INNER JOIN Rolee AS T3 ON T3.Role_Id = T2.Role_Id WHERE T2.Match_Id = 335992 AND T1.Player_Name = 'K Goel'"}],"db_id":"soccer_2016"}380{"prompt":"Who is the CBSA officer of the post point in the area with the highest number of 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        Who is the CBSA officer of the post point in the area with the highest number of employees?\n        \n        ### Context:\n        #### Query Context:\n        CBSA officer refers to CBSA_name; the highest number of employees refers to MAX(employees);\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.CBSA_name FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T2.employees = ( SELECT MAX(employees) FROM zip_data )"}],"db_id":"address"}381{"prompt":"Between 1917 to 1920, what are the names of the team who ranked first in the first half of the season 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        Between 1917 to 1920, what are the names of the team who ranked first in the first half of the season each year?\n        \n        ### Context:\n        #### Query Context:\n        year BETWEEN 1917 AND 1920; first half of season refers to half = 1\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, 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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 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 T2.name FROM TeamsHalf AS T1 INNER JOIN Teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.half = 1 AND T1.rank = 1 AND T1.year BETWEEN 1917 AND 1920"}],"db_id":"hockey"}382{"prompt":"Who is the patient involved in the care plan with code 311791003?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 patient involved in the care plan with code 311791003?\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 T2.first, T2.last FROM careplans AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T1.CODE = 315043002"}],"db_id":"synthea"}383{"prompt":"List down the eateries' IDs with structural inspection type in February 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        List down the eateries' IDs with structural inspection type in February 2016.\n        \n        ### Context:\n        #### Query Context:\n        eateries' IDs refer to business_id; structural inspection type refers to inspections WHERE type = 'Structural Inspection'; in February 2016 refers to year(date) = 2016 and month(date) = 2;\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 business_id FROM inspections WHERE type = 'Structural Inspection' AND `date` LIKE '2016-02%'"}],"db_id":"food_inspection"}384{"prompt":"What is the BMI of David Bornhammar?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 BMI of David Bornhammar?\n        \n        ### Context:\n        #### Query Context:\n        BMI = DIVIDE(weight_in_kg, power(DIVIDE(height_in_cm, 100), 2));\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(T2.weight_in_kg AS REAL) \/ (CAST(T3.height_in_cm AS REAL) \/ 100 * (CAST(T3.height_in_cm AS REAL) \/ 100)) FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id INNER JOIN height_info AS T3 ON T1.height = T3.height_id WHERE T1.PlayerName = 'David Bornhammar'"}],"db_id":"ice_hockey_draft"}385{"prompt":"Among the products that are both manufactured in house and salable, how many of them get over 10 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        Among the products that are both manufactured in house and salable, how many of them get over 10 reviews?\n        \n        ### Context:\n        #### Query Context:\n        manufactured in house refers to MakeFlag = 1; salable product refers to FinishedGoodsFlag = 1; over 10 reviews refers to count(comments)>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, 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[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'], 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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 T2.Name FROM ProductReview AS T1 INNER JOIN Product AS T2 USING (ProductID) WHERE T2.FinishedGoodsFlag = 1 AND T2.MakeFlag = 1 GROUP BY T2.Name ORDER BY COUNT(T1.COMMENTS) > 10"}],"db_id":"works_cycles"}386{"prompt":"Which customer addresses are no longer active?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 addresses are no longer active?\n        \n        ### Context:\n        #### Query Context:\n        no longer active refers to address_status = 'Inactive'; customer address refers to street_number, street_name, city\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 DISTINCT T1.street_name FROM address AS T1 INNER JOIN customer_address AS T2 ON T1.address_id = T2.address_id INNER JOIN address_status AS T3 ON T3.status_id = T2.status_id WHERE T3.address_status = 'Inactive'"}],"db_id":"books"}387{"prompt":"How many games were released 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        How many games were released in 1981?\n        \n        ### Context:\n        #### Query Context:\n        released in 1981 refers to release_year = 1981\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(T.id) FROM game_platform AS T WHERE T.release_year = 1981"}],"db_id":"video_games"}388{"prompt":"Calculate the percentage of legislators who are Senator and were born in 1964.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 legislators who are Senator and were born in 1964.\n        \n        ### Context:\n        #### Query Context:\n        are senator refers to class IS NOT NULL; born in 1964 refers to birthday_bio = 1964; calculation = MULTIPLY(DIVIDE(COUNT(class IS NOT NULL THEN bioguide_id), COUNT(bioguide_id)), 1.0)\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 CAST(SUM(CASE WHEN T2.class IS NOT NULL THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.birthday_bio LIKE '%1964%'"}],"db_id":"legislator"}389{"prompt":"Which year was the goalie who had the most postseaon shots Against in 2008 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        Which year was the goalie who had the most postseaon shots Against in 2008 born?\n        \n        ### Context:\n        #### Query Context:\n        the most postseason shots Against refers to max(PostSA); year born refers to birthYear\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'], 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'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, 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'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', 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'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.birthYear FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID WHERE T2.year = 2008 ORDER BY T2.PostSA DESC LIMIT 1"}],"db_id":"hockey"}390{"prompt":"Among the restaurants being inspected in 2016, how many of them are in 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        Among the restaurants being inspected in 2016, how many of them are in San Francisco?\n        \n        ### Context:\n        #### Query Context:\n        inspected in 2016 refers to YEAR(date) = 2016; San Francisco refers to city in ('San Francisco', 'SF' ,'S.F.', 'SAN FRANCISCO');\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 inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE STRFTIME('%Y', T1.`date`) = '2016' AND T2.city IN ('San Francisco', 'SAN FRANCISCO', 'SF', 'S.F.')"}],"db_id":"food_inspection"}391{"prompt":"Tell the Id number of the business with the most number of violations.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Id number of the business with the most number of violations.\n        \n        ### Context:\n        #### Query Context:\n        Id number for that business refers to business_id; the most number of violations refers to MAX(COUNT(business_id));\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 business_id FROM violations GROUP BY business_id ORDER BY COUNT(business_id) DESC LIMIT 1"}],"db_id":"food_inspection"}392{"prompt":"Which sport does the event \"Shooting Women's Trap\" 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        Which sport does the event \"Shooting Women's Trap\" belong to?\n        \n        ### Context:\n        #### Query Context:\n        sport refers to sport_name; event \"Shooting Women's Trap\" refers to event_name = 'Shooting Women''s Trap';\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.sport_name FROM sport AS T1 INNER JOIN event AS T2 ON T1.id = T2.sport_id WHERE T2.event_name LIKE 'Shooting Women%s Trap'"}],"db_id":"olympics"}393{"prompt":"How many solutions are there whose respositories received the number of stars more than one third of the number of forks?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are there whose respositories received the number of stars more than one third of the number of forks?\n        \n        ### Context:\n        #### Query Context:\n        number of solutions are there whose repositories received the number of stars more than one third of the number of forks refers to Stars>DIVIDE(Forks, 3);\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(DISTINCT T1.Id) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T1.Stars > CAST(T1.Forks AS REAL) \/ 3"}],"db_id":"codebase_comments"}394{"prompt":"Which state has the highest number of clients who gave a 5-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        Which state has the highest number of clients who gave a 5-star review?\n        \n        ### Context:\n        #### Query Context:\n        highest number of clients refers to MAX(COUNT(client_id)); 5-star review refers to stars = 5;\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 T2.state_abbrev FROM reviews AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T1.Stars = 5 GROUP BY T2.state_abbrev ORDER BY COUNT(T2.state_abbrev) DESC LIMIT 1"}],"db_id":"retail_complains"}395{"prompt":"Provide the name of the song from the movie directed by Ben Sharpsteen.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the song from the movie directed by Ben Sharpsteen.\n        \n        ### Context:\n        #### Query Context:\n        Ben Sharpsteen refers to director = 'Ben Sharpsteen';\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.song FROM characters AS T1 INNER JOIN director AS T2 ON T1.movie_title = T2.name WHERE T2.director = 'Ben Sharpsteen'"}],"db_id":"disney"}396{"prompt":"What is the average age of the users who use model device of R7?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 users who use model device of R7?\n        \n        ### Context:\n        #### Query Context:\n        average age = AVG(age);\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 SUM(T1.age) \/ COUNT(T1.device_id) AS avg FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T2.device_model = 'R7'"}],"db_id":"talkingdata"}397{"prompt":"What is the capital of Australia? Is the capital a headquarter to any organization? Name the organization(s).","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 capital of Australia? Is the capital a headquarter to any organization? Name the organization(s).\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.Capital, T1.Name FROM organization AS T1 INNER JOIN country AS T2 ON T1.City = T2.Capital WHERE T2.Name = 'Australia'"}],"db_id":"mondial_geo"}398{"prompt":"List the types and results of the inspections done on Riverwalk 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        List the types and results of the inspections done on Riverwalk caf\u00e9.\n        \n        ### Context:\n        #### Query Context:\n        type refers to inspection_type; Riverwalk caf\u00e9 refers to facility_type = 'RIVERWALK CAFE'\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 T2.inspection_type, T2.results FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T1.facility_type = 'RIVERWALK CAFE'"}],"db_id":"food_inspection_2"}399{"prompt":"How many books under 300 pages has HarperCollins Publishers published?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 under 300 pages has HarperCollins Publishers published?\n        \n        ### Context:\n        #### Query Context:\n        under 300 pages refers to num_pages < 300; 'HarperCollins Publishers\" 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 COUNT(*) FROM book AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.publisher_id WHERE T2.publisher_name = 'HarperCollins Publishers' AND T1.num_pages < 300"}],"db_id":"books"}400{"prompt":"Indicate the opening hours of businesses are with category in 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        Indicate the opening hours of businesses are with category in fashion.\n        \n        ### Context:\n        #### Query Context:\n        opening hours refers to opening_time; 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 T4.opening_time 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 Business_Hours AS T4 ON T3.business_id = T4.business_id WHERE T1.category_name LIKE 'Fashion'"}],"db_id":"public_review_platform"}401{"prompt":"Identify all the restaurants in Yolo County by their 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        Identify all the restaurants in Yolo County by their label.\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, T1.label FROM generalinfo AS T1 INNER JOIN geographic AS T2 ON T1.city = T2.city WHERE T2.county = 'yolo county'"}],"db_id":"restaurant"}402{"prompt":"What is the pair of words that is repeated the most times? Identify them 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        What is the pair of words that is repeated the most times? Identify them by their ID.\n        \n        ### Context:\n        #### Query Context:\n        repeated the most times refer to MAX(occurrences); pair is a relationship of two words: w1st and w2nd, where w1st is word id of the first word and w2nd is a word id of the second word;\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 w1st, w2nd FROM biwords WHERE occurrences = ( SELECT MAX(occurrences) FROM biwords )"}],"db_id":"language_corpus"}403{"prompt":"Please list the coach IDs who received the award twice from 1970 to 1990.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 coach IDs who received the award twice from 1970 to 1990.\n        \n        ### Context:\n        #### Query Context:\n        from 1970 to 1990 refers to year between 1970 and 1990;\u00a0 received award twice refers to coachID where Count(coachID) = 2\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 coachID FROM awards_coaches WHERE year BETWEEN 1970 AND 1990 GROUP BY coachID, award HAVING COUNT(award) = 2"}],"db_id":"professional_basketball"}404{"prompt":"What is the percentage of flights which landed at Pittsburgh were faster than scheduled?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 flights which landed at Pittsburgh were faster than scheduled?\n        \n        ### Context:\n        #### Query Context:\n        percentage = MULTIPLY(DIVIDE(SUM(ACTUAL_ELAPSED_TIME < T2.CRS_ELAPSED_TIME), COUNT(Code)), 100); landed at refers to DEST; Pittsburgh refers to Description which contains 'Pittsburgh'; faster than scheduled refers to ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME;\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 CAST(SUM(CASE WHEN T1.ACTUAL_ELAPSED_TIME < T1.CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T2.Code = T1.DEST WHERE T2.Description LIKE '%Pittsburgh%' AND T1.CRS_ELAPSED_TIME IS NOT NULL AND T1.ACTUAL_ELAPSED_TIME IS NOT NULL"}],"db_id":"airline"}405{"prompt":"In which province and country does Moldoveanu located? State its height.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 province and country does Moldoveanu located? State its height.\n        \n        ### Context:\n        #### Query Context:\n        Moldoveanu is a mountain\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.Province, T2.Country, T1.Height FROM mountain AS T1 INNER JOIN geo_mountain AS T2 ON T1.Name = T2.Mountain WHERE T1.Name = 'Moldoveanu'"}],"db_id":"mondial_geo"}406{"prompt":"Among Frank Welker's voice-acted movies, list the movie titles and the total gross when the estimated inflation rate was less than 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        Among Frank Welker's voice-acted movies, list the movie titles and the total gross when the estimated inflation rate was less than 2.\n        \n        ### Context:\n        #### Query Context:\n        Frank Welker refers to voice-actor = 'Frank Welker'; estimated inflation rate was less than 2 can be computed as follows DIVIDE(inflation_adjusted_gross, total_gross) as percentage < 2;\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, T1.total_gross FROM movies_total_gross AS T1 INNER JOIN `voice-actors` AS T2 ON T1.movie_title = T2.movie WHERE T2.`voice-actor` = 'Frank Welker' AND CAST(REPLACE(trim(T1.inflation_adjusted_gross, '$'), ',', '') AS REAL) * 1.0 \/ CAST(REPLACE(trim(T1.total_gross, '$'), ',', '') AS REAL) * 1.0 < 2"}],"db_id":"disney"}407{"prompt":"On 8\/29\/2013 at 6:14:01 PM, how many bikes were borrowed from San Jose Diridon Caltrain Station?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 8\/29\/2013 at 6:14:01 PM, how many bikes were borrowed from San Jose Diridon Caltrain Station?\n        \n        ### Context:\n        #### Query Context:\n        How many bikes borrowed can be computed as SUBTRACT(SUM(dock_count), bikes_available where name = 'San Jose Diridon Caltrain Station' and time = '2013\/08\/29 06:14:01');\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 SUM(T1.dock_count - T2.bikes_available) FROM station AS T1 INNER JOIN status AS T2 ON T1.id = T2.station_id WHERE T1.name = 'San Jose Diridon Caltrain Station' AND T2.time = '2013\/08\/29 06:14:01'"}],"db_id":"bike_share_1"}408{"prompt":"Name the Chief Executive Officer and when he\/she was hired.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Chief Executive Officer and when he\/she was hired.\n        \n        ### Context:\n        #### Query Context:\n        Chief Financial Offer is a 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 T1.fname, T1.lname, T1.hire_date FROM employee AS T1 INNER JOIN jobs AS T2 ON T1.job_id = T2.job_id WHERE T2.job_desc = 'Chief Financial Officier'"}],"db_id":"book_publishing_company"}409{"prompt":"Calculate the average number of images with an attribute class of \"keyboard\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 images with an attribute class of \"keyboard\".\n        \n        ### Context:\n        #### Query Context:\n        AVG(IMG_ID) where OBJ_CLASS = 'keyboard';\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 AVG(T1.IMG_ID) FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T2.OBJ_CLASS = 'keyboard'"}],"db_id":"image_and_language"}410{"prompt":"Student21 is enlisted in which organization and has the student 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        Student21 is enlisted in which organization and has the student been absent?\n        \n        ### Context:\n        #### Query Context:\n        organization refers to organ\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.month, T1.organ FROM enlist AS T1 INNER JOIN longest_absense_from_school AS T2 ON T1.`name` = T2.`name` WHERE T1.name = 'student21'"}],"db_id":"student_loan"}411{"prompt":"What was the percentage of the Bitcoins verifiably burned until 2018\/4\/28?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 percentage of the Bitcoins verifiably burned until 2018\/4\/28?\n        \n        ### Context:\n        #### Query Context:\n        the percentage of the Bitcoins verifiably burned = divide(subtract(SUM(max_supply), SUM(total_supply)),SUM(total_supply))*100%; until 2013\/4\/28 refers to date<'2013-04-08'\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 CAST((SUM(T2.max_supply) - SUM(T2.total_supply)) AS REAL) \/ SUM(T2.total_supply) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T2.date < '2018-04-28' AND T1.name = 'Bitcoin'"}],"db_id":"coinmarketcap"}412{"prompt":"Among the elderlies, state the last name of whose complaint is handled in server YIFAT?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 elderlies, state the last name of whose complaint is handled in server YIFAT?\n        \n        ### Context:\n        #### Query Context:\n        elder refers to age < = 65; last name refers to last\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.last FROM client AS T1 INNER JOIN callcenterlogs AS T2 ON T1.client_id = T2.`rand client` WHERE T1.age > 65 AND T2.server = 'YIFAT'"}],"db_id":"retail_complains"}413{"prompt":"What time does the business with ID no.12 open on Monday?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 time does the business with ID no.12 open on Monday?\n        \n        ### Context:\n        #### Query Context:\n        open time refers to opening_time; on Monday refers to day_of_week = 'Monday'; business with ID no. refers to business_id \n\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 T1.opening_time FROM Business_Hours AS T1 INNER JOIN Days AS T2 ON T1.day_id = T2.day_id WHERE T1.business_id = 12 AND T2.day_of_week = 'Monday'"}],"db_id":"public_review_platform"}414{"prompt":"What is the ID of the university that has only 1% of international students between 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        What is the ID of the university that has only 1% of international students between 2011 to 2015?\n        \n        ### Context:\n        #### Query Context:\n        has only 1% of international students refers to pct_international_students = 1; between 2011 to 2015 refers to year BETWEEN 2011 AND 2015; ID 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 university_id FROM university_year WHERE pct_international_students = 1 AND year BETWEEN 2011 AND 2015"}],"db_id":"university"}415{"prompt":"List the films' titles which were rented by Brian Wyman in July, 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        List the films' titles which were rented by Brian Wyman in July, 2005.\n        \n        ### Context:\n        #### Query Context:\n        rented in July 2005 refers to year (rental_date) = 2005 and month (rental_date) = 7\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 = 'BRIAN' AND T1.last_name = 'WYMAN' AND STRFTIME('%Y', T2.rental_date) = '2005' AND STRFTIME('%m',T2.rental_date) = '7'"}],"db_id":"movie_3"}416{"prompt":"From 7\/9\/2010 to 10\/29\/2013, how many black patients were immunized with the meningococcal MCV4P vaccine?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 7\/9\/2010 to 10\/29\/2013, how many black patients were immunized with the meningococcal MCV4P vaccine?\n        \n        ### Context:\n        #### Query Context:\n        From 7\/9\/2010 to 10\/29\/2013 refers to DATE between '2010-07-09' and '2013-10-29'; black patients refers to race = 'black'; immunized with the meningococcal MCV4P vaccine refers to immunizations.DESCRIPTION = 'meningococcal MCV4P';\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 = 'meningococcal MCV4P' AND T2.DATE BETWEEN '2010-07-09' AND '2013-10-29' AND T1.race = 'black'"}],"db_id":"synthea"}417{"prompt":"For person id No.2054, is his\/her vendor still active?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 person id No.2054, is his\/her vendor still active?\n        \n        ### Context:\n        #### Query Context:\n        ActiveFlag = 1 means 'Vendor no longer used'; ActiveFlag = 2 means 'Vendor is actively used\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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[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.ActiveFlag FROM Vendor AS T1 INNER JOIN BusinessEntityContact AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.PersonID = 2054"}],"db_id":"works_cycles"}418{"prompt":"Who is the oldest among all the casts and crews?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 among all the casts and crews?\n        \n        ### Context:\n        #### Query Context:\n        oldest refers to MIN(birthdate)\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 name FROM Person ORDER BY birthdate ASC LIMIT 1;"}],"db_id":"simpson_episodes"}419{"prompt":"List all the official and unofficial languages used by the country that declared its independence in 1830.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 official and unofficial languages used by the country that declared its independence in 1830.\n        \n        ### Context:\n        #### Query Context:\n        official language refers to IsOfficial = 'T'; unofficial language refers to IsOfficial = 'F'; declared independence in 1830 refers to IndepYear = 1830;\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, T2.IsOfficial FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode WHERE T1.IndepYear = 1830 GROUP BY T2.Language, T2.IsOfficial"}],"db_id":"world"}420{"prompt":"How many Olympic competitors are 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        How many Olympic competitors are from Finland?\n        \n        ### Context:\n        #### Query Context:\n        competitors refer to person_id; from Finland refers to region_name = 'Finland';\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(T1.person_id) FROM person_region AS T1 INNER JOIN noc_region AS T2 ON T1.region_id = T2.id WHERE T2.region_name = 'Finland'"}],"db_id":"olympics"}421{"prompt":"Which country has the smallest land area in square kilometers for 19 consecutive years starting from year 1961? Indicate how much is its land area in square kilometers in those years and the income group 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 smallest land area in square kilometers for 19 consecutive years starting from year 1961? Indicate how much is its land area in square kilometers in those years and the income group of the country.\n        \n        ### Context:\n        #### Query Context:\n        the smallest land area in square kilometers refers to min(value where IndicatorName like 'Land area (sq. km)'); for 19 consecutive years starting from year 1961 refers to Year between 1961 and 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.CountryName, SUM(T1.Value) area, T2.IncomeGroup FROM Indicators AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.IndicatorName = 'Land area (sq. km)' AND T1.Year >= 1961 AND T1.Year < 1980 GROUP BY T1.CountryCode ORDER BY SUM(T1.Value) ASC LIMIT 1"}],"db_id":"world_development_indicators"}422{"prompt":"Who is the heaviest player? State player ID of 5 heaviest 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        Who is the heaviest player? State player ID of 5 heaviest players.\n        \n        ### Context:\n        #### Query Context:\n        5 heaviest players refer to MAX(weight) limit to 5 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'], 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'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 playerID FROM Master ORDER BY weight DESC LIMIT 5"}],"db_id":"hockey"}423{"prompt":"How many patients of Irish ethnicity take medication for 'myocardial infarction'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 patients of Irish ethnicity take medication for 'myocardial infarction'?\n        \n        ### Context:\n        #### Query Context:\n        take medication for myocardial infarction refers to medications.REASONDESCRIPTION = 'Myocardial Infarction';\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 medications AS T2 ON T1.patient = T2.PATIENT WHERE T2.REASONDESCRIPTION = 'Myocardial Infarction' AND T1.ethnicity = 'irish'"}],"db_id":"synthea"}424{"prompt":"List out the number of object samples in image no.41 which are in the class of \"kitchen\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of object samples in image no.41 which are in the class of \"kitchen\"?\n        \n        ### Context:\n        #### Query Context:\n        object samples in the class of \"kitchen\" refer to OBJ_CLASS_ID where OBJ_CLASS = 'kitchen'; image no.41 refers to IMG_ID = 41 ;\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 SUM(CASE WHEN T2.OBJ_CLASS = 'kitchen' THEN 1 ELSE 0 END) FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T1.IMG_ID = 41"}],"db_id":"image_and_language"}425{"prompt":"How many role-playing games did Microsoft Game Studios 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 role-playing games did Microsoft Game Studios publish?\n        \n        ### Context:\n        #### Query Context:\n        role-playing game refers to genre_name = 'Role-Playing'; Microsoft Game Studios refers to publisher_name = 'Microsoft Game Studios'\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(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 = 'Role-Playing' AND T1.publisher_name = 'Microsoft Game Studios'"}],"db_id":"video_games"}426{"prompt":"Find the average number of downloads for Single-Tables released by '2Pac' between 2001 and 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        Find the average number of downloads for Single-Tables released by '2Pac' between 2001 and 2013.\n        \n        ### Context:\n        #### Query Context:\n        2Pac is an artist; releaseType = 'single'; between 2001 and 2013 refers to groupYear between 2001 and 2013; average number of downloads = AVG(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 AVG(totalSnatched) FROM torrents WHERE artist LIKE '2Pac' AND releaseType LIKE 'Single' AND groupYear BETWEEN 2001 AND 2013"}],"db_id":"music_tracker"}427{"prompt":"What is the average number of sales in Japan?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 sales in Japan?\n        \n        ### Context:\n        #### Query Context:\n        in Japan refers to region_name = 'Japan'; average number of sales = multiply(avg(num_sales), 100000) where region_name = 'Japan'\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 AVG(T2.num_sales) * 100000 AS avg_japan FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id WHERE T1.region_name = 'Japan'"}],"db_id":"video_games"}428{"prompt":"Please list the IDs of all the menus in which the dish \"Clear green turtle\" had appeared.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 menus in which the dish \"Clear green turtle\" had appeared.\n        \n        ### Context:\n        #### Query Context:\n        Clear green turtle is a name of dish;\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.menu_id FROM MenuPage AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.menu_page_id INNER JOIN Dish AS T3 ON T2.dish_id = T3.id WHERE T3.name = 'Clear green turtle'"}],"db_id":"menu"}429{"prompt":"Count the image numbers that contain the \"paint\" object.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 image numbers that contain the \"paint\" object.\n        \n        ### Context:\n        #### Query Context:\n        image numbers that contain the \"paint\" object refer to IMG_ID where OBJ_CLASS = 'paint';\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(DISTINCT T1.IMG_ID) FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T2.OBJ_CLASS = 'paint'"}],"db_id":"image_and_language"}430{"prompt":"How many crimes were committed at 018XX S KOMENSKY AVEin May 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 committed at 018XX S KOMENSKY AVEin May 2018?\n        \n        ### Context:\n        #### Query Context:\n         in May 2018 refers to date LIKE '5\/%\/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 date LIKE '5\/%\/2018%' THEN 1 ELSE 0 END) FROM Crime WHERE block = '018XX S KOMENSKY AVE'"}],"db_id":"chicago_crime"}431{"prompt":"What are the regions with Greek restaurants?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 regions with Greek restaurants?\n        \n        ### Context:\n        #### Query Context:\n        Greek restaurant refers to food_type = 'greek'\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 DISTINCT T1.region FROM geographic AS T1 INNER JOIN generalinfo AS T2 ON T1.city = T2.city WHERE T2.food_type = 'greek'"}],"db_id":"restaurant"}432{"prompt":"Calculate the average payment per product under confections 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        Calculate the average payment per product under confections category.\n        \n        ### Context:\n        #### Query Context:\n        under confections category refers to CategoryName = 'Confections';\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(T2.UnitPrice * T2.Quantity * (1 - T2.Discount)) \/ COUNT(T1.ProductID) FROM Products AS T1 INNER JOIN `Order Details` AS T2 ON T1.ProductID = T2.ProductID INNER JOIN Categories AS T3 ON T1.CategoryID = T3.CategoryID WHERE T3.CategoryName = 'Confections'"}],"db_id":"retail_world"}433{"prompt":"Which student failed the course Intro to Database 2? Please give his or 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        Which student failed the course Intro to Database 2? Please give his or her full name.\n        \n        ### Context:\n        #### Query Context:\n        If grade is NULL, it means that this student fails to pass the course; full name refers to f_name and l_name;\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 T2.grade IS NULL AND T3.name = 'Intro to Database 2'"}],"db_id":"cs_semester"}434{"prompt":"How many competitors over the age of 30 participated in the 1992 Winter 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        How many competitors over the age of 30 participated in the 1992 Winter Olympics?\n        \n        ### Context:\n        #### Query Context:\n        competitors over the age of 30 refer to person_id where age > 30; the 1992 Winter Olympics refers to games_name = '1992 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 COUNT(T2.person_id) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.games_name = '1992 Winter' AND T2.age > 30"}],"db_id":"olympics"}435{"prompt":"When was the highest amount of donated? How much was the 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        When was the highest amount of donated? How much was the amount?\n        \n        ### Context:\n        #### Query Context:\n        when refers donation_timestamp; the highest amount 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 donation_timestamp, donation_total FROM donations WHERE donation_total = ( SELECT donation_total FROM donations ORDER BY donation_total DESC LIMIT 1 )"}],"db_id":"donor"}436{"prompt":"Which product did Cruz & Sons Co. order on 2003\/3\/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        Which product did Cruz & Sons Co. order on 2003\/3\/3?\n        \n        ### Context:\n        #### Query Context:\n        Cruz & Sons Co. is name of customer; 2003\/3\/3 refers to orderDate;\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 t4.productName FROM orderdetails AS t1 INNER JOIN orders AS t2 ON t1.orderNumber = t2.orderNumber INNER JOIN customers AS t3 ON t2.customerNumber = t3.customerNumber INNER JOIN products AS t4 ON t1.productCode = t4.productCode WHERE t3.customerName = 'Cruz & Sons Co.' AND t2.orderDate = '2003-03-03'"}],"db_id":"car_retails"}437{"prompt":"What is the name of the player who has been chosen the most times for 'Man of the 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        What is the name of the player who has been chosen the most times for 'Man of the Series'?\n        \n        ### Context:\n        #### Query Context:\n        name of the player refers to Player_Name; most times for 'Man of the Series' refers to max(count(Man_of_the_Match))\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.Player_Name FROM Season AS T1 INNER JOIN Match AS T2 ON T1.Man_of_the_Series = T2.Man_of_the_Match INNER JOIN Player AS T3 ON T2.Man_of_the_Match = T3.Player_Id GROUP BY T3.Player_Name ORDER BY COUNT(T1.Man_of_the_Series) DESC LIMIT 1"}],"db_id":"soccer_2016"}438{"prompt":"List down the districts belong to the country headed by Adolf Ogi.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 districts belong to the country headed by Adolf Ogi.\n        \n        ### Context:\n        #### Query Context:\n        headed by Adolf Ogi refers to HeadOfState = 'Adolf Ogi';\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.District FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode WHERE T1.HeadOfState = 'Adolf Ogi'"}],"db_id":"world"}439{"prompt":"Which nations have a boundary with the Kalahari Desert?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 nations have a boundary with the Kalahari Desert?\n        \n        ### Context:\n        #### Query Context:\n        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 T3.Name FROM desert AS T1 INNER JOIN geo_desert AS T2 ON T1.Name = T2.Desert INNER JOIN country AS T3 ON T3.Code = T2.Country WHERE T1.Name = 'Kalahari'"}],"db_id":"mondial_geo"}440{"prompt":"List all countries with 'Category III' membership in 'IFAD' organization. Please also provide the capital 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        List all countries with 'Category III' membership in 'IFAD' organization. Please also provide the capital 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 Name, Capital FROM country WHERE Code IN ( SELECT Country FROM isMember WHERE type = 'Category III' AND Organization = 'IFAD' )"}],"db_id":"mondial_geo"}441{"prompt":"How many students have been absent less than 4 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        How many students have been absent less than 4 months?\n        \n        ### Context:\n        #### Query Context:\n        absent less than 4 months refers to month < 4;\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 < 4"}],"db_id":"student_loan"}442{"prompt":"Which county is El Cerrito 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 county is El Cerrito from?\n        \n        ### Context:\n        #### Query Context:\n        El Cerrito refers to city = 'el cerrito'\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 city = 'el cerrito'"}],"db_id":"restaurant"}443{"prompt":"Name the country with fastest growth in adjusted net national income in 1980 and state the currency used by this 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 with fastest growth in adjusted net national income in 1980 and state the currency used by this country.\n        \n        ### Context:\n        #### Query Context:\n        fastest growth refers to MAX(Value); IndicatorName = 'Adjusted net national income (annual % growth)'; Year = '1980'; currency refers to CurrencyUnit;\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.countryname, T1.CurrencyUnit FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T2.IndicatorName = 'Adjusted net national income (annual % growth)' AND T2.Year = 1980 AND T1.CurrencyUnit != '' ORDER BY T2.Value DESC LIMIT 1"}],"db_id":"world_development_indicators"}444{"prompt":"Provide the ISBN and price of the book with book ID 7160.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 ISBN and price of the book with book ID 7160.\n        \n        ### Context:\n        #### Query Context:\n        ISBN refers to isbn13;\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, T2.price FROM book AS T1 INNER JOIN order_line AS T2 ON T1.book_id = T2.book_id WHERE T2.book_id = 6503"}],"db_id":"books"}445{"prompt":"Provide the name of the airport which landed the most number of flights on 2018\/8\/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        Provide the name of the airport which landed the most number of flights on 2018\/8\/15.\n        \n        ### Context:\n        #### Query Context:\n        name of the airport refers to Description; airport that landed the most number of flights refers to MAX(COUNT(DEST)); on 2018\/8\/15 refers to FL_DATE = '2018\/8\/15';\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.DEST WHERE T2.FL_DATE = '2018\/8\/15' ORDER BY T2.DEST DESC LIMIT 1"}],"db_id":"airline"}446{"prompt":"Among the players who were born in July and August, how many of them got 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        Among the players who were born in July and August, how many of them got in the Hall of Fame?\n        \n        ### Context:\n        #### Query Context:\n        born in July and August refers to birthMon IN('7','8')\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', 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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(T1.playerID) FROM Master AS T1 INNER JOIN HOF AS T2 ON T1.hofID = T2.hofID WHERE T1.birthMon IN (7, 8)"}],"db_id":"hockey"}447{"prompt":"List all movies with the best rating score. State the movie title and number of Mubi user who loves 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        List all movies with the best rating score. State the movie title and number of Mubi user who loves the movie.\n        \n        ### Context:\n        #### Query Context:\n        best rating score refers to rating_score = 5; number of Mubi user who loves the movie 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 DISTINCT T2.movie_title, T2.movie_popularity FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T1.rating_score = 5"}],"db_id":"movie_platform"}448{"prompt":"What is the direction of the train with a diamond-shaped load in its 2nd car?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 direction of the train with a diamond-shaped load in its 2nd car?\n        \n        ### Context:\n        #### Query Context:\n        2nd car refers to position = 2\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 T2.direction FROM cars AS T1 INNER JOIN trains AS T2 ON T1.train_id = T2.id WHERE T1.position = 2 AND T1.shape = 'diamond'"}],"db_id":"trains"}449{"prompt":"Among the employees in the Manufacturing group in 2007, how many of them are store contacts?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 in the Manufacturing group in 2007, how many of them are store contacts?\n        \n        ### Context:\n        #### Query Context:\n        store contact refers to PersonType = 'SC'; year(EndDate)>2007 and year(StartDate)<2007;\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', '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], 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long.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 films that are 178 minutes long.\n        \n        ### Context:\n        #### Query Context:\n        178 min long refers to length = '178'\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 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[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 length = '178'"}],"db_id":"movie_3"}451{"prompt":"Sum up the likes get by short reviews on businesses located in City Goodyear.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Sum up the likes get by short reviews on businesses located in City Goodyear.\n        \n        ### Context:\n        #### Query Context:\n        short reviews refer to tip_length = 'Short';\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 SUM(T2.likes) AS likes FROM Business AS T1 INNER JOIN Tips AS T2 ON T1.business_id = T2.business_id WHERE T1.city = 'Goodyear'"}],"db_id":"public_review_platform"}452{"prompt":"Which product allows the company to make the highest profit on a single item among those that are the fastest to manufacture? Indicate the rating of the product if there 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        Which product allows the company to make the highest profit on a single item among those that are the fastest to manufacture? Indicate the rating of the product if there any.\n        \n        ### Context:\n        #### Query Context:\n        profit on a single item = SUBTRACT(ListPrice, StandardCost); length of time to manufacture refers to DaysToManufacture; fastest to manucature refers to MIN(DaysToManufacture);\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 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[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 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'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, T2.Rating FROM Product AS T1 INNER JOIN ProductReview AS T2 ON T1.ProductID = T2.ProductID WHERE T1.DaysToManufacture = ( SELECT DaysToManufacture FROM Product ORDER BY DaysToManufacture LIMIT 1 ) ORDER BY T1.ListPrice - T1.StandardCost DESC LIMIT 1"}],"db_id":"works_cycles"}453{"prompt":"What are the top 2 countries with the highest number of indebted suppliers?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 2 countries with the highest number of indebted suppliers?\n        \n        ### Context:\n        #### Query Context:\n        country refers to c_name; highest number of indebted refers to max(sum(acctbal)) where 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 T1.s_nationkey ) AS T ORDER BY T.num LIMIT 2"}],"db_id":"retails"}454{"prompt":"Describe the ordered products which were the most overdue from required 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        Describe the ordered products which were the most overdue from required date.\n        \n        ### Context:\n        #### Query Context:\n        the most overdue from required date refers to MIN(SUBTRACT(ShippedDate, RequiredDate) < 0)\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 T3.ProductName 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 DATEDIFF(T1.ShippedDate, T1.RequiredDate) < 0"}],"db_id":"retail_world"}455{"prompt":"Find and list the names of districts which has below-average stars for Eagle Capital.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 names of districts which has below-average stars for Eagle Capital.\n        \n        ### Context:\n        #### Query Context:\n        below average = AVG(stars) < Stars; Eagle Capital refers to Product = 'Eagle Capital';\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 T2.division FROM reviews AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T1.Product = 'Eagle Capital' AND T1.Stars > ( SELECT AVG(Stars) FROM reviews AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id )"}],"db_id":"retail_complains"}456{"prompt":"List down the games ID of games held in Tokyo.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 games ID of games held in Tokyo.\n        \n        ### Context:\n        #### Query Context:\n        Tokyo refers to city_name = 'Tokyo';\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.games_id FROM games_city AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.id WHERE T2.city_name = 'Tokyo'"}],"db_id":"olympics"}457{"prompt":"What is the average number of horror movies among all movies 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 average number of horror movies among all movies genre?\n        \n        ### Context:\n        #### Query Context:\n        horror movies refers to genre_name = 'horror'; average number = divide(sum(movie_id) when genre_name = 'horror', count(movie_id))\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 T3.genre_name = 'Horror' THEN T1.movie_id ELSE NULL END) AS REAL) \/ 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"}],"db_id":"movies_4"}458{"prompt":"Which year has the most elite 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 year has the most elite users?\n        \n        ### Context:\n        #### Query Context:\n        year has the most elite users refers to year_id with MAX(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 year_id FROM Elite GROUP BY year_id ORDER BY COUNT(user_id) DESC LIMIT 1"}],"db_id":"public_review_platform"}459{"prompt":"How many research assistants of Ogdon Zywicki have an average 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        How many research assistants of Ogdon Zywicki have an average salary?\n        \n        ### Context:\n        #### Query Context:\n        research assistant refers to the student who serves for research where the abbreviation is RA; average salary refers to salary = 'med';\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.prof_id) FROM RA AS T1 INNER JOIN prof AS T2 ON T1.prof_id = T2.prof_id WHERE T2.first_name = 'Ogdon' AND T1.salary = 'med' AND T2.last_name = 'Zywicki'"}],"db_id":"cs_semester"}460{"prompt":"What is the percentage of list created by user who was a subscriber 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        What is the percentage of list created by user who was a subscriber when he created the list?\n        \n        ### Context:\n        #### Query Context:\n        was a subscriber refers to user_subscriber = 1; percentage refers to DIVIDE(COUNT(user_subscriber = 1),COUNT(list_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 CAST(SUM(CASE WHEN user_subscriber = 1 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(list_id) FROM lists_users"}],"db_id":"movie_platform"}461{"prompt":"What is the percentage of the payment amount in 2004 was made by Atelier graphique?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 payment amount in 2004 was made by Atelier graphique?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(SUM(amount) where customerName = 'Atelier graphique'), (SUM(amount)) as percentage where year(paymentDate) = 2004;\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(CASE WHEN t1.customerName = 'Atelier graphique' THEN t2.amount ELSE 0 END) * 100 \/ SUM(t2.amount) FROM customers AS t1 INNER JOIN payments AS t2 ON t1.customerNumber = t2.customerNumber WHERE STRFTIME('%Y', t2.paymentDate) = '2004'"}],"db_id":"car_retails"}462{"prompt":"List all trips where bikes were returned at location 37.331415, -121.8932. State the date the bike 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        List all trips where bikes were returned at location 37.331415, -121.8932. State the date the bike was borrowed.\n        \n        ### Context:\n        #### Query Context:\n        37.331415 and -121.8932 are latitude (lat) and longitude (long) coordinates indicating location; returned at refers to end_station_name; the date the bike was borrowed refers to start_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.end_station_name, T2.start_date FROM station AS T1 INNER JOIN trip AS T2 ON T2.end_station_name = T1.name WHERE T1.lat = 37.331415 AND T1.long = -121.8932"}],"db_id":"bike_share_1"}463{"prompt":"Indicate the name of the customers who have placed an order of 3 units in February 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        Indicate the name of the customers who have placed an order of 3 units in February 2018.\n        \n        ### Context:\n        #### Query Context:\n        name of customer refers to Customer Names; order of 3 unit refers to Order Quantity = 3; in February 2018 refers to OrderDate LIKE '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 T FROM ( SELECT DISTINCT CASE  WHEN T2.`Order Quantity` = 3 AND T2.OrderDate LIKE '2\/%\/18' THEN T1.`Customer Names` END AS T FROM Customers T1 INNER JOIN `Sales Orders` T2 ON T2._CustomerID = T1.CustomerID ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}464{"prompt":"What is the name of the vendor that the project \"Bloody Times\" uses for their 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 name of the vendor that the project \"Bloody Times\" uses for their resources?\n        \n        ### Context:\n        #### Query Context:\n        project \"Bloody Times\" refers to title = 'Bloody Times'\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 T3.vendor_name FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid INNER JOIN resources AS T3 ON T2.projectid = T3.projectid WHERE T1.title = 'Bloody Times'"}],"db_id":"donor"}465{"prompt":"Provide the average elevation of the cities with alias Amherst.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 average elevation of the cities with alias Amherst.\n        \n        ### Context:\n        #### Query Context:\n        AVG(elevation) where alias = 'Amherst';\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 AVG(T2.elevation) FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'Amherst'"}],"db_id":"address"}466{"prompt":"Among sales teams in Midwest region, which sales team has an order quantity greater than 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        Among sales teams in Midwest region, which sales team has an order quantity greater than 5?\n        \n        ### Context:\n        #### Query Context:\n        order quantity greater than 5 refers to Order Quantity > 5\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.`Sales Team` FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T2.Region = 'Midwest' AND T1.`Order Quantity` > 5"}],"db_id":"regional_sales"}467{"prompt":"How many times has Sunrisers Hyderabad been the toss winner of a 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 times has Sunrisers Hyderabad been the toss winner of a game?\n        \n        ### Context:\n        #### Query Context:\n        Sunrisers Hyderabad refers to Team_Name = 'Sunrisers Hyderabad'; time of toss winner refers to count(Toss_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 SUM(CASE WHEN Toss_Winner = ( SELECT Team_Id FROM Team WHERE Team_Name = 'Sunrisers Hyderabad' ) THEN 1 ELSE 0 END) FROM `Match`"}],"db_id":"soccer_2016"}468{"prompt":"Among the employees, give me the full names of those who have less than 4 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        Among the employees, give me the full names of those who have less than 4 territories.\n        \n        ### Context:\n        #### Query Context:\n        less than 4 territories refers to EmployeeID where Count(TerritoryID) < 4\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 FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T2.EmployeeID < 4"}],"db_id":"retail_world"}469{"prompt":"What are the names of the top 3 customers who paid the highest amount of price per order after discount?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 top 3 customers who paid the highest amount of price per order after discount?\n        \n        ### Context:\n        #### Query Context:\n        highest price per order after discount refers to Max(Subtract(Multiply (Unit Price, Order Quantity), Discount Applied)); name of customer refers to Customer Names\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 `Customer Names` FROM ( SELECT T1.`Customer Names` , REPLACE(T2.`Unit Price`, ',', '') * T2.`Order Quantity` - REPLACE(T2.`Unit Price`, ',', '') * T2.`Discount Applied` AS T FROM Customers T1 INNER JOIN `Sales Orders` T2 ON T2._CustomerID = T1.CustomerID ) ORDER BY T DESC LIMIT 3"}],"db_id":"regional_sales"}470{"prompt":"What are the names of the characters voiced by Frank Welker?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 characters voiced by Frank Welker?\n        \n        ### Context:\n        #### Query Context:\n        Frank Welker refers to voice-actor = 'Frank Welker';\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 character FROM `voice-actors` WHERE 'voice-actor' = 'Frank Welker'"}],"db_id":"disney"}471{"prompt":"Compare the total number of orders between customer \"Apollo Ltd\" and \"Pacific Ltd\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Compare the total number of orders between customer \"Apollo Ltd\" and \"Pacific Ltd\".\n        \n        ### Context:\n        #### Query Context:\n        \"Apollo Ltd\" and \"Pacific Ltd\" are both Customer Names; total number of orders refers to COUNT(OrderNumber)\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 T2.`Customer Names` = 'Apollo Ltd' THEN 1 ELSE 0 END), SUM(CASE WHEN T2.`Customer Names` = 'Pacific Ltd' THEN 1 ELSE 0 END) FROM `Sales Orders` AS T1 INNER JOIN Customers AS T2 ON T2.CustomerID = T1._CustomerID"}],"db_id":"regional_sales"}472{"prompt":"Provide the name, model, sweep volume, and introduced year of the car with the best crash protection.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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, model, sweep volume, and introduced year of the car with the best crash protection.\n        \n        ### Context:\n        #### Query Context:\n        car's name refers to car_name; sweep volume = divide(displacement, cylinders); introduced year refers to model_year; the best crash protection refers to max(weight)\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, T1.model, T1.displacement \/ T1.cylinders, T2.model_year FROM data AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID ORDER BY T1.weight DESC LIMIT 1"}],"db_id":"cars"}473{"prompt":"What is the Crankarm product's 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 Crankarm product's net profit?\n        \n        ### Context:\n        #### Query Context:\n        net profit = Subtract(LastReceiptCost, 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, 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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        Give the procedure description of Ms. Jacquelyn Shanahan on 2009\/8\/9.\n        \n        ### Context:\n        #### Query Context:\n        on 2009\/8\/9 refers to DATE = '2009-08-09';\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 FROM patients AS T1 INNER JOIN procedures AS T2 ON T1.patient = T2.PATIENT WHERE T1.prefix = 'Ms.' AND T1.first = 'Jacquelyn' AND T1.last = 'Shanahan' AND T2.DATE = '2009-08-09'"}],"db_id":"synthea"}475{"prompt":"What is the total occurrence of the biwords pairs with \"\u00e0bac\" as its first 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        What is the total occurrence of the biwords pairs with \"\u00e0bac\" as its first word?\n        \n        ### Context:\n        #### Query Context:\n        occurrence refers to occurrences; \u00e0bac refers to word = '\u00e0bac'; first word refers to w1st\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(T2.w1st) FROM words AS T1 INNER JOIN biwords AS T2 ON T1.wid = T2.w1st INNER JOIN words AS T3 ON T3.wid = T2.w2nd WHERE T1.word = '\u00e0bac'"}],"db_id":"language_corpus"}476{"prompt":"How many 'has' predicate classes does image ID 107 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 'has' predicate classes does image ID 107 have?\n        \n        ### Context:\n        #### Query Context:\n        has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107;\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(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 = 107 AND T2.PRED_CLASS = 'has'"}],"db_id":"image_and_language"}477{"prompt":"How many sanitarian employees in Chicago are from the zip code 60617?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 sanitarian employees in Chicago are from the zip code 60617?\n        \n        ### Context:\n        #### Query Context:\n        sanitarian refers to title = 'Sanitarian'; in Chicago refers to city = 'Chicago'; zip code 60617 refers to zip = 60617\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(employee_id) FROM employee WHERE zip = '60617'"}],"db_id":"food_inspection_2"}478{"prompt":"How many kinds of products are supplied by \"Karkki Oy\" 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 kinds of products are supplied by \"Karkki Oy\" company?\n        \n        ### Context:\n        #### Query Context:\n        supplied by 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 COUNT(T1.ProductID) FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T2.CompanyName = 'Karkki Oy'"}],"db_id":"retail_world"}479{"prompt":"Among the transactions, what percentage is done by using a visa card?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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, what percentage is done by using a visa card?\n        \n        ### Context:\n        #### Query Context:\n        visa card refers to CreditCardType = 'Visa'; percentage = MULTIPLY(DIVIDE(SUM(CreditCardType = 'Visa'), COUNT(TransactionID)), 1.0);\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 CreditCardType = 'Visa' THEN TransactionID ELSE NULL END) AS REAL) * 100 \/ COUNT(TransactionID) FROM `transaction`"}],"db_id":"beer_factory"}480{"prompt":"Provide the name of the author who is affiliated with the organization named 'ABB Electrical Machines'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the author who is affiliated with the organization named 'ABB Electrical Machines'.\n        \n        ### Context:\n        #### Query Context:\n        'ABB Electrical Machines' is an affiliation\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 Name FROM Author WHERE Affiliation = 'ABB Electrical Machines'"}],"db_id":"authors"}481{"prompt":"Among the store contact employees, how many of them have a Vista credit card?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 store contact employees, how many of them have a Vista credit card?\n        \n        ### Context:\n        #### Query Context:\n        store contact refers to PersonType = 'SC'; type of credit card refers to CardType; CardType = 'vista';\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'], 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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 percentage of AdventureWorks employees are men?\n        \n        ### Context:\n        #### Query Context:\n        male refers to Gender = 'M'; employee refers to PersonType = 'EM'; percentage = DIVIDE(COUNT(Gender = 'M'), COUNT(PersonType = 'MY'))*100%;\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', 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[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.Gender = 'M' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.BusinessentityID) FROM Person AS T1 INNER JOIN Employee AS T2 ON T1.BusinessentityID = T2.BusinessentityID WHERE T1.PersonType = 'EM'"}],"db_id":"works_cycles"}483{"prompt":"In the crew, who was born in 1962 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        In the crew, who was born in 1962 in California?\n        \n        ### Context:\n        #### Query Context:\n        born in 1962 refers to year(birthdate) = 1962; in California refers to birth_region = 'California'\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 name FROM Person WHERE SUBSTR(birthdate, 1, 4) = '1962' AND birth_region = 'California';"}],"db_id":"simpson_episodes"}484{"prompt":"Betweeen 2016 to 2019, which year recorded the highest number of people with mental illness?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Betweeen 2016 to 2019, which year recorded the highest number of people with mental illness?\n        \n        ### Context:\n        #### Query Context:\n        Between 2016 to 2019 refer to SurveyID 2016 BETWEEN 2019; People with mental illness refers to AnswerText = 'Yes' where questionid = 34\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.SurveyID FROM Answer AS T1 INNER JOIN Question AS T2 ON T1.QuestionID = T2.questionid WHERE T1.SurveyID BETWEEN 2016 AND 2019 AND T2.questionid = 34 AND T1.AnswerText LIKE 'Yes' GROUP BY T1.SurveyID ORDER BY COUNT(T1.UserID) DESC LIMIT 1"}],"db_id":"mental_health_survey"}485{"prompt":"Provide the shipping dates and products of the orders by Gene Hale.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 shipping dates and products of the orders by Gene Hale.\n        \n        ### Context:\n        #### Query Context:\n        Gene Hale' refers to \"Customer Name\"; shipping date refers to \"Ship Date\"; 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.`Ship Date`, 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` = 'Gene Hale'"}],"db_id":"superstore"}486{"prompt":"Among the products ordered in order no. 10248, which product has the biggest ratio of units on order to 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        Among the products ordered in order no. 10248, which product has the biggest ratio of units on order to units in stock?\n        \n        ### Context:\n        #### Query Context:\n        order no. refers to OrderID; biggest ratio = MAX(DIVIDE(UnitsOnOrder, UnitsInStock));\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 `Order Details` AS T2 ON T1.ProductID = T2.ProductID WHERE T2.OrderID = 10248 ORDER BY T1.UnitsOnOrder \/ T1.UnitsInStock DESC LIMIT 1"}],"db_id":"retail_world"}487{"prompt":"Which state was the tweet `tw-685681052912873473` from? Give the state 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 state was the tweet `tw-685681052912873473` from? Give the state code.\n        \n        ### Context:\n        #### Query Context:\n        tw-685681052912873473' 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.StateCode FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID WHERE T1.TweetID = 'tw-685681052912873473'"}],"db_id":"social_media"}488{"prompt":"What is the percentage of female clients in the Middle Atlantic?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 female clients in the Middle Atlantic?\n        \n        ### Context:\n        #### Query Context:\n        female refers to sex = 'Female'; the Middle Atlantic refers to division = 'Middle Atlantic'; percentage = divide(count(client_id where sex = 'Female' and division = 'Middle Atlantic') , count(client_id where division = 'Middle Atlantic')) * 100%\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 CAST(SUM(CASE WHEN T1.sex = 'Female' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.sex) FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.division = 'South Atlantic'"}],"db_id":"retail_complains"}489{"prompt":"List down the movie ID of movie with a budget of 15000000 and a rating between 7 to 8.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 movie ID of movie with a budget of 15000000 and a rating between 7 to 8.\n        \n        ### Context:\n        #### Query Context:\n        a budget of 15000000 refers to Budget = 15000000; rating between 7 to 8 refers to Rating BETWEEN 7 and 8\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 MovieID FROM movie WHERE Rating BETWEEN 7 AND 8 AND Budget = 15000000"}],"db_id":"movie"}490{"prompt":"In Albany Park, how many arrests were made in an apartment due to criminal sexual abuse?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Albany Park, how many arrests were made in an apartment due to criminal sexual abuse?\n        \n        ### Context:\n        #### Query Context:\n        Albany Park refers to district_name = 'Albany Park'; in an apartment refers to location_description = 'APARTMENT'; criminal sexual abuse refers to title = 'Criminal Sexual Abuse'\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 T3.title = 'Criminal Sexual Abuse' THEN 1 ELSE 0 END) FROM District AS T1 INNER JOIN Crime AS T2 ON T1.district_no = T2.district_no INNER JOIN FBI_Code AS T3 ON T2.fbi_code_no = T3.fbi_code_no WHERE T1.district_name = 'Albany Park' AND T2.arrest = 'TRUE' AND T2.location_description = 'APARTMENT'"}],"db_id":"chicago_crime"}491{"prompt":"What is the name of the restaurant that is located in El Dorado County, Lake Tahoe 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 is the name of the restaurant that is located in El Dorado County, Lake Tahoe region?\n        \n        ### Context:\n        #### Query Context:\n        restaurant name refers to label\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.label FROM geographic AS T1 INNER JOIN generalinfo AS T2 ON T1.city = T2.city WHERE T1.region = 'lake tahoe' AND T1.county = 'el dorado county'"}],"db_id":"restaurant"}492{"prompt":"What is the ID of the Publications Rank criteria?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Publications Rank criteria?\n        \n        ### Context:\n        #### Query Context:\n        Publications Rank criteria refers to criteria_name = 'Publications Rank';\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 id FROM ranking_criteria WHERE criteria_name = 'Publications Rank'"}],"db_id":"university"}493{"prompt":"For the team that Scotty Bowman coached in 1982, how many bench minor penalties did they have that 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        For the team that Scotty Bowman coached in 1982, how many bench minor penalties did they have that year?\n        \n        ### Context:\n        #### Query Context:\n        bench minor penalties refer to BenchMinor; Scotty Bowman is a coach; year = 1982;\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', 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'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 T2.BenchMinor 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 T3.firstName = 'Scotty' AND T3.lastName = 'Bowman' AND T1.year = 1982"}],"db_id":"hockey"}494{"prompt":"Among the customers having at least one shipment in 2017, how many of them have an annual revenue of over 30000000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 having at least one shipment in 2017, how many of them have an annual revenue of over 30000000?\n        \n        ### Context:\n        #### Query Context:\n        shipment in 2017 refers to Cast(ship_date AS DATE) = 2017; annual revenue of over 30000000 refers to annual_revenue > 30000000\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(COUNTCUSID) FROM ( SELECT COUNT(T1.cust_id) AS COUNTCUSID FROM customer AS T1 INNER JOIN shipment AS T2 ON T1.cust_id = T2.cust_id WHERE STRFTIME('%Y', T2.ship_date) = '2017' AND T1.annual_revenue > 30000000 GROUP BY T1.cust_id HAVING COUNT(T2.ship_id) >= 1 ) T3"}],"db_id":"shipping"}495{"prompt":"Which business ID have the shortest business operating 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        Which business ID have the shortest business operating hours?\n        \n        ### Context:\n        #### Query Context:\n        the shortest business operating hours refer to MIN(SUBTRACT(closing_time, opening_time));\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_Hours ORDER BY closing_time - opening_time LIMIT 1"}],"db_id":"public_review_platform"}496{"prompt":"What is the average salary of the employees who work as a Trainee?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 employees who work as a Trainee?\n        \n        ### Context:\n        #### Query Context:\n        average = DIVIDE( SUM(salary), COUNT(positiontitle) where positiontitle = 'Trainee'; Trainee is a position title\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)) AS avg FROM employee AS T1  INNER JOIN position AS T2  ON T1.positionID = T2.positionID WHERE T2.positiontitle = 'Trainee'"}],"db_id":"human_resources"}497{"prompt":"What is the genre of the movie whose villain is Commander Rourke?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 of the movie whose villain is Commander Rourke?\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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 T2.genre FROM characters AS T1 INNER JOIN movies_total_gross AS T2 ON T2.movie_title = T1.movie_title WHERE T1.villian = 'Commander Rourke'"}],"db_id":"disney"}498{"prompt":"Give the review of the restaurant at 430, Broadway.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 review of the restaurant at 430, Broadway.\n        \n        ### Context:\n        #### Query Context:\n        430 Broadway refers to street_num = 430 and street_name = 'Broadway'\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.review FROM generalinfo AS T1 INNER JOIN location AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T2.street_name = 'Broadway' AND T2.street_num = 430"}],"db_id":"restaurant"}499{"prompt":"Which summer Olympic have the highest and lowest number of participants?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 summer Olympic have the highest and lowest number of participants?\n        \n        ### Context:\n        #### Query Context:\n        the highest number of participants refers to MAX(COUNT(person_id)); the lowest number of participants refers to MIN(COUNT(person_id)); Which summer Olympic refers to games_name where 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 ( SELECT T1.games_name FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.season = 'Summer' GROUP BY T1.games_year ORDER BY COUNT(T2.person_id) DESC LIMIT 1 ) AS HIGHEST , ( SELECT T1.games_name FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id WHERE T1.season = 'Summer' GROUP BY T1.games_year ORDER BY COUNT(T2.person_id) LIMIT 1 ) AS LOWEST"}],"db_id":"olympics"}500{"prompt":"Among the wikipedia pages on Catalan with more than 300 different words, how many of them have a revision ID of over 28330000?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 wikipedia pages on Catalan with more than 300 different words, how many of them have a revision ID of over 28330000?\n        \n        ### Context:\n        #### Query Context:\n        lid = 1 means it's Catalan language; more than 300 different words refers to words > 300; revision ID of over 28330000 refers to revision > 28330000\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(lid) FROM pages WHERE lid = 1 AND words > 300 AND revision > 28330000"}],"db_id":"language_corpus"}501{"prompt":"Indicate the number of authors affiliated with the organization named 'Arizona State University'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of authors affiliated with the organization named 'Arizona State University'.\n        \n        ### Context:\n        #### Query Context:\n        'Arizona State University' is an affiliation\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(Name) FROM Author WHERE Affiliation = 'Arizona State University'"}],"db_id":"authors"}502{"prompt":"Give the FBI code for the crime described by \"The killing of one human being by another.\"","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 FBI code for the crime described by \"The killing of one human being by another.\"\n        \n        ### Context:\n        #### Query Context:\n        \"The killing of one human being by another\" is the description; FBI code refers to fbi_code_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 fbi_code_no FROM FBI_Code WHERE description = 'The killing of one human being by another.'"}],"db_id":"chicago_crime"}503{"prompt":"Describe name, birth country, role in episode and age in 2022 of the oldest crew member..","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 name, birth country, role in episode and age in 2022 of the oldest crew member..\n        \n        ### Context:\n        #### Query Context:\n        age in 2022 refers to SUBTRACT(2022, substr(birthdate, 0, 5)); oldest refers to MIN(birthdate)\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.name, T1.birth_place, T2.role, 2022 - CAST(SUBSTR(T1.birthdate, 1, 4) AS int) AS age FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.name = T2.person WHERE T1.birthdate IS NOT NULL ORDER BY T1.birthdate LIMIT 1;"}],"db_id":"simpson_episodes"}504{"prompt":"Please list the top ten teams with the highest scores 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        Please list the top ten teams with the highest scores in 2000.\n        \n        ### Context:\n        #### Query Context:\n        in 2000 refers to year = 2000; team with highest score refers to Max(o_fgm)\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 tmID FROM players_teams WHERE year = 2000 GROUP BY tmID ORDER BY SUM(PostPoints) DESC LIMIT 10"}],"db_id":"professional_basketball"}505{"prompt":"Among the cities with alias St Thomas, provide the type of postal point for each 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 cities with alias St Thomas, provide the type of postal point for each city.\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 DISTINCT T2.type FROM alias AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.alias = 'St Thomas'"}],"db_id":"address"}506{"prompt":"Please list the full names and titles of all 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        Please list the full names and titles of all employees.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to LastName, FirstName\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"}],"db_id":"retail_world"}507{"prompt":"List the paper title and journal ID which were published under the conference name of \"International Symposium of Robotics Research\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 paper title and journal ID which were published under the conference name of \"International Symposium of Robotics Research\".\n        \n        ### Context:\n        #### Query Context:\n        \"International Symposium of Robotics Research\" is the FullName of conference;\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, T2.JournalId FROM Conference AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.ConferenceId WHERE T1.FullName = 'International Symposium of Robotics Research' AND T2.Year = 2003"}],"db_id":"authors"}508{"prompt":"In which county can you find the city with the highest number of 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        In which county can you find the city with the highest number of females?\n        \n        ### Context:\n        #### Query Context:\n        highest number of females refers to Max(female_population)\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 T4.county FROM zip_data AS T3 INNER JOIN country AS T4 ON T3.zip_code = T4.zip_code GROUP BY T4.county ORDER BY T3.female_population DESC LIMIT 1"}],"db_id":"address"}509{"prompt":"When was the latest date the items of order no.1 were shipped?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 was the latest date the items of order no.1 were shipped?\n        \n        ### Context:\n        #### Query Context:\n        order no.1 refers to l_orderkey = 1; the latest date shipped refers to MAX(l_shipdate);\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 MAX(l_shipdate) FROM lineitem WHERE l_orderkey = 1"}],"db_id":"retails"}510{"prompt":"List all the coatches of the Oklahoma City Thunder","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 coatches of the Oklahoma City Thunder\n        \n        ### Context:\n        #### Query Context:\n        coach refers to coachID; Oklahoma City Thunder refers to name = 'Oklahoma City Thunder'\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 coachID FROM coaches AS T1 INNER JOIN teams AS T2 ON T1.tmID = T2.tmID WHERE name = 'Oklahoma City Thunder'"}],"db_id":"professional_basketball"}511{"prompt":"How many districts are in Idaho?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 districts are in Idaho?\n        \n        ### Context:\n        #### Query Context:\n        Idaho refers to state = '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 COUNT(district) FROM `current-terms` WHERE state = 'ID'"}],"db_id":"legislator"}512{"prompt":"Which of the films released in 2006 was the most popular among Mubi 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 of the films released in 2006 was the most popular among Mubi users?\n        \n        ### Context:\n        #### Query Context:\n        released in 2006 refers to movie_release_year = 2006; most popular refers to Max(movie_popularity); film refers to movie;\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 FROM movies WHERE movie_release_year = 2006 ORDER BY movie_popularity DESC LIMIT 1"}],"db_id":"movie_platform"}513{"prompt":"What is the average age of clients in South Atlantic?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 clients in South Atlantic?\n        \n        ### Context:\n        #### Query Context:\n        in South Atlantic refers to division = 'South Atlantic'; average age refers to avg(age)\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 AVG(T1.age) FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.division = 'South Atlantic'"}],"db_id":"retail_complains"}514{"prompt":"Is word id \"88\" the word id for title \"Animals\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 word id \"88\" the word id for title \"Animals\"?\n        \n        ### Context:\n        #### Query Context:\n        word id \"88\" refers to wid = 88\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 CASE WHEN COUNT(T1.pid) > 0 THEN 'YES' ELSE 'NO' END AS YORN FROM pages AS T1 INNER JOIN pages_words AS T2 ON T1.pid = T2.pid WHERE T2.wid = 88 AND T1.title = 'Animals'"}],"db_id":"language_corpus"}515{"prompt":"State the male population for all zip code which were under the Berlin, NH CBSA.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 male population for all zip code which were under the Berlin, NH CBSA.\n        \n        ### Context:\n        #### Query Context:\n        \"Berlin, NH\" is the CBSA_name\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.male_population FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Berlin, NH' GROUP BY T2.male_population"}],"db_id":"address"}516{"prompt":"How many matches are there in April, 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        How many matches are there in April, 2008?\n        \n        ### Context:\n        #### Query Context:\n        in April, 2008 refers to Match_date like '2008-04%'\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 '2008-04%'"}],"db_id":"soccer_2016"}517{"prompt":"Who are the coaches for team with winning rate of 80% 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        Who are the coaches for team with winning rate of 80% and above?\n        \n        ### Context:\n        #### Query Context:\n        winning rate of 80% and above refers to Divide (won, Sum(won, lost)) > 0.8; coaches refers to coachID\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 coachID FROM coaches GROUP BY tmID, coachID, won, lost HAVING CAST(won AS REAL) * 100 \/ (won + lost) > 80"}],"db_id":"professional_basketball"}518{"prompt":"How many orders were handled by Michael Suyama. State the order 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        How many orders were handled by Michael Suyama. State the order ID.\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(T2.OrderID) FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T1.FirstName = 'Michael' AND T1.LastName = 'Suyama'"}],"db_id":"retail_world"}519{"prompt":"Which countries in the upper middle income category still have unfinished external debt reporting? Please provide the country codes 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 countries in the upper middle income category still have unfinished external debt reporting? Please provide the country codes in your answer.\n        \n        ### Context:\n        #### Query Context:\n        in the upper middle income category refers to incomegroup = 'Upper middle income'; still have unfinished external debt reporting refers to ExternalDebtReportingStatus = 'Preliminary'\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 CountryCode FROM Country WHERE IncomeGroup = 'Upper middle income' AND ExternalDebtReportingStatus = 'Preliminary'"}],"db_id":"world_development_indicators"}520{"prompt":"How many orders were from Hanna Moos company in 1999?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 were from Hanna Moos company in 1999?\n        \n        ### Context:\n        #### Query Context:\n        \"Hanna Moos\" is the CompanyName; in 1999 refer to YEAR (OrderDate) = 1999\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.OrderID) FROM Customers AS T1 INNER JOIN Orders AS T2 ON T1.CustomerID = T2.CustomerID WHERE STRFTIME('%Y', T2.OrderDate) = '1999' AND T1.CompanyName = 'Hanna Moos'"}],"db_id":"retail_world"}521{"prompt":"Please list the titles of the films that are released in 2006 and have a rental rate of $2.99.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 films that are released in 2006 and have a rental rate of $2.99.\n        \n        ### Context:\n        #### Query Context:\n        released in 2006 refers to release_year = 2006; rental rate of $2.99 refers to rental_rate = 2.99\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 release_year = 2006 AND rental_rate = 2.99"}],"db_id":"movie_3"}522{"prompt":"Which two Asian countries share a border that is 1,782 kilometers long?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Asian countries share a border that is 1,782 kilometers long?\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 T4.Country1, T4.Country2 FROM continent AS T1 INNER JOIN encompasses AS T2 ON T1.Name = T2.Continent INNER JOIN country AS T3 ON T3.Code = T2.Country INNER JOIN borders AS T4 ON T4.Country1 = T3.Code WHERE T1.Name = 'Asia' AND T4.Length = 1782"}],"db_id":"mondial_geo"}523{"prompt":"Among the items shipped in 1994 via truck, how many items were returned?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 items shipped in 1994 via truck, how many items were returned?\n        \n        ### Context:\n        #### Query Context:\n        1994 refers to year(l_shipdate) = 1994; via truck refers to l_shipmode = 'TRUCK'; returned refers to l_returnflag = 'R'\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_orderkey) FROM lineitem WHERE STRFTIME('%Y', l_shipdate) = '1994' AND l_returnflag = 'R' AND l_shipmode = 'TRUCK'"}],"db_id":"retails"}524{"prompt":"Who are the voice actors in the movie that came out on 11\/24\/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        Who are the voice actors in the movie that came out on 11\/24\/2010?\n        \n        ### Context:\n        #### Query Context:\n        Came out on 11\/24\/2010 refers to release_date = 'Nov 24, 2010';\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 T2.`voice-actor` FROM movies_total_gross AS T1 INNER JOIN `voice-actors` AS T2 ON T1.movie_title = T2.movie WHERE T1.release_date = 'Nov 24, 2010'"}],"db_id":"disney"}525{"prompt":"On what date was the rented material for the movie BLOOD ARGONAUTS returned?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 date was the rented material for the movie BLOOD ARGONAUTS returned?\n        \n        ### Context:\n        #### Query Context:\n        'BLOOD ARGONAUTS' is a title of a film; date a movie was returned refers to return_date\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.rental_date 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 WHERE T3.title = 'BLOOD ARGONAUTS'"}],"db_id":"movie_3"}526{"prompt":"Which day in the month of November, 2014 have a foggy weather in the zip code 94301 and in total, how many bikes were borrowed by subscribers from all of the stations in the said day?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 day in the month of November, 2014 have a foggy weather in the zip code 94301 and in total, how many bikes were borrowed by subscribers from all of the stations in the said day?\n        \n        ### Context:\n        #### Query Context:\n        day in the month of November, 2014 refers to start_date between '11\/1\/2014' and '11\/30\/2014'; foggy weather refers to events = 'Fog'; subscriber refers to subscription_type; all of the stations bikes were borrowed from refer to start_station_name;\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, COUNT(T1.start_station_name) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code WHERE T2.date LIKE '11\/%\/2014%' AND T2.zip_code = 94301 AND T2.events = 'Fog' AND T1.subscription_type = 'Subscriber'"}],"db_id":"bike_share_1"}527{"prompt":"List the full name of all the 'Production Technician - WC50'","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the 'Production Technician - WC50'\n        \n        ### Context:\n        #### Query Context:\n        full name = FirstName+MiddleName+LastName; Production Technician - WC50 is a job title;\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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'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, 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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 average rental rate of sci-fi film titles?\n        \n        ### Context:\n        #### Query Context:\n        sci-fi film refers to category.name = 'Sci-Fi'; average rental rate = avg(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 AVG(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 T3.category_id = T2.category_id WHERE T3.`name` = 'Sci-Fi'"}],"db_id":"movie_3"}529{"prompt":"Indicate the number of installs and include the percentage of positive sentiments of FREEDOME VPN Unlimited anonymous Wifi Security.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of installs and include the percentage of positive sentiments of FREEDOME VPN Unlimited anonymous Wifi Security.\n        \n        ### Context:\n        #### Query Context:\n        FREEDOME VPN Unlimited anonymous Wifi Security is the App; 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 T1.Installs , CAST(SUM(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE 0 END) * 100 \/ SUM(CASE WHEN T2.Sentiment IS NOT NULL THEN 1.0 ELSE 0 END) AS REAL) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'FREEDOME VPN Unlimited anonymous Wifi Security'"}],"db_id":"app_store"}530{"prompt":"Among the German customers, how many of the them has credit limit of zero?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 German customers, how many of the them has credit limit of zero?\n        \n        ### Context:\n        #### Query Context:\n        German is a nationality of country = 'Germany'; CREDITLIMIT = 0\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(customerNumber) FROM customers WHERE creditLimit = 0 AND country = 'Germany'"}],"db_id":"car_retails"}531{"prompt":"List the name of employees in Japan office and who are they reporting 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 name of employees in Japan office and who are they reporting to.\n        \n        ### Context:\n        #### Query Context:\n        Japan is the name of the country; 'reportsTO' is the leader of the 'employeeNumber';\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 t2.firstName, t2.lastName, t2.reportsTo FROM offices AS t1 INNER JOIN employees AS t2 ON t1.officeCode = t2.officeCode WHERE t1.country = 'Japan'"}],"db_id":"car_retails"}532{"prompt":"What is the employee's last name at 7211 S Hermitage Ave, Chicago, IL?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 employee's last name at 7211 S Hermitage Ave, Chicago, IL?\n        \n        ### Context:\n        #### Query Context:\n        7211 S Hermitage Ave refers to address = '7211 S Hermitage Ave'; Chicago refers to city = 'Chicago'; IL refers to state = 'IL'\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 last_name FROM employee WHERE address = '7211 S Hermitage Ave' AND city = 'Chicago' AND state = 'IL'"}],"db_id":"food_inspection_2"}533{"prompt":"What is the criteria name of the university ID 32 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        What is the criteria name of the university ID 32 in 2015?\n        \n        ### Context:\n        #### Query Context:\n        in 2015 refers to year = 2015\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 T1.criteria_name FROM ranking_criteria AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.ranking_criteria_id WHERE T2.university_id = 32 AND T2.year = 2015"}],"db_id":"university"}534{"prompt":"Find out the difference between the number of publishers who released the games on the PS3 and X360.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the difference between the number of publishers who released the games on the PS3 and X360.\n        \n        ### Context:\n        #### Query Context:\n        difference = SUBTRACT(SUM(platform_name = 'PS3'), SUM(platform_name = 'X360')); PS3 refers to platform_name = 'PS3'; X360 refers to platform_name = 'X360';\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 T2.platform_name = 'PS3' THEN T1.game_publisher_id ELSE NULL END) - COUNT(CASE WHEN T2.platform_name = 'X360' THEN T1.game_publisher_id ELSE NULL END) FROM game_platform AS T1 INNER JOIN platform AS T2 ON T1.platform_id = T2.id"}],"db_id":"video_games"}535{"prompt":"List the scenes and descriptions in Act 1 of \" Pericles, Prince of Tyre\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 scenes and descriptions in Act 1 of \" Pericles, Prince of Tyre\".\n        \n        ### Context:\n        #### Query Context:\n        \" Pericles, Prince of Tyre\" refers to LongTitle = 'Pericles, Prince of Tyre'\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.Scene, T2.Description FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.LongTitle = 'Pericles, Prince of Tyre' AND T2.Act = 1"}],"db_id":"shakespeare"}536{"prompt":"List out all the coach ID who have served more than 2 different teams.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the coach ID who have served more than 2 different teams.\n        \n        ### Context:\n        #### Query Context:\n        more than 2 different teams refers to count(tmID) > 2\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 coachID FROM coaches GROUP BY coachID HAVING COUNT(DISTINCT tmID) > 2"}],"db_id":"professional_basketball"}537{"prompt":"How many dishes are there in total in the menus with the name \"Waldorf Astoria\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are there in total in the menus with the name \"Waldorf Astoria\"?\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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 T3.name = 'Waldorf Astoria' THEN 1 ELSE 0 END) FROM MenuItem AS T1 INNER JOIN MenuPage AS T2 ON T1.menu_page_id = T2.id INNER JOIN Menu AS T3 ON T2.menu_id = T3.id"}],"db_id":"menu"}538{"prompt":"Calculate the total price of books ordered by customer named Lucas Wyldbore.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 price of books ordered by customer named Lucas Wyldbore.\n        \n        ### Context:\n        #### Query Context:\n        total price refers to Sum(price); full name is composed of 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 SUM(T1.price) 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"}539{"prompt":"What is the long title of the Shakespeare's work with Act 4 Scene 5 described as \"Mytilene. A street before the brothel.\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Shakespeare's work with Act 4 Scene 5 described as \"Mytilene. A street before the brothel.\"?\n        \n        ### Context:\n        #### Query Context:\n        described as \"Mytilene. A street before the brothel.\" refers to chapters.Description = 'Mytilene. A street before the brothel.'\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.LongTitle FROM works AS T1 RIGHT JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T2.Description = 'Mytilene. A street before the brothel.'"}],"db_id":"shakespeare"}540{"prompt":"What number of words are there on revision page 27457362?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of words are there on revision page 27457362?\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 words FROM pages WHERE revision = 27457362"}],"db_id":"language_corpus"}541{"prompt":"Among all students, calculate the percentage of 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        Among all students, calculate the percentage of disabled students.\n        \n        ### Context:\n        #### Query Context:\n        percentage = CONCAT(DIVIDE(MULTIPLY(COUNT(disabled.name), 100), (COUNT(person.name that is not in 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 CAST(COUNT(T2.name) AS REAL) * 100 \/ COUNT(T1.name) FROM person AS T1 LEFT JOIN disabled AS T2 ON T2.name = T1.name"}],"db_id":"student_loan"}542{"prompt":"How many female Twitter users are there from Wisconsin?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Twitter users are there from Wisconsin?\n        \n        ### Context:\n        #### Query Context:\n        female users refers to Gender = 'Female'; 'Wisconsin' is the State\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(T1.Likes) FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID INNER JOIN user AS T3 ON T3.UserID = T1.UserID WHERE T2.State = 'Wisconsin' AND T3.Gender = 'Female'"}],"db_id":"social_media"}543{"prompt":"List all podcast with its itunes url for all title containing the word '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        List all podcast with its itunes url for all title containing the word 'Dream'.\n        \n        ### Context:\n        #### Query Context:\n        containing the word 'Dream' refers to title LIKE '%Dream%'\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 itunes_url FROM podcasts WHERE title LIKE '%Dream%' GROUP BY itunes_url"}],"db_id":"music_platform_2"}544{"prompt":"Please list the page IDs of all the Wikipedia pages that have the word \"nombre\" appeared on 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        Please list the page IDs of all the Wikipedia pages that have the word \"nombre\" appeared on it.\n        \n        ### Context:\n        #### Query Context:\n        nombre refers to word = 'nombre'; page IDs refers to pid\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.pid FROM words AS T1 INNER JOIN pages_words AS T2 ON T1.wid = T2.wid WHERE T1.word = 'nombre'"}],"db_id":"language_corpus"}545{"prompt":"What is the ratio for subscriber to customer given that the starting and the ending stations is 2nd at South Park?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 ratio for subscriber to customer given that the starting and the ending stations is 2nd at South Park?\n        \n        ### Context:\n        #### Query Context:\n        subscriber refers to subscription_type = 'Subscriber'; customer refers to subscription_type = 'customer';starting station refers to start_station_name; ending station refers to end_statio_name; start_station_name = '2nd at South Park' AND end_station_name = '2nd at South Park'\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(IIF(subscription_type = 'Subscriber', 1, 0)) AS REAL) \/ SUM(IIF(subscription_type = 'Customer', 1, 0)) FROM trip WHERE start_station_name = '2nd at South Park' AND end_station_name = '2nd at South Park'"}],"db_id":"bike_share_1"}546{"prompt":"How many users have no followers 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 users have no followers in 2014?\n        \n        ### Context:\n        #### Query Context:\n        in 2004 refers to user_yelping_since_year = 2004; no follower refers to user_fans = 'None'\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(user_id) FROM Users WHERE user_yelping_since_year = 2004 AND user_fans LIKE 'None'"}],"db_id":"public_review_platform"}547{"prompt":"Provide the itunes id and url for podcast titled 'Brown Suga Diaries'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 itunes id and url for podcast titled 'Brown Suga Diaries'.\n        \n        ### Context:\n        #### Query Context:\n        url refers to itunes_url; 'Brown Suga Diaries' 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 itunes_id, itunes_url FROM podcasts WHERE title = 'Brown Suga Diaries'"}],"db_id":"music_platform_2"}548{"prompt":"What is the full name of the team that has the most players from UCLA?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 has the most players from UCLA?\n        \n        ### Context:\n        #### Query Context:\n        \"UCLA\" is the college; team with most players refers to tmID where Max(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 T3.name FROM players AS T1 INNER JOIN players_teams AS T2 ON T1.playerID = T2.playerID INNER JOIN teams AS T3 ON T3.tmID = T2.tmID WHERE T1.college = 'UCLA' GROUP BY T3.name ORDER BY COUNT(DISTINCT T1.playerID) DESC LIMIT 1"}],"db_id":"professional_basketball"}549{"prompt":"What is the summary of the episode in which Emily Blunt is featured 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 summary of the episode in which Emily Blunt is featured in?\n        \n        ### Context:\n        #### Query Context:\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 T1.summary FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id WHERE T2.person = 'Emily Blunt';"}],"db_id":"simpson_episodes"}550{"prompt":"Why did Elly Koss need to take Acetaminophen?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Why did Elly Koss need to take Acetaminophen?\n        \n        ### Context:\n        #### Query Context:\n        why need to take Acetaminophen refers to REASONDESCRIPTION\u00a0 where DESCRIPTION like 'Acetaminophen%' from medications;\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 medications AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Elly' AND T1.last = 'Koss' AND T2.description LIKE 'Acetaminophen%'"}],"db_id":"synthea"}551{"prompt":"Among the orders from 2016 in the Central region, what is the product with the lowest 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        Among the orders from 2016 in the Central region, what is the product with the lowest profit?\n        \n        ### Context:\n        #### Query Context:\n        orders from 2016 refers to \"Order Date\" = '2016%'; product refers to \"Product Name\"; lowest profit refers to min(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 T2.`Product Name` FROM central_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T2.Region = 'Central' AND STRFTIME('%Y', T1.`Order Date`) = '2016' ORDER BY T1.Profit ASC LIMIT 1"}],"db_id":"superstore"}552{"prompt":"Please provide the path of solution of method whose tokenized name is html parser feed.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 path of solution of method whose tokenized name is html parser feed.\n        \n        ### Context:\n        #### Query Context:\n        path of solution refers to Path; method refers to Name; tokenized name refers to NameTokenized; Name = ''html parser feed';\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.NameTokenized = 'html parser feed'"}],"db_id":"codebase_comments"}553{"prompt":"Calculate the percentage of 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        Calculate the percentage of suppliers in Germany.\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(s_suppkey where n_name = 'GERMANY'), COUNT(s_suppkey)) 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.n_name = 'GERMANY', 1, 0)) AS REAL) * 100 \/ COUNT(T1.s_suppkey) FROM supplier AS T1 INNER JOIN nation AS T2 ON T1.s_nationkey = T2.n_nationkey WHERE T1.s_acctbal < 0"}],"db_id":"retails"}554{"prompt":"What is the difference in the average GPA of students who took the hardest and easiest courses?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the average GPA of students who took the hardest and easiest courses?\n        \n        ### Context:\n        #### Query Context:\n        difference in the average gpa = SUBTRACT(AVG(gpa WHERE MAX(diff)), AVG(gpa where min(diff))); difficulty of the course refers to diff; hardest course refers to MAX(diff); easiest course refers to MIN(diff);\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 AVG(T1.gpa) 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.diff IN (2, 1) GROUP BY T3.diff"}],"db_id":"cs_semester"}555{"prompt":"Please list the motif of the genes that are located in the cytoplasm and have 7 chromosomes.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 motif of the genes that are located in the cytoplasm and have 7 chromosomes.\n        \n        ### Context:\n        #### Query Context:\n        \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 T2.GeneID1, T2.GeneID2 FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Localization = 'cytoplasm' AND T1.Chromosome = 7"}],"db_id":"genes"}556{"prompt":"What is the total number of suppliers from 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        What is the total number of suppliers from Germany?\n        \n        ### Context:\n        #### Query Context:\n        suppliers refer to s_suppkey; Germany is the name of the nation which 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 COUNT(T1.s_suppkey) FROM supplier AS T1 INNER JOIN nation AS T2 ON T1.s_nationkey = T2.n_nationkey WHERE T2.n_name = 'GERMANY'"}],"db_id":"retails"}557{"prompt":"Among the independent countries whose type of government is republic, what is the biggest number of deserts they 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        Among the independent countries whose type of government is republic, what is the biggest number of deserts they 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 COUNT(T3.Desert) FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country INNER JOIN geo_desert AS T3 ON T3.Country = T2.Country WHERE T2.Government = 'republic'"}],"db_id":"mondial_geo"}558{"prompt":"Who has got the most number of \"funny\" type of compliments? Give the user 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        Who has got the most number of \"funny\" type of compliments? Give the user ID.\n        \n        ### Context:\n        #### Query Context:\n        type of compliments refers to compliment_type; most number of funny type of compliments refers to MAX(COUNT(number of compliments = 'high' WHERE 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 user_id FROM Users_Compliments WHERE compliment_id IN ( SELECT compliment_id FROM Compliments WHERE compliment_type LIKE 'funny' )"}],"db_id":"public_review_platform"}559{"prompt":"Name the movie with the most 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        Name the movie with the most ratings.\n        \n        ### Context:\n        #### Query Context:\n        movie with the most rating refers to MAX(SUM(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 movie_title FROM movies GROUP BY movie_title ORDER BY COUNT(movie_title) DESC LIMIT 1"}],"db_id":"movie_platform"}560{"prompt":"How many products sold by Adam Hernandez?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 products sold by Adam Hernandez?\n        \n        ### Context:\n        #### Query Context:\n        products sold by Adam Hernandez refer to SUM(Order Quantity where Sales Team = 'Adam Hernandez');\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 T2.`Sales Team` = 'Adam Hernandez' THEN 1 ELSE 0 END) FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID"}],"db_id":"regional_sales"}561{"prompt":"How many unique directors with an average earnings of 2 and a quality of 3 have not made comedy films? 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        How many unique directors with an average earnings of 2 and a quality of 3 have not made comedy films? List them.\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 DISTINCT T1.directorid FROM directors AS T1 INNER JOIN movies2directors AS T2 ON T1.directorid = T2.directorid WHERE T1.d_quality = 3 AND T1.avg_revenue = 2 AND T2.genre != 'Comedy'"}],"db_id":"movielens"}562{"prompt":"Among the violations in 2016, how many of them have unscheduled 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        Among the violations in 2016, how many of them have unscheduled inspections?\n        \n        ### Context:\n        #### Query Context:\n        unscheduled inspections refer to type = 'Routine - Unschedule'; year(date) = 2016;\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(T2.business_id) FROM violations AS T1 INNER JOIN inspections AS T2 ON T1.business_id = T2.business_id WHERE STRFTIME('%Y', T1.`date`) = '2016' AND T2.type = 'Routine - Unscheduled'"}],"db_id":"food_inspection"}563{"prompt":"List the names and prices of the cars with model 82 and mileage per gallon of greater than 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        List the names and prices of the cars with model 82 and mileage per gallon of greater than 30.\n        \n        ### Context:\n        #### Query Context:\n        car's name refers to car_name; model 82 refers to model = 82; mileage per gallon of greater than 30 refers to mpg > 30\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.car_name, T1.price FROM price AS T1 INNER JOIN data AS T2 ON T1.ID = T2.ID WHERE T2.model = 82 AND T2.mpg > 30"}],"db_id":"cars"}564{"prompt":"What are the apps with only 5,000+ installs?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 apps with only 5,000+ installs?\n        \n        ### Context:\n        #### Query Context:\n        Installs = '5,000+';\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 App FROM playstore WHERE Installs = '5,000+'"}],"db_id":"app_store"}565{"prompt":"How much does David Bornhammar weigh 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 much does David Bornhammar weigh in kilograms?\n        \n        ### Context:\n        #### Query Context:\n        weigh 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 = 'David Bornhammar'"}],"db_id":"ice_hockey_draft"}566{"prompt":"On which years did Aruba got a footnote on the series code AG.LND.FRST.K2?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 years did Aruba got a footnote on the series code AG.LND.FRST.K2?\n        \n        ### Context:\n        #### Query Context:\n        Aruba is the name of country where ShortName = 'Aruba'\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.Year FROM Country AS T1 INNER JOIN FootNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T1.ShortName = 'Aruba' AND T2.Seriescode = 'AG.LND.FRST.K2'"}],"db_id":"world_development_indicators"}567{"prompt":"Tell the number of reviews given by James House.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 reviews given by James House.\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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(T2.CustomerID) FROM customers AS T1 INNER JOIN rootbeerreview AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.First = 'James' AND T1.Last = 'House'"}],"db_id":"beer_factory"}568{"prompt":"Indicate the title of all the films rated as 'Adults Only'.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 title of all the films rated as 'Adults Only'.\n        \n        ### Context:\n        #### Query Context:\n        'Adults Only' refers to rating = 'NC-17'\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 rating = 'NC-17'"}],"db_id":"movie_3"}569{"prompt":"In August of 1996, how many orders were placed by the customer with the highest amount of 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        In August of 1996, how many orders were placed by the customer with the highest amount of orders?\n        \n        ### Context:\n        #### Query Context:\n        August of 1996 refers to OrderDate = '1996-8'; highest amount of orders refers to MAX(COUNT(OrderID))\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(OrderID) FROM Orders WHERE OrderDate LIKE '1996-08%' GROUP BY CustomerID ORDER BY COUNT(OrderID) DESC LIMIT 1"}],"db_id":"retail_world"}570{"prompt":"What are the words that were paired with \"John\", list down 10 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 are the words that were paired with \"John\", list down 10 of them.\n        \n        ### Context:\n        #### Query Context:\n        pair with \"John\" refers to w1st.word = \"John\" or w2nd.word = \"John\"\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 w2nd FROM biwords WHERE w1st = ( SELECT wid FROM words WHERE word = 'john' ) LIMIT 10"}],"db_id":"language_corpus"}571{"prompt":"What is the full name of the actor who has the highest number of restricted films?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 actor who has the highest number of restricted films?\n        \n        ### Context:\n        #### Query Context:\n        restricted refers to rating = 'R'; highest number of film refers to Max(Count(film_id)); 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 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 AS T3 ON T2.film_id = T3.film_id WHERE T3.rating = 'R' GROUP BY T1.first_name, T1.last_name ) AS T ORDER BY T.num DESC LIMIT 1"}],"db_id":"movie_3"}572{"prompt":"What cities are located in Northern 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        What cities are located in Northern California?\n        \n        ### Context:\n        #### Query Context:\n        Northern California refers to region = 'northern california'\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 city FROM geographic WHERE region = 'northern california'"}],"db_id":"restaurant"}573{"prompt":"For the event which happened at 23:55:16 on 2016\/5\/7, in the location coordinate(113, 28), on what device did it happen? Give the name of the device model.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 event which happened at 23:55:16 on 2016\/5\/7, in the location coordinate(113, 28), on what device did it happen? Give the name of the device model.\n        \n        ### Context:\n        #### Query Context:\n        at 23:55:16 on 2016\/5\/7 refers to timestamp = '2016\/5\/7 23:55:16'; location coordinate (113, 28) refers to longitude = '113' AND latitude = '28';\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_model FROM phone_brand_device_model2 AS T1 INNER JOIN events AS T2 ON T1.device_id = T2.event_id WHERE T2.longitude = '113' AND T2.latitude = '28' AND T2.timestamp = '2016-05-07 23:55:16'"}],"db_id":"talkingdata"}574{"prompt":"On what dates did the billable period begin for patients with the last name Dickinson?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 dates did the billable period begin for patients with the last name Dickinson?\n        \n        ### Context:\n        #### Query Context:\n        dates when the billable period begin refers to BILLABLEPERIOD;\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.BILLABLEPERIOD FROM patients AS T1 INNER JOIN claims AS T2 ON T1.patient = T2.PATIENT WHERE T1.last = 'Dickinson'"}],"db_id":"synthea"}575{"prompt":"Among the suppliers from Middle East region, how many suppliers were 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 from Middle East region, how many suppliers were in debt?\n        \n        ### Context:\n        #### Query Context:\n        suppliers from Middle East region refer to s_name where r_name = 'MIDDLE EAST'; 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 region AS T1 INNER JOIN nation AS T2 ON T1.r_regionkey = T2.n_regionkey INNER JOIN supplier AS T3 ON T2.n_nationkey = T3.s_nationkey WHERE T3.s_acctbal < 0 AND T1.r_name = 'MIDDLE EAST'"}],"db_id":"retails"}576{"prompt":"Calculate the total production for each product which were supplied from Japan","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 production for each product which were supplied from Japan\n        \n        ### Context:\n        #### Query Context:\n        from Japan refers to Country = 'Japan'; total production refers to ADD(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(T1.UnitsInStock + T1.UnitsOnOrder) FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T2.Country = 'Japan'"}],"db_id":"retail_world"}577{"prompt":"What is the average number of tweets posted by the users in a city in Argentina?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 tweets posted by the users in a city in Argentina?\n        \n        ### Context:\n        #### Query Context:\n        \"Argentina\" is the Country; average number of tweets in a city = Divide (Count(TweetID where Country = 'Argentina'), Count (City))\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 = 'Buenos Aires' THEN 1.0 ELSE 0 END) \/ COUNT(T1.TweetID) AS avg FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID WHERE T2.Country = 'Argentina'"}],"db_id":"social_media"}578{"prompt":"Among the pairs of genes that are not from the class of motorproteins, how many of them are negatively correlated?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 pairs of genes that are not from the class of motorproteins, how many of them are negatively correlated?\n        \n        ### Context:\n        #### Query Context:\n        If Expression_Corr < 0, it means the negatively correlated\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 COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 AND T1.Class = 'Motorproteins'"}],"db_id":"genes"}579{"prompt":"How many cars worth greater than 40000 were 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        How many cars worth greater than 40000 were from the USA?\n        \n        ### Context:\n        #### Query Context:\n        worth greater than 40000 refers to price > 40000; from the USA refers to country = 'USA'\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 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 = 'USA' AND T1.price > 40000"}],"db_id":"cars"}580{"prompt":"What is the highest amount of difference between the ordered quantity and actual quantity received in a single purchase order and to which vendor was the purchase order made?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 amount of difference between the ordered quantity and actual quantity received in a single purchase order and to which vendor was the purchase order made?\n        \n        ### Context:\n        #### Query Context:\n        highest amount of difference between the ordered quantity and actual quantity received in a single purchase order refers to MAX(SUBTRACT(OrderQty, ReceivedQty));\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, 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'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, 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minimum weight of all-star players coming from UCLA 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        What is the minimum weight of all-star players coming from UCLA college?\n        \n        ### Context:\n        #### Query Context:\n        minimum weight refers to Min(weight)\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, 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'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 MIN(T1.weight) FROM players AS T1 INNER JOIN player_allstar AS T2 ON T1.playerID = T2.playerID WHERE T1.college = 'UCLA'"}],"db_id":"professional_basketball"}582{"prompt":"What are the URL to the list page on Mubi of the lists with followers between 1-2 and whose last update timestamp was on 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 are the URL to the list page on Mubi of the lists with followers between 1-2 and whose last update timestamp was on 2012?\n        \n        ### Context:\n        #### Query Context:\n        URL to the list page on Mubi refers to list_url; list_followers = 1 OR list_followers = 2; last update timestamp was on 2012 refers to list_update_timestamp_utc BETWEEN '2012-1-1' AND '2012-12-31';\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_url FROM lists WHERE list_update_timestamp_utc LIKE '2012%' AND list_followers BETWEEN 1 AND 2 ORDER BY list_update_timestamp_utc DESC LIMIT 1"}],"db_id":"movie_platform"}583{"prompt":"What is the general description for case number JB106010?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 general description for case number JB106010?\n        \n        ### Context:\n        #### Query Context:\n        general description refers to primary_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 T1.primary_description FROM IUCR AS T1 INNER JOIN Crime AS T2 ON T1.iucr_no = T2.iucr_no WHERE T2.case_number = 'JB106010'"}],"db_id":"chicago_crime"}584{"prompt":"State the number of tweets from Michigan on Thursdays.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 tweets from Michigan on Thursdays.\n        \n        ### Context:\n        #### Query Context:\n        \"Michigan\" is the State; 'Thursday' is the Weekday; number of tweets refers to 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 COUNT(T1.TweetID) FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID WHERE T1.Weekday = 'Thursday' AND T2.State = 'Michigan'"}],"db_id":"social_media"}585{"prompt":"Provide the population of the city of the 'World Tourism Organization' headquarter.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 population of the city of the 'World Tourism Organization' headquarter.\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.Population FROM organization AS T1 INNER JOIN city AS T2 ON T1.City = T2.Name WHERE T1.Name = 'World Tourism Organization'"}],"db_id":"mondial_geo"}586{"prompt":"How many grocery stores paid $250 fine upon their 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        How many grocery stores paid $250 fine upon their inspection?\n        \n        ### Context:\n        #### Query Context:\n        grocery store refers to facility_type = 'Grocery Store'; $250 fine refers to fine = 250\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 INNER JOIN violation AS T3 ON T2.inspection_id = T3.inspection_id WHERE T1.facility_type = 'Grocery Store' AND T3.fine = 250"}],"db_id":"food_inspection_2"}587{"prompt":"List the full name of all employees who work in the 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        List the full name of all employees who work in the Northern region.\n        \n        ### Context:\n        #### Query Context:\n        full names = FirstName, LastName; 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.FirstName, T1.LastName FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID INNER JOIN Region AS T4 ON T3.RegionID = T4.RegionID WHERE T4.RegionDescription = 'Northern'"}],"db_id":"retail_world"}588{"prompt":"How many non-essential genes are located in the nucleus?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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-essential genes are located in the nucleus?\n        \n        ### Context:\n        #### Query Context:\n        \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 COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID WHERE T2.Localization = 'nucleus' AND T1.Essential = 'Non-Essential'"}],"db_id":"genes"}589{"prompt":"What party does the area with the zip code 91701 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 party does the area with the zip code 91701 belong to?\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.party FROM congress AS T1 INNER JOIN state AS T2 ON T1.abbreviation = T2.abbreviation INNER JOIN zip_data AS T3 ON T2.abbreviation = T3.state WHERE T3.zip_code = 91701 GROUP BY T1.party"}],"db_id":"address"}590{"prompt":"What are the issues of the complains of male clients and products from age 25 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 are the issues of the complains of male clients and products from age 25 and below?\n        \n        ### Context:\n        #### Query Context:\n        male refers to sex = 'Male'; age 25 and below refers to age < 25\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.Issue FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.sex = 'Male' AND T1.age < 25"}],"db_id":"retail_complains"}591{"prompt":"List all of the conferences where a paper was published 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        List all of the conferences where a paper was published in 2008.\n        \n        ### Context:\n        #### Query Context:\n        Published in 2008 refer to Year = 2008\n\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 DISTINCT T2.FullName FROM Paper AS T1 INNER JOIN Conference AS T2 ON T1.ConferenceId = T2.Id WHERE T1.Year = 2008"}],"db_id":"authors"}592{"prompt":"Of all the lakes in Bolivia, which is the deepest?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 lakes in Bolivia, which is the deepest?\n        \n        ### Context:\n        #### Query Context:\n        Bolivia is the 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 lake AS T1 INNER JOIN geo_lake AS T2 ON T1.Name = T2.Lake INNER JOIN province AS T3 ON T3.Name = T2.Province INNER JOIN country AS T4 ON T4.Code = T3.Country WHERE T4.Name = 'Bolivia' ORDER BY T1.Depth DESC LIMIT 1"}],"db_id":"mondial_geo"}593{"prompt":"Of all the countries of the Hindu religion, which has the lowest ratio of people per square meter of surface?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 countries of the Hindu religion, which has the lowest ratio of people per square meter of surface?\n        \n        ### Context:\n        #### Query Context:\n        ratio of people per square meter of surface = 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 T1.Name FROM country AS T1 INNER JOIN religion AS T2 ON T1.Code = T2.Country WHERE T2.Name = 'Hindu' ORDER BY T1.Population \/ T1.Area ASC LIMIT 1"}],"db_id":"mondial_geo"}594{"prompt":"Which character has the longest screen time in the movie Batman?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 character has the longest screen time in the movie Batman?\n        \n        ### Context:\n        #### Query Context:\n        longest screen time refers to max(screentime); movie Batman refers to title = 'Batman'\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 = 'Batman' ORDER BY T2.screentime DESC LIMIT 1"}],"db_id":"movie"}595{"prompt":"If Jose Rodriguez tried his best, how many percentage can his salary raise without changing his 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        If Jose Rodriguez tried his best, how many percentage can his salary raise without changing his position?\n        \n        ### Context:\n        #### Query Context:\n        Jose Rodriguez is the full name of an employee; full name = firstname, lastname; calculation = DIVIDE(SUBTRACT(maxsalary, salary), salary) * 100\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 100 * (CAST(REPLACE(SUBSTR(T2.maxsalary, 4), ',', '') AS REAL) - CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL)) \/ CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL) AS per FROM employee AS T1  INNER JOIN position AS T2  ON T1.positionID = T2.positionID WHERE T1.firstname = 'Jose' AND T1.lastname = 'Rodriguez'"}],"db_id":"human_resources"}596{"prompt":"Among all indian restaurants in Castro St., Mountainview, how many of them is about cookhouse in their 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        Among all indian restaurants in Castro St., Mountainview, how many of them is about cookhouse in their label?\n        \n        ### Context:\n        #### Query Context:\n        indian restaurant refers to food_type = 'indian'; Castro St. refers to street_name = 'castro st'; Mountainview refers to city = 'mountainview'; have the word \"Indian\" in label refers to label = 'indian'\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 location AS T1 INNER JOIN generalinfo AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T1.street_name = 'castro st' AND T1.city = 'mountain view' AND T2.food_type = 'indian' AND T2.label LIKE '%cookhouse%'"}],"db_id":"restaurant"}597{"prompt":"Calculate the difference between the total number of students and the number of international international students in Univeristy of Tokyo from 2011 to 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        Calculate the difference between the total number of students and the number of international international students in Univeristy of Tokyo from 2011 to 2014.\n        \n        ### Context:\n        #### Query Context:\n        international students refers to DIVIDE(MULTIPLY(num_students, pct_international_students), 100); difference refers to SUBTRACT(SUM(num_students), SUM(DIVIDE(MULTIPLY(num_students, pct_international_students), 100))); in University of Tokyo refers to university_name = 'University of Tokyo'; from 2011 to 2014 refers to year BETWEEN 2011 AND 2014\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(T1.num_students) - SUM(CAST(T1.num_students * T1.pct_international_students AS REAL) \/ 100) FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id WHERE T1.year BETWEEN 2011 AND 2014 AND T2.university_name = 'University of Tokyo'"}],"db_id":"university"}598{"prompt":"In books authored by Abraham Lincoln, what is the percentage of the books published in 1992?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 books authored by Abraham Lincoln, what is the percentage of the books published in 1992?\n        \n        ### Context:\n        #### Query Context:\n        \"Abraham Lincoln\" is the author_name; published in 1992 refers to publication_date LIKE '1992%'; percentage = Divide (Sum(publication_date LIKE '1992%'), Count(publication_date)) * 100\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 CAST(SUM(CASE WHEN STRFTIME('%Y', T1.publication_date) = '1992' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) 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 = 'Abraham Lincoln'"}],"db_id":"books"}599{"prompt":"Provide the number of students at Yale University in 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        Provide the number of students at Yale University in 2016.\n        \n        ### Context:\n        #### Query Context:\n        number of students refers to num_students; Yale University refers to university_name = 'Yale University'; in 2016 refers to year = 2016\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 T1.num_students FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id WHERE T2.university_name = 'Yale University' AND T1.year = 2016"}],"db_id":"university"}600{"prompt":"Of the black officers, how many of them investigated cases between the years 2010 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        Of the black officers, how many of them investigated cases between the years 2010 and 2015?\n        \n        ### Context:\n        #### Query Context:\n        black refers to race = 'B'; between the years 2010 and 2015 refers to date between '2010-01-01' and '2015-12-31'; case refers to case_number\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 officers AS T1 INNER JOIN incidents AS T2 ON T2.case_number = T1.case_number WHERE T1.race = 'B' AND T2.date BETWEEN '2010-01-01' AND '2015-12-31'"}],"db_id":"shooting"}601{"prompt":"Among the players who won an award in the year 1983, how many of them play the position of goalie?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 won an award in the year 1983, how many of them play the position of goalie?\n        \n        ### Context:\n        #### Query Context:\n        position of goalie refers to pos = 'G'\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(playerID) FROM AwardsPlayers WHERE pos = 'G' AND year = 1983"}],"db_id":"hockey"}602{"prompt":"How many actors with the surname Kilmer 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 actors with the surname Kilmer are there?\n        \n        ### Context:\n        #### Query Context:\n        surname means 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 COUNT(actor_id) FROM actor WHERE last_name = 'Kilmer'"}],"db_id":"movie_3"}603{"prompt":"What is the phone brand of the device model \"\u575a\u679c\u624b\u673a\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 phone brand of the device model \"\u575a\u679c\u624b\u673a\"?\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 phone_brand FROM phone_brand_device_model2 WHERE device_model = '\u575a\u679c\u624b\u673a'"}],"db_id":"talkingdata"}604{"prompt":"Which country is University of Veterinary Medicine Vienna located in? Give its country 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 country is University of Veterinary Medicine Vienna located in? Give its country id.\n        \n        ### Context:\n        #### Query Context:\n        University of Veterinary Medicine Vienna refers to university_name = 'University of Veterinary Medicine Vienna';\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 country_id FROM university WHERE university_name = 'University of Veterinary Medicine Vienna'"}],"db_id":"university"}605{"prompt":"Among customers with IDs from 1 to 100, what is the highest price of products they 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        Among customers with IDs from 1 to 100, what is the highest price of products they purchased?\n        \n        ### Context:\n        #### Query Context:\n        IDs from 1 to 100 refers to CustomerID BETWEEN 1 AND 100 ; highest price refers to MAX(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 T1.Price FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T2.CustomerID BETWEEN 1 AND 100 ORDER BY T1.Price DESC LIMIT 1"}],"db_id":"sales"}606{"prompt":"List the ingredients which measure in slices.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 ingredients which measure in slices.\n        \n        ### Context:\n        #### Query Context:\n        slices refers to unit = 'slice(s)'\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 WHERE T2.unit = 'slice(s)'"}],"db_id":"cookbook"}607{"prompt":"What dishes made their first and last appearances in 1855 and 1900, respectively?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 dishes made their first and last appearances in 1855 and 1900, respectively?\n        \n        ### Context:\n        #### Query Context:\n        first appearance in 1855 refers to first_appeared = 1855; last appearance in 1900 refers to last_appeared = 1900;\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 Dish WHERE first_appeared = 1855 AND last_appeared = 1900"}],"db_id":"menu"}608{"prompt":"Provide the name of competitors from Greece.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 competitors from Greece.\n        \n        ### Context:\n        #### Query Context:\n        name refers to full_name; Greece refers to region_name = 'Greece';\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.full_name 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 = 'Greece'"}],"db_id":"olympics"}609{"prompt":"How many awards has Rene Balcer been nominated 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        How many awards has Rene Balcer been nominated for?\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 COUNT(T2.award_id) FROM Person AS T1 INNER JOIN Award AS T2 ON T1.person_id = T2.person_id WHERE T1.name = 'Rene Balcer'"}],"db_id":"law_episode"}610{"prompt":"Calculate the difference between the total sales in the East superstore and the total sales in the West 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        Calculate the difference between the total sales in the East superstore and the total sales in the West superstore.\n        \n        ### Context:\n        #### Query Context:\n        East superstore refers to Region = 'East'; West superstore refers to Region = 'West'; difference = subtract(sum(Sales) when Region = 'East', sum(Sales) when Region = 'West')\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(T1.Sales) - SUM(T2.Sales) AS difference FROM east_superstore AS T1 INNER JOIN west_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID`"}],"db_id":"superstore"}611{"prompt":"What is the percentage of act number 5 in Titus Andronicus?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 act number 5 in Titus Andronicus?\n        \n        ### Context:\n        #### Query Context:\n        act number 5 refers to Act = 5; Titus Andronicus refers to Title = 'Titus Andronicus'; percentage = divide(sum(Act = 5), count(Act)) 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.act = 5, 1, 0)) AS REAL) * 100 \/ COUNT(T2.act) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Titus Andronicus'"}],"db_id":"shakespeare"}612{"prompt":"Among the products with product ID lower than 15, how many of them costs 10 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        Among the products with product ID lower than 15, how many of them costs 10 and below?\n        \n        ### Context:\n        #### Query Context:\n        product ID lower than 15 refers to ProductID < 15; costs 10 and below refers to Price; Price < = 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 COUNT(ProductID) FROM Products WHERE ProductID < 15 AND Price <= 10"}],"db_id":"sales"}613{"prompt":"How many biwords pairs are there whose second word is \"grec\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 biwords pairs are there whose second word is \"grec\"?\n        \n        ### Context:\n        #### Query Context:\n        grec refers to word = 'grec'; wid where word = 'grec' AS w2nd\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(T2.w1st) FROM words AS T1 INNER JOIN biwords AS T2 ON T1.wid = T2.w2nd WHERE T1.word = 'grec'"}],"db_id":"language_corpus"}614{"prompt":"What is the percentage for free application with a rating 4.5 and above have not been updated since 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        What is the percentage for free application with a rating 4.5 and above have not been updated since 2018?\n        \n        ### Context:\n        #### Query Context:\n        paid refers to Type = 'Paid'; application refers to App; Rating>4.5; Last Updated>'2018; percentage = DIVIDE(SUM(Genres = 'Mature 17+' and Rating>4.5 and\u00a0substr(\"Last Updated\",-4,4)>'2018' )), (COUNT(App)) as percent;\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 CAST(SUM(CASE WHEN SUBSTR('Last Updated', -4) > '2018' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(App) PER FROM playstore WHERE Type = 'Free' AND Rating >= 4.5"}],"db_id":"app_store"}615{"prompt":"Name the object class of the image with lowest bounding box.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 object class of the image with lowest bounding box.\n        \n        ### Context:\n        #### Query Context:\n        bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H);\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.OBJ_CLASS FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID ORDER BY T1.H LIMIT 1"}],"db_id":"image_and_language"}616{"prompt":"What is the phone number of Kerry Pryor?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 phone number of Kerry Pryor?\n        \n        ### Context:\n        #### Query Context:\n        \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 phone_number FROM student WHERE l_name = 'Pryor' AND f_name = 'Kerry'"}],"db_id":"cs_semester"}617{"prompt":"How many Sales Rep who are working in Tokyo? List out email and full name of those 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        How many Sales Rep who are working in Tokyo? List out email and full name of those employees.\n        \n        ### Context:\n        #### Query Context:\n        Sales Rep is a job title; Tokyo is a city; full name = firstName+lastName;\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.firstName, T1.lastName, T1.email FROM employees AS T1 INNER JOIN offices AS T2 ON T1.officeCode = T2.officeCode WHERE T2.city = 'Tokyo' AND T1.jobTitle = 'Sales Rep'"}],"db_id":"car_retails"}618{"prompt":"How many canned A&W were purchased in 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 canned A&W were purchased in 2016?\n        \n        ### Context:\n        #### Query Context:\n        canned refers to ContainerType = 'Can'; A&W refers to BrandName = 'A&W'; in 2016 refers to PurchaseDate > = '2016-01-01' AND PurchaseDate < = '2016-12-31';\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 rootbeerbrand AS T2 ON T1.BrandID = T2.BrandID WHERE T1.ContainerType = 'Can' AND T2.BrandName = 'A&W' AND T1.PurchaseDate LIKE '2016%'"}],"db_id":"beer_factory"}619{"prompt":"In the West, how many stores are there in the city whose land area is below 20,000,000?","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        In the West, how many stores are there in the city whose land area is below 20,000,000?\n        \n        ### Context:\n        #### Query Context:\n        \"West\" is the Region; land area is below 20,000,000 refers to Land Area < 20,000,000\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.Region = 'West' AND T2.`Land Area` < 20000000 THEN 1 ELSE 0 END) FROM Regions AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StateCode = T1.StateCode"}],"db_id":"regional_sales"}620{"prompt":"Other than the Chief Executive Officer, who is the employee who has the highest payrate? State the 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        Other than the Chief Executive Officer, who is the employee who has the highest payrate? State the rate.\n        \n        ### Context:\n        #### Query Context:\n        other than the Chief Executive Officer refers to JobTitle! = 'Chief Executive Officer'; highest payrate refers to max(Rate)\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'], 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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 much time do businesses in El Mirage City, AZ State operate in average daily?\n        \n        ### Context:\n        #### Query Context:\n        how much time does this business open refers to SUBTRACT(closing_time, opening_time); DIVIDE(SUM(SUBTRACT(closing_time, opening_time)), SUM(Business.business_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 SUM(T2.closing_time - T2.opening_time) FROM Business AS T1 INNER JOIN Business_Hours AS T2 ON T1.business_id = T2.business_id WHERE T1.city = 'El Mirage' AND T1.state = 'AZ'"}],"db_id":"public_review_platform"}622{"prompt":"What was the review context from Jacksonville on 2017\/7\/22?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 review context from Jacksonville on 2017\/7\/22?\n        \n        ### Context:\n        #### Query Context:\n        Jacksonville refers to city = 'Jacksonville'; on 2017\/7\/22 refers to Date = '2017-07-22';\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.Reviews FROM reviews AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.city = 'Jacksonville' AND T1.Date = '2017-07-22'"}],"db_id":"retail_complains"}623{"prompt":"How many female students have enlisted for the Army?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 students have enlisted for the Army?\n        \n        ### Context:\n        #### Query Context:\n        female students refers to enlist.name who are NOT in male.name; Army refers to organ = 'army';\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 SUM(IIF(T3.name IS NULL, 1, 0)) AS \"result\" FROM enlist AS T1 INNER JOIN person AS T2 ON T1.name = T2.name LEFT JOIN male AS T3 ON T2.name = T3.name WHERE T1.organ = 'army'"}],"db_id":"student_loan"}624{"prompt":"List down the company names which supplied products for the order on 14th August, 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        List down the company names which supplied products for the order on 14th August, 1996.\n        \n        ### Context:\n        #### Query Context:\n        products refers to Order_Details.ProductID; on 14th August, 1996 refers to OrderDate = '8\/14\/1996'\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.CompanyName FROM Suppliers AS T1 INNER JOIN Products AS T2 ON T1.SupplierID = T2.SupplierID INNER JOIN `Order Details` AS T3 ON T2.ProductID = T3.ProductID INNER JOIN Orders AS T4 ON T3.OrderID = T4.OrderID WHERE date(T4.OrderDate) = '1996-08-14'"}],"db_id":"retail_world"}625{"prompt":"What is the total value of shipped vintage car orders from 2003-2004?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 value of shipped vintage car orders from 2003-2004?\n        \n        ### Context:\n        #### Query Context:\n        total value = SUM(MULTIPLY(quantityOrdered, priceEach)); shipped orders refers to status = 'Shipped'; vintage car is a product line; year(orderDate) between 2003 and 2004;\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(T2.priceEach * T2.quantityOrdered) FROM products AS T1 INNER JOIN orderdetails AS T2 ON T1.productCode = T2.productCode INNER JOIN orders AS T3 ON T2.orderNumber = T3.orderNumber WHERE T3.status = 'Shipped' AND T3.orderDate BETWEEN '2003-01-01' AND '2004-12-31'"}],"db_id":"car_retails"}626{"prompt":"What is the percentage of American players among all the players who 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        What is the percentage of American players among all the players who have gotten in the Hall of Fame?\n        \n        ### Context:\n        #### Query Context:\n        percentage of American players = divide(count(hofID where birthCountry = 'USA'), count(hofID))*100%\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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'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(COUNT(CASE WHEN T1.birthCountry = 'USA' THEN T1.playerID ELSE NULL END) AS REAL) * 100 \/ COUNT(T1.playerID) FROM Master AS T1 INNER JOIN HOF AS T2 ON T1.hofID = T2.hofID"}],"db_id":"hockey"}627{"prompt":"In what platform does the game ID 178 available?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 what platform does the game ID 178 available?\n        \n        ### Context:\n        #### Query Context:\n        platform refers to platform_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 T3.platform_name FROM game_publisher AS T1 INNER JOIN game_platform AS T2 ON T1.id = T2.game_publisher_id INNER JOIN platform AS T3 ON T2.platform_id = T3.id WHERE T1.game_id = 178"}],"db_id":"video_games"}628{"prompt":"Which country has the shortest life expectancy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 shortest life expectancy?\n        \n        ### Context:\n        #### Query Context:\n        shortest life expectancy refers to MIN(LifeExpectancy);\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 Name FROM Country ORDER BY LifeExpectancy LIMIT 1"}],"db_id":"world"}629{"prompt":"When were the products ordered by Cruz & Sons Co. on 2003-03-03 shipped?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 were the products ordered by Cruz & Sons Co. on 2003-03-03 shipped?\n        \n        ### Context:\n        #### Query Context:\n        Cruz & Sons Co. is name of customer; ordered on 2003-03-03 refers to orderDate;\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.shippedDate FROM orders AS t1 INNER JOIN customers AS t2 ON t1.customerNumber = t2.customerNumber WHERE t2.customerName = 'Cruz & Sons Co.' AND t1.orderDate = '2003-03-03'"}],"db_id":"car_retails"}630{"prompt":"Among legislators who have an Instagram account, list down their full names and nicknames who have a Thomas ID of 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        Among legislators who have an Instagram account, list down their full names and nicknames who have a Thomas ID of less than 1000.\n        \n        ### Context:\n        #### Query Context:\n        have an Instagram account refers to instagram is not null; full names refers to official_full_name; nicknames refers to nickname_name; Thomas ID of less than 1000 refers to thomas_id < 1000;\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, T1.nickname_name FROM current AS T1 INNER JOIN `social-media` AS T2 ON T2.bioguide = T1.bioguide_id WHERE T2.instagram IS NOT NULL AND T1.thomas_id < 1000"}],"db_id":"legislator"}631{"prompt":"Please list the bad alias of all the residential areas with a median female age of over 32.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 bad alias of all the residential areas with a median female age of over 32.\n        \n        ### Context:\n        #### Query Context:\n        median female age of over 32 refers to median_female_age > 32\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.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32"}],"db_id":"address"}632{"prompt":"What is the occasion for menu with ID 12463?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 occasion for menu with ID 12463?\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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 occasion FROM Menu WHERE id = 12463"}],"db_id":"menu"}633{"prompt":"List down the DOB of players who received the \"man of the match\" 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        List down the DOB of players who received the \"man of the match\" award.\n        \n        ### Context:\n        #### Query Context:\n        \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.DOB FROM Match AS T1 INNER JOIN Player AS T2 ON T2.Player_Id = T1.Man_of_the_Match"}],"db_id":"soccer_2016"}634{"prompt":"What movies did director Jack Kinney 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        What movies did director Jack Kinney direct?\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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 name FROM director WHERE director = 'Jack Kinney'"}],"db_id":"disney"}635{"prompt":"How many countries are using the same type of currency? Please list the short names of any 3 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        How many countries are using the same type of currency? Please list the short names of any 3 countries.\n        \n        ### Context:\n        #### Query Context:\n        any 3 countries refers to count(shortname)>3\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 FROM country WHERE currencyunit = 'U.S. dollar' LIMIT 3"}],"db_id":"world_development_indicators"}636{"prompt":"List all the keyword for 'Take My Life, Please' 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        List all the keyword for 'Take My Life, Please' episode.\n        \n        ### Context:\n        #### Query Context:\n        \"Take My Life,Please\" is the title of episode\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.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Take My Life, Please';"}],"db_id":"simpson_episodes"}637{"prompt":"Calculate the average payment amount per 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        Calculate the average payment amount per customer.\n        \n        ### Context:\n        #### Query Context:\n        average payment refers to AVG(amount)\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(amount) FROM payment GROUP BY customer_id"}],"db_id":"movie_3"}638{"prompt":"Provide list titles created by user who are eligible for trial 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        Provide list titles created by user who are eligible for trial when he created the list.\n        \n        ### Context:\n        #### Query Context:\n        eligible for trial refers to user_eligible_for_trial = 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 DISTINCT T2.list_title FROM lists_users AS T1 INNER JOIN lists AS T2 ON T1.list_id = T2.list_id WHERE T1.user_eligible_for_trial = 1"}],"db_id":"movie_platform"}639{"prompt":"Which author of the paper \"Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web\" is affiliated with National Taiwan University Department of Computer Science and Information Engineering Taiwan?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 author of the paper \"Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web\" is affiliated with National Taiwan University Department of Computer Science and Information Engineering Taiwan?\n        \n        ### Context:\n        #### Query Context:\n        \"Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web\" is the title of paper; affiliated with refers to Affiliation; \"National Taiwan University Department of Computer Science and Information Engineering Taiwan\" 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 T1.Name FROM PaperAuthor AS T1 INNER JOIN Paper AS T2 ON T1.PaperId = T2.Id WHERE T2.Title = 'Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web' AND T1.Affiliation = 'National Taiwan University Department of Computer Science and Information Engineering Taiwan'"}],"db_id":"authors"}640{"prompt":"What is the percentage of the methods' solutions that need to be compiled 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 is the percentage of the methods' solutions that need to be compiled among the methods whose comments is XML format?\n        \n        ### Context:\n        #### Query Context:\n        comment is XML format refers to CommentIsXml = 1; solution needs to be compiled refesr to WasCompiled = 0; percentage = MULTIPLY(DIVIDE(SUM(WasCompiled = 0), COUNT(Solution.Id)), 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 T1.WasCompiled = 0 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.SolutionId) FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.CommentIsXml = 1"}],"db_id":"codebase_comments"}641{"prompt":"How many crime cases have been classified as \"Weapons Violation\" by the FBI?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 cases have been classified as \"Weapons Violation\" by the FBI?\n        \n        ### Context:\n        #### Query Context:\n        \"Weapons Violation\" is the title of crime; crime cases refers to report_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 SUM(CASE WHEN T2.title = 'Weapons Violation' THEN 1 ELSE 0 END) FROM Crime AS T1 INNER JOIN FBI_Code AS T2 ON T1.fbi_code_no = T2.fbi_code_no"}],"db_id":"chicago_crime"}642{"prompt":"Which product line has the most products that are salable?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 line has the most products that are salable?\n        \n        ### Context:\n        #### Query Context:\n        Saleable product refers to FinishedGoodsFlag = 1\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'], 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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, 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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        List out 10 business ID that are being reviewed the most by users and list out what are top 3 business categories.\n        \n        ### Context:\n        #### Query Context:\n        being reviewed the most refers to MAX(user_id); business categories refer 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 T2.business_id, T3.category_name FROM Reviews 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 GROUP BY T2.business_id ORDER BY COUNT(T1.user_id) DESC LIMIT 10"}],"db_id":"public_review_platform"}644{"prompt":"In which platform does the game titled 15 Days available?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 platform does the game titled 15 Days available?\n        \n        ### Context:\n        #### Query Context:\n        platform refers to platform_name; the game titled 15 Days refers to game_name = '15 Days'\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 T1.platform_name FROM platform AS T1 INNER JOIN game_platform AS T2 ON T1.id = T2.platform_id INNER JOIN game_publisher AS T3 ON T2.game_publisher_id = T3.id INNER JOIN game AS T4 ON T3.game_id = T4.id WHERE T4.game_name = 'Counter Force'"}],"db_id":"video_games"}645{"prompt":"Which country are the majority of the suppliers 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        Which country are the majority of the suppliers located?\n        \n        ### Context:\n        #### Query Context:\n        majority of the suppliers located refers to MAX(COUNT(SupplierID))\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 Country FROM Suppliers GROUP BY Country ORDER BY COUNT(SupplierID) DESC LIMIT 1"}],"db_id":"retail_world"}646{"prompt":"Give me the immunization codes and dates on which Ms. Jacquelyn Shanahan was immunized with influenza seasonal injectable preservative-free vaccine.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 immunization codes and dates on which Ms. Jacquelyn Shanahan was immunized with influenza seasonal injectable preservative-free vaccine.\n        \n        ### Context:\n        #### Query Context:\n        immunization codes refers to immunizations.CODE; immunization dates refers to immunizations.DATE; immunized with influenza seasonal injectable preservative-free vaccine refers to immunizations.DESCRIPTION = 'Influenza seasonal injectable preservative free'; 4 consecutive years refers to 4 succeeding years;\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.CODE, T2.DATE FROM patients AS T1 INNER JOIN immunizations AS T2 ON T1.patient = T2.PATIENT WHERE T1.prefix = 'Ms.' AND T1.first = 'Jacquelyn' AND T1.last = 'Shanahan' AND T2.DESCRIPTION = 'Influenza  seasonal  injectable  preservative free'"}],"db_id":"synthea"}647{"prompt":"What is the average revenue of American movies 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 is the average revenue of American movies in 2006?\n        \n        ### Context:\n        #### Query Context:\n        American movies refers to country_name = 'United States of America'; in 2006 refers to release_date LIKE '2006%'; average revenue = AVG(revenue)\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 AVG(T1.revenue) 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 WHERE T3.COUNTry_name = 'United States of America' AND CAST(STRFTIME('%Y', T1.release_date) AS INT) = 2006"}],"db_id":"movies_4"}648{"prompt":"What is the attribute value of an active business with a low review count and 3 stars which is located at Goodyear, AZ?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 attribute value of an active business with a low review count and 3 stars which is located at Goodyear, AZ?\n        \n        ### Context:\n        #### Query Context:\n        active business ID refers to active = 'true'; low review count refers to review_count = 'Low'; 3 stars refers to stars = 3; Goodyear is a city; AZ is a state\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.attribute_value 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.state = 'AZ' AND T1.city = 'Goodyear' AND T1.active = 'true' AND T1.stars = 3 AND T1.review_count = 'Low'"}],"db_id":"public_review_platform"}649{"prompt":"What is the current address of customer Kandy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 current address of customer Kandy?\n        \n        ### Context:\n        #### Query Context:\n        current address refers to address_status = 1; address refers to street_number, street_name, city\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.street_number, T3.street_name, T3.city FROM customer AS T1 INNER JOIN customer_address AS T2 ON T1.customer_id = T2.customer_id INNER JOIN address AS T3 ON T3.address_id = T2.address_id INNER JOIN address_status AS T4 ON T4.status_id = T2.status_id WHERE T1.first_name = 'Kandy'"}],"db_id":"books"}650{"prompt":"Show the name of country id 66.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 name of country id 66.\n        \n        ### Context:\n        #### Query Context:\n        name of country 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 country_name FROM country WHERE id = 66"}],"db_id":"university"}651{"prompt":"In which city is the brewery AJ Stephans Beverages 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 city is the brewery AJ Stephans Beverages located?\n        \n        ### Context:\n        #### Query Context:\n        AJ Stephans 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 City FROM rootbeerbrand WHERE BreweryName = 'AJ Stephans Beverages'"}],"db_id":"beer_factory"}652{"prompt":"Which business had the most number of inspections? Give the Id number for that 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        Which business had the most number of inspections? Give the Id number for that business.\n        \n        ### Context:\n        #### Query Context:\n        the most number of inspections refers to MAX(COUNT(business_id)); Id number for that business refers to business_id;\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 business_id FROM inspections GROUP BY business_id ORDER BY COUNT(business_id) DESC LIMIT 1"}],"db_id":"food_inspection"}653{"prompt":"Complaint about Credit Card mostly came from clients of which age 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        Complaint about Credit Card mostly came from clients of which age group?\n        \n        ### Context:\n        #### Query Context:\n        about Credit Card refers to Product = 'Credit Card'; teenager refers to 13 < age < = 19; adult refers to 19 < age < = 65; elder refers to age < = 65\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 SUM(CASE WHEN T1.age > 13 AND T1.age <= 19 THEN 1 ELSE 0 END), SUM(CASE WHEN T1.age > 19 AND T1.age <= 65 THEN 1 ELSE 0 END) AS adult , SUM(CASE WHEN T1.age > 65 THEN 1 ELSE 0 END) AS elder FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.Product = 'Credit card'"}],"db_id":"retail_complains"}654{"prompt":"Please indicate the review count of the \"active life\" businesses in Phoenix.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 review count of the \"active life\" businesses in Phoenix.\n        \n        ### Context:\n        #### Query Context:\n        active life refers to category_name = 'Active Life'; Phoenix refers to city = 'Phoenix';\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(*) 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 = 'Active Life' AND T3.city = 'Phoenix'"}],"db_id":"public_review_platform"}655{"prompt":"How many Thai restaurants can be found in San Pablo Ave, Albany?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Thai restaurants can be found in San Pablo Ave, Albany?\n        \n        ### Context:\n        #### Query Context:\n        Thai restaurant refers to food_type = 'thai'; San Pablo Ave Albany refers to street_name = 'san pablo ave' AND T1.city = 'albany'\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.food_type = 'thai' AND T1.city = 'albany' AND T2.street_name = 'san pablo ave'"}],"db_id":"restaurant"}656{"prompt":"Please list any three orders that caused a loss 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        Please list any three orders that caused a loss to the company.\n        \n        ### Context:\n        #### Query Context:\n        caused a loss to the company refers to Profit < 0\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 `Order ID` FROM central_superstore WHERE Profit < 0 LIMIT 3"}],"db_id":"superstore"}657{"prompt":"What is the id of the patient who has the longest allergy period?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 patient who has the longest allergy period?\n        \n        ### Context:\n        #### Query Context:\n        id of the patient refers to PATIENT from allergies where STOP is not null; the longest allergy period refers to MAX(SUBTRACT((SUBSTR(STOP, - 2, 1)! = '9' then substr(STOP, length(STOP) - 1) + 2000 end), (SUBSTR(START, - 2, 1) = '9' then substr(START, length(START) - 1) + 1900 else substr(START, length(START) - 1) + 2000 end)));\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 PATIENT FROM allergies WHERE STOP IS NOT NULL GROUP BY PATIENT ORDER BY CASE WHEN SUBSTR(STOP, -2, 1) != '9' THEN SUBSTR(STOP, LENGTH(STOP) - 1) + 2000 END - CASE WHEN SUBSTR(START, -2, 1) = '9' THEN SUBSTR(START, LENGTH(START) - 1) + 1900 ELSE SUBSTR(START, LENGTH(START) - 1) + 2000 END LIMIT 1"}],"db_id":"synthea"}658{"prompt":"What is the original price of the \"Black Avery Flip-Chart Easel Binder\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 original price of the \"Black Avery Flip-Chart Easel Binder\"?\n        \n        ### Context:\n        #### Query Context:\n        \"Black Avery Flip-Chart Easel Binder\" is the \"Product Name\"; original price = divide(Sales, subtract(1, Discount))\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 T1.Sales \/ (1 - T1.Discount) FROM central_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T2.`Product Name` = 'Blackstonian Pencils'"}],"db_id":"superstore"}659{"prompt":"What is the average rating for the podcast that is most reviewed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 for the podcast that is most reviewed?\n        \n        ### Context:\n        #### Query Context:\n        most reviewed refers to Max(Count(reviews.podcast_id)); average rating refers to AVG (rating)\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 AVG(T2.rating) FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id GROUP BY T1.podcast_id ORDER BY COUNT(T2.content) DESC LIMIT 1"}],"db_id":"music_platform_2"}660{"prompt":"What is the average number of dishes per menu in the Souper de Luxe menus? Identify what is the name of the dish that appeared the most in all of its menus.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 dishes per menu in the Souper de Luxe menus? Identify what is the name of the dish that appeared the most in all of its menus.\n        \n        ### Context:\n        #### Query Context:\n        average number of dishes per menu = DIVIDE(COUNT(dish_id), COUNT(menu_page_id)); Souper de Luxe is a name of menu; dish that appeared the most in all of its menus refers to MAX(COUNT(dish_id));\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(*), T1.dish_id FROM MenuItem AS T1 INNER JOIN MenuPage AS T2 ON T1.menu_page_id = T2.id INNER JOIN Menu AS T3 ON T2.menu_id = T3.id INNER JOIN Dish AS T4 ON T1.dish_id = T4.id WHERE T3.name = 'Souper de Luxe' GROUP BY T3.id ORDER BY COUNT(T1.dish_id) DESC LIMIT 1"}],"db_id":"menu"}661{"prompt":"How many paragraphs are there in the scene whose description is \"A Sea-port in Cyprus. An open place near the quay.\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the scene whose description is \"A Sea-port in Cyprus. An open place near the quay.\"?\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 SUM(T2.ParagraphNum) FROM chapters AS T1 INNER JOIN paragraphs AS T2 ON T1.id = T2.chapter_id WHERE T1.Description = 'A Sea-port in Cyprus. An open place near the quay.'"}],"db_id":"shakespeare"}662{"prompt":"What is the current official Youtube username of Chris Van Hollen?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 current official Youtube username of Chris Van Hollen?\n        \n        ### Context:\n        #### Query Context:\n        Youtube username refers to youtube; Chris Van Hollen is an official_full_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.youtube FROM current AS T1 INNER JOIN `social-media` AS T2 ON T2.bioguide = T1.bioguide_id WHERE T1.official_full_name = 'Chris Van Hollen'"}],"db_id":"legislator"}663{"prompt":"How many films did \u00c5ke Sandgren 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 films did \u00c5ke Sandgren direct?\n        \n        ### Context:\n        #### Query Context:\n        Ake Sandgren is the director name;\u00a0 film refers to movie\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(movie_title) FROM movies WHERE director_name = '\u00c5ke Sandgren'"}],"db_id":"movie_platform"}664{"prompt":"How many donations of more than $100 were made for an honoree?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 donations of more than $100 were made for an honoree?\n        \n        ### Context:\n        #### Query Context:\n        an honoree refers to for_honoree = 't'; more than $100 refers to dollar_amount = '100_and_up'\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 dollar_amount = '100_and_up' AND for_honoree = 't'"}],"db_id":"donor"}665{"prompt":"In 2006, how many restricted films were 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 2006, how many restricted films were released?\n        \n        ### Context:\n        #### Query Context:\n        restricted refers to rating = 'R'; release refers to release_year; in 2006 refers to release_year = 2006; 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 COUNT(film_id) FROM film WHERE rating = 'R' AND release_year = 2006"}],"db_id":"movie_3"}666{"prompt":"How many subscribers are in the zip code of 94301 and what is the hottest temperature recorded on that 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        How many subscribers are in the zip code of 94301 and what is the hottest temperature recorded on that zip code?\n        \n        ### Context:\n        #### Query Context:\n        hottest temperature refers to MAX(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 COUNT(T3.zip_code), T3.max_temperature_f FROM trip AS T2 INNER JOIN weather AS T3 ON T3.zip_code = T2.zip_code WHERE T3.zip_code = 94301 AND T2.subscription_type = 'Subscriber' ORDER BY T3.max_temperature_f DESC LIMIT 1"}],"db_id":"bike_share_1"}667{"prompt":"How much are the sales of the games in region ID 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 much are the sales of the games in region ID 4?\n        \n        ### Context:\n        #### Query Context:\n        how much are the sales = SUM(num_sales);\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 = 4"}],"db_id":"video_games"}668{"prompt":"What is the average winning rate of the Buffalo Sabres 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        What is the average winning rate of the Buffalo Sabres in 2000?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(SUM(DIVIDE(W,G), COUNT(oppID) where year = 2000; Montreal Canadiens is 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, '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 SUM(CAST(T2.W AS REAL) \/ T2.G) \/ COUNT(T1.oppID) FROM TeamVsTeam AS T1 INNER JOIN Teams AS T2 ON T1.year = T2.year AND T1.tmID = T2.tmID WHERE T2.name = 'Buffalo Sabres' AND T1.year = 2000"}],"db_id":"hockey"}669{"prompt":"Provide the most used keyword in the 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        Provide the most used keyword in the movies.\n        \n        ### Context:\n        #### Query Context:\n        most used keyword refers to keyword_name where max(count(keyword_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.keyword_name FROM keyword AS T1 INNER JOIN movie_keywords AS T2 ON T1.keyword_id = T2.keyword_id GROUP BY T1.keyword_name ORDER BY COUNT(T1.keyword_name) DESC LIMIT 1"}],"db_id":"movies_4"}670{"prompt":"List down the ID of movies with running time of 3 and average revenue of 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        List down the ID of movies with running time of 3 and average revenue of 1?\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 T1.movieid FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid INNER JOIN directors AS T3 ON T2.directorid = T3.directorid WHERE T1.runningtime = 3 AND T3.avg_revenue = 1"}],"db_id":"movielens"}671{"prompt":"Which books were released by Orson Scott Card in 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        Which books were released by Orson Scott Card in 2001?\n        \n        ### Context:\n        #### Query Context:\n        \"Orson Scott Card\" is the author_name; released in 2001 refers to publication_date LIKE '2001%'; books refers to title\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 = 'Orson Scott Card' AND STRFTIME('%Y', T1.publication_date) = '2001'"}],"db_id":"books"}672{"prompt":"For the device with an event occurring on 2016\/5\/1 at 0:55:25, what is the gender of its 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 the device with an event occurring on 2016\/5\/1 at 0:55:25, what is the gender of its user?\n        \n        ### Context:\n        #### Query Context:\n        on 2016\/5\/1 at 0:55:25 refers to timestamp = '2016-05-01 00:55:25';\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.gender FROM gender_age AS T1 INNER JOIN events AS T2 ON T1.device_id = T2.device_id WHERE T2.timestamp = '2016-05-01 00:55:25'"}],"db_id":"talkingdata"}673{"prompt":"Please provide full name of any two countries using 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 provide full name of any two countries using special trade system.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to longname; using special trade system refers to systemoftrade = 'Special trade system'\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 LongName FROM Country WHERE SystemOfTrade = 'Special trade system' LIMIT 2"}],"db_id":"world_development_indicators"}674{"prompt":"Among the actors starred in Die Hard 2, list their net worth and birth date of actors with a height between 60 to 65.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 actors starred in Die Hard 2, list their net worth and birth date of actors with a height between 60 to 65.\n        \n        ### Context:\n        #### Query Context:\n        Die Hard 2 refers to Title = 'Die Hard 2'; height between 60 to 65 refers to Height (Inches) BETWEEN 60 AND 65\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 T3.NetWorth, T3.`Date of Birth` 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 = 'Die Hard 2' AND T3.`Height (Inches)` BETWEEN 60 AND 65"}],"db_id":"movie"}675{"prompt":"Which country had the game that Away team made the most 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        Which country had the game that Away team made the most goals?\n        \n        ### Context:\n        #### Query Context:\n        the most goals refer to MAX(FTAG), which is a short name for Final-time Away-team Goals;\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 T2.country FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division GROUP BY T2.country ORDER BY SUM(T1.FTAG) DESC LIMIT 1"}],"db_id":"european_football_1"}676{"prompt":"Among the root beer brands that do not advertise on Twitter, how many of them have root beers sold 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        Among the root beer brands that do not advertise on Twitter, how many of them have root beers sold in August, 2014?\n        \n        ### Context:\n        #### Query Context:\n        do not advertise on Twitter refers to twitter IS NULL; 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.Twitter IS NULL"}],"db_id":"beer_factory"}677{"prompt":"What percentage of the total prices of all orders are shipped internationally?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 total prices of all orders are shipped internationally?\n        \n        ### Context:\n        #### Query Context:\n        shipped internationally refers to method_name = 'International'; percentage = Divide (Sum(price where method_name = 'International'), Sum(price)) * 100\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 CAST(SUM(CASE WHEN T3.method_name = 'International' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM cust_order AS T1 INNER JOIN order_line AS T2 ON T1.order_id = T2.order_id INNER JOIN shipping_method AS T3 ON T3.method_id = T1.shipping_method_id"}],"db_id":"books"}678{"prompt":"Name all the trips where the bike was borrowed and returned on a different day. State the city where the bike was returned.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the trips where the bike was borrowed and returned on a different day. State the city where the bike was returned.\n        \n        ### Context:\n        #### Query Context:\n        the bike was borrowed and returned on a different day implies that start_date and end_date are not equal to each other; where the bike was returned refers to end_station_name;\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 DISTINCT T1.city FROM station AS T1 INNER JOIN trip AS T2 ON T2.start_station_name = T1.name WHERE SUBSTR(CAST(T2.start_date AS TEXT), INSTR(T2.start_date, '\/') + 1) - SUBSTR(CAST(T2.start_date AS TEXT), INSTR(T2.start_date, ' ') - 5) <> SUBSTR(CAST(T2.end_date AS TEXT), INSTR(T2.end_date, '\/') + 1) - SUBSTR(CAST(T2.end_date AS TEXT), INSTR(T2.end_date, ' ') - 5)"}],"db_id":"bike_share_1"}679{"prompt":"How many total occurrences are there in the three-letter 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        How many total occurrences are there in the three-letter words?\n        \n        ### Context:\n        #### Query Context:\n        three-letter words are words composed of exactly three letters;\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 SUM(occurrences) FROM words WHERE LENGTH(word) = 3"}],"db_id":"language_corpus"}680{"prompt":"List all employees working for publisher 'GGG&G'. State their name and job description.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 employees working for publisher 'GGG&G'. State their name and job description.\n        \n        ### Context:\n        #### Query Context:\n        name = fname, lname; job description refers to job_desc; publisher refers 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.fname, T1.lname, T3.job_desc FROM employee AS T1 INNER JOIN publishers AS T2 ON T1.pub_id = T2.pub_id INNER JOIN jobs AS T3 ON T1.job_id = T3.job_id WHERE T2.pub_name = 'GGG&G'"}],"db_id":"book_publishing_company"}681{"prompt":"List all the streets where pizza-serving restaurants are found in San Jose.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 streets where pizza-serving restaurants are found in San Jose.\n        \n        ### Context:\n        #### Query Context:\n        street refers to street_name; pizza-serving restaurant refers to food_type = 'pizza'; San Jose refers to city = 'san jose'\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.city = T2.city WHERE T2.food_type = 'pizza' AND T1.city = 'san jose'"}],"db_id":"restaurant"}682{"prompt":"What is the number of air carriers 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        What is the number of air carriers in the database?\n        \n        ### Context:\n        #### Query Context:\n        \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 COUNT(Code) FROM `Air Carriers`"}],"db_id":"airline"}683{"prompt":"In films with a length duration of 113 minutes, how many of the films are starred by Kirk Jovovich?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 films with a length duration of 113 minutes, how many of the films are starred by Kirk Jovovich?\n        \n        ### Context:\n        #### Query Context:\n        length duration of 113 minutes refers to length = 113; 'Kirk Jovovich' is a full name of an actor; 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 COUNT(T1.actor_id) 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 T3.length = 113 AND T1.first_name = 'Kirk' AND T1.last_name = 'Jovovich'"}],"db_id":"movie_3"}684{"prompt":"How many seasons did Pune Warriors participate 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        How many seasons did Pune Warriors participate in?\n        \n        ### Context:\n        #### Query Context:\n        Pune Warriors refers to Team_Name = 'Pune Warriors'\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(T.Season_Year) FROM ( SELECT T4.Season_Year 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 Season AS T4 ON T2.Season_Id = T4.Season_Id WHERE T1.Team_Name = 'Pune Warriors' GROUP BY T4.Season_Year ) T"}],"db_id":"soccer_2016"}685{"prompt":"How many active employees whose payrate is equal or below 30 per 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        How many active employees whose payrate is equal or below 30 per hour.\n        \n        ### Context:\n        #### Query Context:\n        active employee refers to CurrentFlag = 1; Rate< = 30;\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, 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[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 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'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(T1.BusinessEntityID) FROM Employee AS T1 INNER JOIN EmployeePayHistory AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.CurrentFlag = 1 AND T2.Rate <= 30"}],"db_id":"works_cycles"}686{"prompt":"If I'm from the Muscle Machine Inc, to which e-mail adress should I write a letter if I want to reach the superior of my 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        If I'm from the Muscle Machine Inc, to which e-mail adress should I write a letter if I want to reach the superior of my sales representitive?\n        \n        ### Context:\n        #### Query Context:\n        Muscle Machine Inc is name of customer; superior refers to 'reportsTO', who is the leader of the 'employeeNumber'\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 t2.email FROM customers AS t1 INNER JOIN employees AS t2 ON t1.salesRepEmployeeNumber = t2.employeeNumber WHERE t1.customerName = 'Muscle Machine Inc'"}],"db_id":"car_retails"}687{"prompt":"Provide the names of podcasts in the art category 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        Provide the names of podcasts in the art category in 2018.\n        \n        ### Context:\n        #### Query Context:\n        art category refers to category = 'arts'; in 2018 refers to created_at like '2018%'; name of podcast refers to title\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 reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.category = 'arts' AND T2.created_at LIKE '2018-%'"}],"db_id":"music_platform_2"}688{"prompt":"What is the proportion of the papers that have the keyword \"cancer\"? Please provide a list of authors and their affiliations.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the papers that have the keyword \"cancer\"? Please provide a list of authors and their affiliations.\n        \n        ### Context:\n        #### Query Context:\n        Proportion refer to DIVIDE(COUNT(Keyword = \u2019cancer\u2019), COUNT(PaperID))\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.Keyword = 'cancer' THEN 1 ELSE 0 END) AS REAL) \/ COUNT(T1.Id), T2.Name, T2.Affiliation FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId"}],"db_id":"authors"}689{"prompt":"How many tweets have a klout of over 50?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 have a klout of over 50?\n        \n        ### Context:\n        #### Query Context:\n        klout of over 50 refers to Klout > 50\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 Klout > 50"}],"db_id":"social_media"}690{"prompt":"What is the difference in the most populated city of Allentown-Bethlehem-Easton, PA-NJ in 2020 against its population in 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        What is the difference in the most populated city of Allentown-Bethlehem-Easton, PA-NJ in 2020 against its population in 2010?\n        \n        ### Context:\n        #### Query Context:\n        \"Allentown-Bethlehem-Easton, PA-NJ\" is the CBSA_name; most populated city refers to Max(population_2020); difference = Subtract (population_2020, population_2011)\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.population_2020 - T1.population_2010 AS result_data FROM zip_data AS T1 INNER JOIN CBSA AS T2 ON T1.CBSA = T2.CBSA WHERE T2.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ' ORDER BY T1.population_2020 DESC LIMIT 1"}],"db_id":"address"}691{"prompt":"What is indicator code of Rural 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 indicator code of Rural population?\n        \n        ### Context:\n        #### Query Context:\n        IndicatorName = 'Rural population';\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 IndicatorCode FROM Indicators WHERE IndicatorName = 'Rural population'"}],"db_id":"world_development_indicators"}692{"prompt":"Calculate the total profit made by chocolate floral blue coral cyan.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 profit made by chocolate floral blue coral cyan.\n        \n        ### Context:\n        #### Query Context:\n        SUBTRACT(MULTIPLY(l_extendedprice, (SUBTRACT(1, l_discount)), MULTIPLY(ps_supplycost, l_quantity))) where p_name = 'chocolate floral blue coral cyan';\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(T3.l_extendedprice * (1 - T3.l_discount) - T2.ps_supplycost * T3.l_quantity) FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey INNER JOIN lineitem AS T3 ON T2.ps_partkey = T3.l_partkey AND T2.ps_suppkey = T3.l_suppkey WHERE T1.p_name = 'chocolate floral blue coral cyan'"}],"db_id":"retails"}693{"prompt":"Calculate the percentage of business which opened on Sunday from 9AM to 9PM based on the number of business opened on Sunday.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 business which opened on Sunday from 9AM to 9PM based on the number of business opened on Sunday.\n        \n        ### Context:\n        #### Query Context:\n        Sunday refers to day_of_week = 'Sunday' where day_id = 1; opened from 9AM to 9PM refers to Business_Hours where opening_time = '9AM' and closing_time = '9PM'; DIVIDE(COUNT(opening_time = '9AM' and closing_time = '9PM' and day_of_week = 'Sunday'), COUNT(opening_time = NOT NULL and closing_time = NOT NULL and day_of_week = 'Sunday')) 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 CAST(SUM(CASE WHEN T2.opening_time = '9AM' AND T2.closing_time = '9PM' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.day_id) FROM Days AS T1 INNER JOIN Business_Hours AS T2 ON T1.day_id = T2.day_id WHERE T1.day_of_week = 'Sunday'"}],"db_id":"public_review_platform"}694{"prompt":"What are the miles per gallon of the most expensive car?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 miles per gallon of the most expensive car?\n        \n        ### Context:\n        #### Query Context:\n        miles per gallon refers to mpg; 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.mpg FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID ORDER BY T2.price DESC LIMIT 1"}],"db_id":"cars"}695{"prompt":"What is the percentage of the complaint calls from Mr Mason Javen Lopez has got the consent provided by the 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 percentage of the complaint calls from Mr Mason Javen Lopez has got the consent provided by the customer?\n        \n        ### Context:\n        #### Query Context:\n        percentage = MULTIPLY(DIVIDE(SUM(\"Consumer consent provided?\" = 'Consent provided'), COUNT(client_id)), 1.0); Mr refers to sex = 'Male'; consent provided by the customer refers to \"Consumer consent provided?\" = 'Consent provided';\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 CAST(SUM(CASE WHEN T2.`Consumer consent provided?` = 'Consent provided' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.`Consumer consent provided?`) FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.sex = 'Male' AND T1.first = 'Mason' AND T1.middle = 'Javen' AND T1.last = 'Lopez'"}],"db_id":"retail_complains"}696{"prompt":"How many games did team of the scoring champion win in 2001 NBA 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 team of the scoring champion win in 2001 NBA season?\n        \n        ### Context:\n        #### Query Context:\n        the scoring champion refers to max(won); 2001 refers to year = 2001; NBA refers to lgID = 'NBA'\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.W FROM teams AS T1 INNER JOIN series_post AS T2 ON T1.tmID = T2.tmIDLoser AND T1.year = T2.year WHERE T2.year = 2001 ORDER BY T1.o_fgm DESC LIMIT 1"}],"db_id":"professional_basketball"}697{"prompt":"What is the least purchased product by stores in the city of Santa Clarita?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 least purchased product by stores in the city of Santa Clarita?\n        \n        ### Context:\n        #### Query Context:\n        least purchased product refers to Min(Count(Product Name)); 'Santa Clarita' is 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 T1.`Product Name` FROM Products AS T1 INNER JOIN `Sales Orders` AS T2 ON T2._ProductID = T1.ProductID INNER JOIN `Store Locations` AS T3 ON T3.StoreID = T2._StoreID WHERE T3.`City Name` = 'Santa Clarita' GROUP BY T1.`Product Name` ORDER BY COUNT(T1.`Product Name`) ASC LIMIT 1"}],"db_id":"regional_sales"}698{"prompt":"Calculate the quantity percentage of the gift products in the total trading 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        Calculate the quantity percentage of the gift products in the total trading quantity.\n        \n        ### Context:\n        #### Query Context:\n        percentage = MULTIPLY(DIVIDE(SUM(Quantity WHERE Price = 0), SUM(Quantity)), 1.0); gift products refers to Price = 0;\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 CAST(SUM(IIF(T1.Price = 0, T2.Quantity, 0)) AS REAL) * 100 \/ SUM(T2.Quantity)FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID"}],"db_id":"sales"}699{"prompt":"How many pages are there in the \"Emil Kuehn\" menu?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 pages are there in the \"Emil Kuehn\" menu?\n        \n        ### Context:\n        #### Query Context:\n        Emil Kuehn is a name of menu;\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 = 'Emil Kuehn' THEN 1 ELSE 0 END) FROM Menu AS T1 INNER JOIN MenuPage AS T2 ON T1.id = T2.menu_id"}],"db_id":"menu"}700{"prompt":"Calculate average scene per act in Antony and Cleopatra.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 average scene per act in Antony and Cleopatra.\n        \n        ### Context:\n        #### Query Context:\n        Antony and Cleopatra refers to Title = 'Antony and Cleopatra'; average scene per act = divide(sum(Scene), count(act))\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 CAST(SUM(T2.Scene) AS REAL) \/ COUNT(T2.act) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Antony and Cleopatra'"}],"db_id":"shakespeare"}701{"prompt":"What was the rating of the episodes that Jace Alexander worked 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        What was the rating of the episodes that Jace Alexander worked on?\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.rating 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 T3.name = 'Jace Alexander'"}],"db_id":"law_episode"}702{"prompt":"In which Olympic Games has Morten Aleksander Djupvik participated?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Olympic Games has Morten Aleksander Djupvik participated?\n        \n        ### Context:\n        #### Query Context:\n        In which Olympic Games refer to games_year;\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.games_name 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 = 'Morten Aleksander Djupvik'"}],"db_id":"olympics"}703{"prompt":"State the courses and level of courses by professors who are faculty 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        State the courses and level of courses by professors who are faculty employees.\n        \n        ### Context:\n        #### Query Context:\n        professors who are faculty employees refers to professor = 1; faculty employees refers to 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 T3.course_id, T3.courseLevel FROM taughtBy AS T1 INNER JOIN person AS T2 ON T1.p_id = T2.p_id INNER JOIN course AS T3 ON T3.course_id = T1.course_id WHERE T2.hasPosition = 'Faculty_eme'"}],"db_id":"computer_student"}704{"prompt":"Please list the birth date of the player who has won the most MVPs.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 birth date of the player who has won the most MVPs.\n        \n        ### Context:\n        #### Query Context:\n        won the most MVP refers to Max(Count(award = 'Most Valuable Player'))\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 T1.birthDate FROM players AS T1 INNER JOIN awards_players AS T2 ON T1.playerID = T2.playerID WHERE T2.award = 'Most Valuable Player' GROUP BY T1.playerID, T1.birthDate ORDER BY COUNT(award) DESC LIMIT 1"}],"db_id":"professional_basketball"}705{"prompt":"Give the full name of the actor with the highest rental 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        Give the full name of the actor with the highest rental rate.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name; the highest rental rate refers to max(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 T1.first_name, T1.last_name FROM actor AS T1 INNER JOIN film_actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T3.film_id = T2.film_id ORDER BY T3.rental_rate DESC LIMIT 1"}],"db_id":"movie_3"}706{"prompt":"How many districts did John Conyers, Jr. serve 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 districts did John Conyers, Jr. serve in total?\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 COUNT(T3.district) FROM ( SELECT T2.district FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.official_full_name = 'John Conyers, Jr.' GROUP BY T2.district ) T3"}],"db_id":"legislator"}707{"prompt":"Indicate the page id of Wikipedia about Catalan language of all the pages that have a numeric value in their 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        Indicate the page id of Wikipedia about Catalan language of all the pages that have a numeric value in their title.\n        \n        ### Context:\n        #### Query Context:\n        the page id of Wikipedia refers to pid; numeric value contains only numbers, LIKE '%0%' OR '%1%' OR '%2%' OR '%3%' OR '%4%' OR '%5%' OR '%6%' OR '%7%' OR '%8%' OR '%9%';\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 pid, title FROM pages WHERE title LIKE '%0%' OR '%1%' OR '%2%' OR '%3%' OR '%4%' OR '%5%' OR '%6%' OR '%7%' OR '%8%' OR '%9%'"}],"db_id":"language_corpus"}708{"prompt":"How many of Shakespeare's works were finished before the year 1602?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Shakespeare's works were finished before the year 1602?\n        \n        ### Context:\n        #### Query Context:\n        finished before the year 1602 refers to Date < 1602\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 works WHERE Date < 1602"}],"db_id":"shakespeare"}709{"prompt":"How many percent of senators were from class 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 percent of senators were from class 1?\n        \n        ### Context:\n        #### Query Context:\n        senator refers to type = 'sen'; class 1 refers to class = 1; calculation = MULTIPLY(DIVIDE(COUNT(class = 1 then bioguide), COUNT(bioguide)), 1.0)\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 CAST(SUM(CASE WHEN class = 1 THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM `historical-terms` WHERE type = 'sen'"}],"db_id":"legislator"}710{"prompt":"How many cases of billing dispute issues occurred in the Mountain 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 cases of billing dispute issues occurred in the Mountain division?\n        \n        ### Context:\n        #### Query Context:\n        billing dispute refers to issue = 'Billing disputes';\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.Issue) FROM events AS T1 INNER JOIN client AS T2 ON T1.Client_ID = T2.client_id INNER JOIN district AS T3 ON T2.district_id = T3.district_id WHERE T1.Issue = 'Billing disputes' AND T3.division = 'Mountain'"}],"db_id":"retail_complains"}711{"prompt":"In the year 1998, how many home wins did the team which had the 1st round, 12th pick have that 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        In the year 1998, how many home wins did the team which had the 1st round, 12th pick have that year?\n        \n        ### Context:\n        #### Query Context:\n        in 1998 refers to year = 1998; 1st round refers to draftRound = 1; 12th pick refers to draftSelection = 12; home win refers to homeWon\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.homeWon FROM draft AS T1 INNER JOIN teams AS T2 ON T1.tmID = T2.tmID AND T1.draftYear = T2.year WHERE T1.draftRound = 1 AND T1.draftSelection = 12 AND T1.draftYear = 1998"}],"db_id":"professional_basketball"}712{"prompt":"How many shipments were ordered by a customer in Florida?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 shipments were ordered by a customer in Florida?\n        \n        ### Context:\n        #### Query Context:\n        customer in Florida refers to state = 'FL'\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(T1.cust_id) FROM customer AS T1 INNER JOIN shipment AS T2 ON T1.cust_id = T2.cust_id WHERE T1.state = 'FL'"}],"db_id":"shipping"}713{"prompt":"Among all the trips, which day had the most bikes borrowed? What was the average coldest temperature on that day?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 trips, which day had the most bikes borrowed? What was the average coldest temperature on that day?\n        \n        ### Context:\n        #### Query Context:\n        which day had the most bikes borrowed refers to MAX(start_date); the average coldest temperature can be computed as DIVIDE(SUM(min_temperature_f), COUNT(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 T2.date, AVG(T2.min_temperature_f) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code GROUP BY T2.date ORDER BY COUNT(T1.start_date) DESC LIMIT 1"}],"db_id":"bike_share_1"}714{"prompt":"Users in which city of Argentina post the most tweets?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Users in which city of Argentina post the most tweets?\n        \n        ### Context:\n        #### Query Context:\n        \"Argentina\" is the Country; post the most tweets refers to 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 T2.City FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID WHERE T2.Country = 'Argentina' GROUP BY T2.City ORDER BY COUNT(T1.TweetID) DESC LIMIT 1"}],"db_id":"social_media"}715{"prompt":"Name the shipper which had the most shipments in first quarter of 1998.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 shipper which had the most shipments in first quarter of 1998.\n        \n        ### Context:\n        #### Query Context:\n        Name the shipper refers to CompanyName; most shipments refers to max(count(OrderID)); first quarter of 1998 refers to ShippedDate = 1998\/1 and ShippedDate = 1998\/2 and ShippedDate = 1998\/3 and ShippedDate = 1998\/4\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.CompanyName FROM Shippers AS T1 INNER JOIN Orders AS T2 ON T1.ShipperID = T2.ShipVia WHERE STRFTIME('%Y', T2.ShippedDate) = '1998' GROUP BY T1.CompanyName ORDER BY COUNT(T2.OrderID) DESC LIMIT 1"}],"db_id":"retail_world"}716{"prompt":"State name of unemployed students who have the longest duration of absense 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        State name of unemployed students who have the longest duration of absense from school.\n        \n        ### Context:\n        #### Query Context:\n        longest duration of absense refers to MAX(month)\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 unemployed AS T2 ON T1.name = T2.name ORDER BY T1.month DESC LIMIT 1"}],"db_id":"student_loan"}717{"prompt":"State the customer name of orders which has shipped date in 7\/8\/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        State the customer name of orders which has shipped date in 7\/8\/2018.\n        \n        ### Context:\n        #### Query Context:\n        shipped date in 7\/8\/2018 refers to ShipDate = '7\/8\/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 T FROM ( SELECT DISTINCT CASE  WHEN T2.ShipDate = '7\/8\/18' THEN T1.`Customer Names` END AS T FROM Customers T1 INNER JOIN `Sales Orders` T2 ON T2._CustomerID = T1.CustomerID ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}718{"prompt":"What are the names of the two people who won an award for their role as directors?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 two people who won an award for their role as directors?\n        \n        ### Context:\n        #### Query Context:\n        won an award refers to result = 'Winner'; role as director refers to role = 'director'\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.name FROM Person AS T1 INNER JOIN Award AS T2 ON T1.person_id = T2.person_id WHERE T2.Result = 'Winner' AND T2.role = 'director'"}],"db_id":"law_episode"}719{"prompt":"How many females use ZUK Z1 phones in the age group under 23?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 females use ZUK Z1 phones in the age group under 23?\n        \n        ### Context:\n        #### Query Context:\n        females refers to gender = 'F'; ZUK Z1 refers to phone_brand = 'ZUK' AND device_model = 'Z1'; under 23 refers to `group` = 'F23-' ;\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.gender = 'F' AND T2.device_model = 'Z1' AND T1.`group` = 'F23-' AND T2.phone_brand = 'ZUK'"}],"db_id":"talkingdata"}720{"prompt":"How many methods with solutions with path 'maravillas_linq-to-delicious\\tasty.sln'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 methods with solutions with path 'maravillas_linq-to-delicious\\tasty.sln'?\n        \n        ### Context:\n        #### Query Context:\n        solution refers to SolutionId;\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.SolutionId) FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T1.Path = 'maravillas_linq-to-delicious\\tasty.sln'"}],"db_id":"codebase_comments"}721{"prompt":"Indicate the bounding box of the image 8.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 bounding box of the image 8.\n        \n        ### Context:\n        #### Query Context:\n        bounding box refers to X, Y, W, H from IMG_OBJ; image 8 refers to 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 X, Y, W, H FROM IMG_OBJ WHERE IMG_ID = 8"}],"db_id":"image_and_language"}722{"prompt":"Which indicator name uses the Weighted average method and has the lowest value?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 indicator name uses the Weighted average method and has the lowest value?\n        \n        ### Context:\n        #### Query Context:\n        AggregationMethod = 'Weighted average'; the lowest value implies MIN(Value);\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.IndicatorName, MIN(T1.Value) FROM Indicators AS T1 INNER JOIN Series AS T2 ON T1.IndicatorName = T2.IndicatorName WHERE T2.AggregationMethod = 'Weighted average'"}],"db_id":"world_development_indicators"}723{"prompt":"What is the name of the course with the highest satisfaction from 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 name of the course with the highest satisfaction from students?\n        \n        ### Context:\n        #### Query Context:\n        sat refers to student's satisfaction degree with the course where sat = 5 stands for the highest satisfaction;\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 DISTINCT T2.name FROM registration AS T1 INNER JOIN course AS T2 ON T1.course_id = T2.course_id WHERE T1.sat = 5"}],"db_id":"cs_semester"}724{"prompt":"How many country uses the 2008 System of National Accounts methodology? List our their table 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        How many country uses the 2008 System of National Accounts methodology? List our their table name.\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 TableName  FROM Country WHERE SystemOfNationalAccounts = 'Country uses the 2008 System of National Accounts methodology.'"}],"db_id":"world_development_indicators"}725{"prompt":"Provide the number of users who took part in the \"mental health survey for 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        Provide the number of users who took part in the \"mental health survey for 2016\".\n        \n        ### Context:\n        #### Query Context:\n        mental health survey for 2016 refers to SurveyID = 2016\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 2016'"}],"db_id":"mental_health_survey"}726{"prompt":"Which non-playoffs team had the most points in the regular season in the year 1998?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 non-playoffs team had the most points in the regular season in the year 1998?\n        \n        ### Context:\n        #### Query Context:\n        non-playoff refers to PostGP = 0; in the year 1998 refers to year = 1998; the most points refers to max(o_pts)\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.year = 1998 AND T1.PostGP = 0 ORDER BY T1.points DESC LIMIT 1"}],"db_id":"professional_basketball"}727{"prompt":"Calculate the percentage of podcasts in the fiction-science-fiction 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        Calculate the percentage of podcasts in the fiction-science-fiction category.\n        \n        ### Context:\n        #### Query Context:\n        percentage = \u00a0 Divide (Count(podcast_id(category = 'fiction-science-fiction')), 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 category = 'fiction-science-fiction' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(podcast_id) OR '%' \"percentage\" FROM categories"}],"db_id":"music_platform_2"}728{"prompt":"What is the name of the co-authors of the paper titled 'Particle identification using the time-over-threshold method in the ATLAS Transition Radiation Tracker'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 co-authors of the paper titled 'Particle identification using the time-over-threshold method in the ATLAS Transition Radiation Tracker'?\n        \n        ### Context:\n        #### Query Context:\n        paper titled 'Particle identification using the time-over-threshold method in the ATLAS Transition Radiation Tracker' refers to Title = 'Particle identification using the time-over-threshold method in the ATLAS Transition Radiation Tracker'\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.Name FROM PaperAuthor AS T1 INNER JOIN Paper AS T2 ON T1.PaperId = T2.Id WHERE T2.Title = 'Particle identification using the time-over-threshold method in the ATLAS Transition Radiation Tracker'"}],"db_id":"authors"}729{"prompt":"Which apps have multiple genres and what is the total sentiment subjectivity of these 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        Which apps have multiple genres and what is the total sentiment subjectivity of these apps?\n        \n        ### Context:\n        #### Query Context:\n        multiple genres refers to COUNT(Genres>1; total sentiment subjectivity = Sum(Sentiment_Subjectivity);\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 SUM(T2.Sentiment_Subjectivity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Genres > 1"}],"db_id":"app_store"}730{"prompt":"Please state the segment, the name, the address, and the phone number of customer number 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 state the segment, the name, the address, and the phone number of customer number 3.\n        \n        ### Context:\n        #### Query Context:\n        segment refers to c_mktsegment; name refers to c_name; address refers to c_address; phone number refers to c_phone; customer number 3 refers to c_custkey = 3\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 c_mktsegment, c_name, c_address, c_phone FROM customer WHERE c_custkey = 3"}],"db_id":"retails"}731{"prompt":"How many matches were held at the venue named \"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        How many matches were held at the venue named \"Newlands\"?\n        \n        ### Context:\n        #### Query Context:\n        the venue named \"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 SUM(CASE WHEN T2.Venue_Name = 'Newlands' THEN 1 ELSE 0 END) FROM Match AS T1 INNER JOIN Venue AS T2 ON T2.Venue_Id = T1.Venue_Id"}],"db_id":"soccer_2016"}732{"prompt":"Please list the IDs of all the faculty employees who teaches a basic or medium 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        Please list the IDs of all the faculty employees who teaches a basic or medium undergraduate course.\n        \n        ### Context:\n        #### Query Context:\n        faculty employees refers to hasPosition = 'Faculty_eme'; basic or medium undergraduate course refers to courseLevel = 'Level_300'\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 INNER JOIN person AS T3 ON T3.p_id = T2.p_id WHERE T1.courseLevel = 'Level_300' AND T3.hasPosition = 'Faculty_eme'"}],"db_id":"computer_student"}733{"prompt":"Find the total production amount and product names which had \"10 - 500 g pkgs.\" as quantity 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        Find the total production amount and product names which had \"10 - 500 g pkgs.\" as quantity per unit.\n        \n        ### Context:\n        #### Query Context:\n        total production amount refers to ADD(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 UnitsInStock + UnitsOnOrder, ProductName FROM Products WHERE QuantityPerUnit = '10 - 500 g pkgs.'"}],"db_id":"retail_world"}734{"prompt":"Provide the number of orders by Daisey Lamball in 2021.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 orders by Daisey Lamball in 2021.\n        \n        ### Context:\n        #### Query Context:\n        in 2021 refers to order_date LIKE '2021%'\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 cust_order AS T1 INNER JOIN customer AS T2 ON T1.customer_id = T2.customer_id WHERE T2.first_name = 'Daisey' AND T2.last_name = 'Lamball' AND STRFTIME('%Y', T1.order_date) = '2021'"}],"db_id":"books"}735{"prompt":"In male customers ages from 30 to 50, how many of them has an income ranges from 2000 to 2300?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 male customers ages from 30 to 50, how many of them has an income ranges from 2000 to 2300?\n        \n        ### Context:\n        #### Query Context:\n        male customers ages from 30 to 50 refer to SEX = 'Male' where age BETWEEN 30 AND 50; income ranges from 2000 to 2300 refers to INCOME_K BETWEEN 2000 AND 3000;\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 T1.SEX = 'Male' AND T1.age >= 30 AND T1.age <= 50 AND T2.INCOME_K >= 2000 AND T2.INCOME_K <= 2300"}],"db_id":"software_company"}736{"prompt":"What is the contact form of the legislator named Rick Crawford?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 form of the legislator named Rick Crawford?\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 T2.contact_form FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.official_full_name = 'Rick Crawford'"}],"db_id":"legislator"}737{"prompt":"What is Walt Disney Pictures' most popular 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 Walt Disney Pictures' most popular movie?\n        \n        ### Context:\n        #### Query Context:\n        Walt Disney Pictures refers to company_name = 'Walt Disney Pictures'; most popular movie 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 = 'Walt Disney Pictures' ORDER BY T3.popularity DESC LIMIT 1"}],"db_id":"movies_4"}738{"prompt":"What is the longest time for a student from the navy department to be 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        What is the longest time for a student from the navy department to be absent from school?\n        \n        ### Context:\n        #### Query Context:\n        navy department refers to organ = 'navy'; longest time to be absent from school refers to MAX(month)\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.month FROM longest_absense_from_school AS T1 INNER JOIN enlist AS T2 ON T1.`name` = T2.`name` WHERE T2.organ = 'navy' ORDER BY T1.`month` DESC LIMIT 1"}],"db_id":"student_loan"}739{"prompt":"What proportion of work order is in Subassembly?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 proportion of work order is in Subassembly?\n        \n        ### Context:\n        #### Query Context:\n        proportion = DIVIDE(SUM(Name = 'Subassembly'). (COUNT(WorkOrderID)));\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, 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'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, 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'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 100.0 * SUM(CASE WHEN T1.Name = 'Subassembly' THEN 1 ELSE 0 END) \/ COUNT(T2.WorkOrderID) FROM Location AS T1 INNER JOIN WorkOrderRouting AS T2 ON T1.LocationID = T2.LocationID"}],"db_id":"works_cycles"}740{"prompt":"What is solution 1's processing time and how many methods have been using this solution?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 solution 1's processing time and how many methods have been using this solution?\n        \n        ### Context:\n        #### Query Context:\n        solution refers to Solution.Id and SolutionId; Solution.Id = 1; SolutionId = 1; processing time refers to ProcessedTime; methods refers to Name;\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.ProcessedTime, COUNT(T2.SolutionId) FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.SolutionId = 1"}],"db_id":"codebase_comments"}741{"prompt":"How many male students are enrolled at OCC?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 students are enrolled at OCC?\n        \n        ### Context:\n        #### Query Context:\n        male students are mentioned in male.name; OCC refers to school = 'occ';\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 enrolled AS T1 INNER JOIN male AS T2 ON T1.name = T2.name WHERE T1.school = 'occ'"}],"db_id":"student_loan"}742{"prompt":"How many Olympic events did Michael Phelps II join in total? Find the percentage of the events where he won a gold medal.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Olympic events did Michael Phelps II join in total? Find the percentage of the events where he won a gold medal.\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(event_id where medal_name = 'Gold'), COUNT(event_id)) as percentage where full_name = 'Michael Fred Phelps, II';\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(T3.event_id) , CAST(COUNT(CASE WHEN T4.id = '1' THEN 1 ELSE NULL END) AS REAL) * 100 \/ COUNT(T4.id) FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id INNER JOIN competitor_event AS T3 ON T2.id = T3.competitor_id INNER JOIN medal AS T4 ON T3.medal_id = T4.id WHERE T1.full_name = 'Michael Fred Phelps, II'"}],"db_id":"olympics"}743{"prompt":"What is the difference between the number of matches where SC Ganguly played as a Captain and those matches where he played other roles?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 between the number of matches where SC Ganguly played as a Captain and those matches where he played other roles?\n        \n        ### Context:\n        #### Query Context:\n        SC Ganguly refers to Player_Name = 'SC Ganguly'; played as a Captain refers to Role_Id = 1; played other roles refers to Role_Id > 1; difference refers to SUBTRACT(COUNT(Role_Id = 1), COUNT(Role_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 SUM(CASE WHEN T3.Role_Id = 1 THEN 1 ELSE 0 END) - SUM(CASE WHEN T3.Role_Id > 1 THEN 1 ELSE 0 END) FROM Player_Match AS T1 INNER JOIN Player AS T2 ON T1.Player_Id = T2.Player_Id INNER JOIN Rolee AS T3 ON T1.Role_Id = T3.Role_Id WHERE T2.Player_Name = 'SC Ganguly'"}],"db_id":"soccer_2016"}744{"prompt":"How many object elements are there on average in each 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        How many object elements are there on average in each image?\n        \n        ### Context:\n        #### Query Context:\n        object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_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_CLASS_ID) AS REAL) \/ COUNT(DISTINCT IMG_ID) FROM IMG_OBJ"}],"db_id":"image_and_language"}745{"prompt":"State the number of positive tweets from Ha Noi.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 positive tweets from Ha Noi.\n        \n        ### Context:\n        #### Query Context:\n        positive tweet refers to Sentiment > 0; 'Ha Noi' is the State\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(T1.TweetID) FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID WHERE T1.Sentiment > 0 AND T2.State = 'Ha Noi'"}],"db_id":"social_media"}746{"prompt":"What is the maximum weight being transported to New York during a single 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 is the maximum weight being transported to New York during a single shipment?\n        \n        ### Context:\n        #### Query Context:\n        \"New York\" is the city_name; maximum weight refers to Max(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 MAX(T1.weight) FROM shipment AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.city_id WHERE T2.city_name = 'New York'"}],"db_id":"shipping"}747{"prompt":"What are the total products value shipped to Brazil by Speedy Express 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 total products value shipped to Brazil by Speedy Express Company?\n        \n        ### Context:\n        #### Query Context:\n        shipped to Brazil refers to ShipCountry = 'Brazil'; by Speedy Express Company refers to CompanyName = 'Speedy Express'; total products value refers to sum(MULTIPLY(UnitPrice, 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 SUM(T2.Quantity * T2.UnitPrice) FROM Orders AS T1 INNER JOIN `Order Details` AS T2 ON T1.OrderID = T2.OrderID INNER JOIN Shippers AS T3 ON T1.ShipVia = T3.ShipperID WHERE T3.CompanyName = 'Speedy Express' AND T1.ShipCountry = 'Brazil'"}],"db_id":"retail_world"}748{"prompt":"What was the total unit sold for item 10 when the average temperature was below the median 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        What was the total unit sold for item 10 when the average temperature was below the median temperature?\n        \n        ### Context:\n        #### Query Context:\n        item 10 refers to item_nbr = 10; average temperature below median temperature refers to tavg < avg(tavg); total units refers to Sum(units)\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(T5.units) FROM weather AS T4 INNER JOIN sales_in_weather AS T5 ON T4.`date` = T5.`date` INNER JOIN relation AS T6 ON T5.store_nbr = T6.store_nbr WHERE T5.item_nbr = 10 AND T4.tavg < ( SELECT AVG(T1.tavg) FROM weather AS T1 INNER JOIN sales_in_weather AS T2 ON T1.`date` = T2.`date` INNER JOIN relation AS T3 ON T2.store_nbr = T3.store_nbr WHERE T2.item_nbr = 10 )"}],"db_id":"sales_in_weather"}749{"prompt":"What are the last updated date for English film titles 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 last updated date for English film titles that were released in 2006?\n        \n        ### Context:\n        #### Query Context:\n        the last updated date refers to last_update; English is name of language; released in 2006 refers to release_year = 2006\n\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 DISTINCT T1.last_update FROM film AS T1 INNER JOIN `language` AS T2 ON T1.language_id = T2.language_id WHERE T2.`name` = 'English' AND T1.release_year = 2006"}],"db_id":"movie_3"}750{"prompt":"What is the profit of the product with the highest list price and of the product with the lowest list price other than 0? Indicates the depth the component is from its parent.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 of the product with the highest list price and of the product with the lowest list price other than 0? Indicates the depth the component is from its parent.\n        \n        ### Context:\n        #### Query Context:\n        profit = subtract(ListPrice, StandardCost); the depth the component from its parent refers to BOMLevel;\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, 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'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, 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'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 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'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 ( SELECT ListPrice - StandardCost FROM Product WHERE ListPrice != 0 ORDER BY ListPrice DESC LIMIT 1 ) , ( SELECT ListPrice - StandardCost FROM Product WHERE ListPrice != 0 ORDER BY ListPrice LIMIT 1 )"}],"db_id":"works_cycles"}751{"prompt":"Among the author included in the paper of \"Inspection resistant memory: Architectural support for security from physical examination\", write down the author name and ID who were affiliated with Microsoft Research, 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        Among the author included in the paper of \"Inspection resistant memory: Architectural support for security from physical examination\", write down the author name and ID who were affiliated with Microsoft Research, USA.\n        \n        ### Context:\n        #### Query Context:\n        \"Inspection resistant memory: Architectural support for security from physical examination\" is the title of paper;\u00a0 'Microsoft Research, USA' is the Affiliation\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, T1.Id FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId WHERE T1.Title = 'Inspection resistant memory: Architectural support for security FROM physical examination' AND T2.Affiliation = 'Microsoft Research, USA'"}],"db_id":"authors"}752{"prompt":"Who is the head of state of the country where the city of Pyongyang is under?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 state of the country where the city of Pyongyang is under?\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.HeadOfState FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode WHERE T2.Name = 'Pyongyang'"}],"db_id":"world"}753{"prompt":"List the PG-13 romantic comedy movie titles and their release dates.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 PG-13 romantic comedy movie titles and their release dates.\n        \n        ### Context:\n        #### Query Context:\n        PG-13 refers to MPAA_rating = 'PG-13'; romantic comedy refers to genre = 'Romantic Comedy';\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 movie_title, release_date FROM movies_total_gross WHERE MPAA_rating = 'PG-13' AND genre = 'Romantic Comedy'"}],"db_id":"disney"}754{"prompt":"Among the students with high salary, what is total number of students with a GPA higher 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        Among the students with high salary, what is total number of students with a GPA higher than 3?\n        \n        ### Context:\n        #### Query Context:\n        high salary refers to salary = 'high'; GPA higher than 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(T1.student_id) FROM RA AS T1 INNER JOIN student AS T2 ON T1.student_id = T2.student_id WHERE T1.salary = 'high' AND T2.gpa > 3"}],"db_id":"cs_semester"}755{"prompt":"Which artist has id \"16\"? Provide her or his tag 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 artist has id \"16\"? Provide her or his tag genre.\n        \n        ### Context:\n        #### Query Context:\n        FALSE;\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 T2.tag FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T1.id = 16"}],"db_id":"music_tracker"}756{"prompt":"How many flights from Dallas to Santa Ana departed on 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 flights from Dallas to Santa Ana departed on time?\n        \n        ### Context:\n        #### Query Context:\n        from Dallas refers to ORIGIN = 'DFW'; to Santa Ana refers to DEST = 'SNA'; departed on time 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 COUNT(*) FROM Airlines WHERE DEST = 'SNA' AND ORIGIN = 'DFW' AND DEP_DELAY = 0"}],"db_id":"airline"}757{"prompt":"How many gold medals does Henk Jan Zwolle 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 gold medals does Henk Jan Zwolle have?\n        \n        ### Context:\n        #### Query Context:\n        gold medals refer to medal_name = 'Gold';\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(T1.id) FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id INNER JOIN competitor_event AS T3 ON T2.id = T3.competitor_id INNER JOIN medal AS T4 ON T3.medal_id = T4.id WHERE T1.full_name = 'Henk Jan Zwolle' AND T4.medal_name = 'Gold'"}],"db_id":"olympics"}758{"prompt":"How much is the salary of the first ever employee that was hired?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 salary of the first ever employee that was hired?\n        \n        ### Context:\n        #### Query Context:\n        first-ever employee that was hired refers to MIN(hiredate)\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 salary FROM employee ORDER BY hiredate ASC LIMIT 1"}],"db_id":"human_resources"}759{"prompt":"In Argentina, how many universities 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        In Argentina, how many universities are there?\n        \n        ### Context:\n        #### Query Context:\n        In Argentina refers to country_name = 'Argentina';\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 COUNT(*) FROM university AS T1 INNER JOIN country AS T2 ON T1.country_id = T2.id WHERE T2.country_name = 'Argentina'"}],"db_id":"university"}760{"prompt":"Who is the user who created the list titled 'Sound and Vision'? Was he a subcriber 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        Who is the user who created the list titled 'Sound and Vision'? Was he a subcriber when he created the list?\n        \n        ### Context:\n        #### Query Context:\n        list titled 'Sound and Vision' refers to list_title = 'Sound and Vision'; user_subscriber = 1 means the user was a subscriber when he rated the movie; user_subscriber = 0 means the user was not a subscriber when he rated the movie\n\n\n\n\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.user_id, T1.user_subscriber FROM lists_users AS T1 INNER JOIN lists AS T2 ON T1.list_id = T2.list_id WHERE T2.list_title LIKE 'Sound and Vision'"}],"db_id":"movie_platform"}761{"prompt":"How many postal points are there under the congress representative from the House of Representatives in Mississippi?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 postal points are there under the congress representative from the House of Representatives in Mississippi?\n        \n        ### Context:\n        #### Query Context:\n        postal points refer to zip_code; Mississippi is the name of the state, in which name = 'Mississippi';\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.state = 'Mississippi'"}],"db_id":"address"}762{"prompt":"What is the opening time of the active businesses in Chandler that has 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 opening time of the active businesses in Chandler that has a medium review count.\n        \n        ### Context:\n        #### Query Context:\n        active businesses refers to active = 'true'; in Chandler refers to city = 'Chandler'\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.opening_time 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 'Chandler' AND T1.active LIKE 'TRUE' AND T1.review_count LIKE 'Medium'"}],"db_id":"public_review_platform"}763{"prompt":"How many 1 star ratings are there in the worst rated episode of the 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 1 star ratings are there in the worst rated episode of the season?\n        \n        ### Context:\n        #### Query Context:\n        worst rated episode refers to MIN(rating)\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 Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE T2.stars = 1 ORDER BY T1.rating LIMIT 1;"}],"db_id":"simpson_episodes"}764{"prompt":"When is the birthday of the actor who played \"Sully\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 birthday of the actor who played \"Sully\"?\n        \n        ### Context:\n        #### Query Context:\n        birthday refers to Date of Birth; \"Sully\" refers to Character Name = 'Sully'\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.`Date of Birth` FROM characters AS T1 INNER JOIN actor AS T2 ON T1.ActorID = T2.ActorID WHERE T1.`Character Name` = 'Sully'"}],"db_id":"movie"}765{"prompt":"List down the address of patients who have billable period in 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        List down the address of patients who have billable period in 2010.\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 DISTINCT T1.address FROM patients AS T1 INNER JOIN claims AS T2 ON T1.patient = T2.PATIENT WHERE T2.BILLABLEPERIOD LIKE '2010%'"}],"db_id":"synthea"}766{"prompt":"What is the code of Gwynedd 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        What is the code of Gwynedd State?\n        \n        ### Context:\n        #### Query Context:\n        code refers to StateCode\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 DISTINCT StateCode FROM location WHERE State = 'Gwynedd'"}],"db_id":"social_media"}767{"prompt":"The lake with the highest altitude is located in which 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        The lake with the highest altitude is located in which city?\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 FROM lake AS T1 LEFT JOIN located AS T2 ON T2.Lake = T1.Name ORDER BY T1.Altitude DESC LIMIT 1"}],"db_id":"mondial_geo"}768{"prompt":"What was the total number of the games that player Id \"rutlewa01\" played in 1967?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 total number of the games that player Id \"rutlewa01\" played in 1967?\n        \n        ### Context:\n        #### Query Context:\n        total number of the games refers to GP\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 GP FROM Goalies WHERE playerID = 'rutlewa01' AND year = 1967"}],"db_id":"hockey"}769{"prompt":"Among the reviews from midwest region, what are the products that received 1 star?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 reviews from midwest region, what are the products that received 1 star?\n        \n        ### Context:\n        #### Query Context:\n        1 star refers to 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 DISTINCT T3.Product FROM state AS T1 INNER JOIN district AS T2 ON T1.StateCode = T2.state_abbrev INNER JOIN reviews AS T3 ON T2.district_id = T3.district_id WHERE T1.Region = 'Midwest' AND T3.Stars = 1"}],"db_id":"retail_complains"}770{"prompt":"What is the event sponsored by the Republican House?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 event sponsored by the Republican House?\n        \n        ### Context:\n        #### Query Context:\n        sponsored by the Republican House refers to sponsor = 'REPUBLICAN HOUSE';\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 event FROM Menu WHERE sponsor = 'REPUBLICAN HOUSE'"}],"db_id":"menu"}771{"prompt":"How many publications were published in relation to the conference 'Adaptive Multimedia Retrieval' 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        How many publications were published in relation to the conference 'Adaptive Multimedia Retrieval' in 2007?\n        \n        ### Context:\n        #### Query Context:\n        'Adaptive Multimedia Retrieval is the FullName of paper; in 2007 refer to Year = 2007\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.ConferenceId) FROM Conference AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.ConferenceId WHERE T1.FullName = 'Adaptive Multimedia Retrieval' AND T2.Year = 2007"}],"db_id":"authors"}772{"prompt":"For the question \u201cWhat US state or territory do you work in?\u201d, how many people gave \"Kansas\" as the 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        For the question \u201cWhat US state or territory do you work in?\u201d, how many people gave \"Kansas\" as the answer?\n        \n        ### Context:\n        #### Query Context:\n        question refers to questiontext; AnswerText = 'Kansas'\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 'What US state or territory do you work in?' AND T1.AnswerText = 'Kansas'"}],"db_id":"mental_health_survey"}773{"prompt":"How many categories in total do the app users who were not active when event no.2 happened 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        How many categories in total do the app users who were not active when event no.2 happened belong to?\n        \n        ### Context:\n        #### Query Context:\n        not active refers to is_active = 0; event no. refers to event_id; event_id = 2;\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(*) FROM ( SELECT COUNT(DISTINCT T1.category) AS result FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id INNER JOIN app_events AS T3 ON T2.app_id = T3.app_id WHERE T3.event_id = 2 AND T3.is_active = 0 GROUP BY T1.category ) T"}],"db_id":"talkingdata"}774{"prompt":"Where was the ID 336005 match held? Please give me the venue and the 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        Where was the ID 336005 match held? Please give me the venue and the city.\n        \n        ### Context:\n        #### Query Context:\n        ID 336005 match refers to Match_Id = '336005'; venue refers to Venue_Name; city refers to City_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 T2.Venue_Name, T3.City_Name FROM `Match` AS T1 INNER JOIN Venue AS T2 ON T1.Venue_Id = T2.Venue_Id INNER JOIN City AS T3 ON T2.City_Id = T3.City_Id WHERE T1.Match_Id = '336005'"}],"db_id":"soccer_2016"}775{"prompt":"List down all of the sales IDs for sales handled by sales people with first name starting with alphabet \"s\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 sales IDs for sales handled by sales people with first name starting with alphabet \"s\".\n        \n        ### Context:\n        #### Query Context:\n        first name starting with alphabet \"s\" refers to FirstName LIKE 's%';\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.SalesID FROM Sales AS T1 INNER JOIN Employees AS T2 ON T1.SalesPersonID = T2.EmployeeID WHERE SUBSTR(T2.FirstName, 1, 1) = 's'"}],"db_id":"sales"}776{"prompt":"How many different types of crimes, according to the primary description, have occurred in the Hermosa neighborhood?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 types of crimes, according to the primary description, have occurred in the Hermosa neighborhood?\n        \n        ### Context:\n        #### Query Context:\n        \"Hermosa\" is the neighborhood_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 SUM(CASE WHEN T4.neighborhood_name = 'Hermosa' THEN 1 ELSE 0 END) FROM IUCR AS T1 INNER JOIN Crime AS T2 ON T2.iucr_no = T1.iucr_no INNER JOIN Community_Area AS T3 ON T3.community_area_no = T2.community_area_no INNER JOIN Neighborhood AS T4 ON T4.community_area_no = T3.community_area_no"}],"db_id":"chicago_crime"}777{"prompt":"List all the name of products with the ID of 30 to 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        List all the name of products with the ID of 30 to 40.\n        \n        ### Context:\n        #### Query Context:\n        products with the ID of 30 to 40 refer to Product Name WHERE ProductID BETWEEN 30 AND 40;\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 CASE  WHEN ProductID BETWEEN 30 AND 40 THEN `Product Name` ELSE NULL END AS T FROM Products ) WHERE T IS NOT NULL"}],"db_id":"regional_sales"}778{"prompt":"Please list any three criminal sexual assault cases against persons where the criminals have been arrested.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 criminal sexual assault cases against persons where the criminals have been arrested.\n        \n        ### Context:\n        #### Query Context:\n        \"Criminal Sexual Assault\" is the title of crime; against person refers to crime_against = 'Persons'; criminals have been arrested refers to arrest = 'TRUE'; cases refers to case_number\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 = 'Criminal Sexual Assault' AND T2.arrest = 'TRUE' AND T1.crime_against = 'Persons' LIMIT 3"}],"db_id":"chicago_crime"}779{"prompt":"What is the average price of games belonging in the arcade genre which has a content rating of Everyone 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        What is the average price of games belonging in the arcade genre which has a content rating of Everyone 10+?\n        \n        ### Context:\n        #### Query Context:\n        average price = AVG(Price);\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(Price) FROM playstore WHERE 'Content Rating' = 'Everyone 10+' AND Genres = 'Arcade'"}],"db_id":"app_store"}780{"prompt":"List all the attribute classes of the images that have a (5,5) 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        List all the attribute classes of the images that have a (5,5) coordinate.\n        \n        ### Context:\n        #### Query Context:\n        attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5;\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 T1.ATT_CLASS 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.X = 5 AND T3.Y = 5"}],"db_id":"image_and_language"}781{"prompt":"List the top 5 lowest rated puzzle games and count the number of negative sentiments the games received.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 top 5 lowest rated puzzle games and count the number of negative sentiments the games received.\n        \n        ### Context:\n        #### Query Context:\n        lowest rating refers to MIN(Rating); puzzle is the genre;\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 T1.App, COUNT(T1.App) COUNTNUMBER FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment = 'Negative' GROUP BY T1.App ORDER BY T1.Rating LIMIT 5"}],"db_id":"app_store"}782{"prompt":"For how many times had Elly Koss have her Systolic Blood Pressure observed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 had Elly Koss have her Systolic Blood Pressure observed?\n        \n        ### Context:\n        #### Query Context:\n        Systolic Blood Pressure refers to DESCRIPTION = 'Systolic Blood Pressure';\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.description) FROM patients AS T1 INNER JOIN observations AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Elly' AND T1.last = 'Koss' AND T2.description = 'Systolic Blood Pressure'"}],"db_id":"synthea"}783{"prompt":"Which business ID has the most 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        Which business ID has the most reviews?\n        \n        ### Context:\n        #### Query Context:\n        the most reviews refer to MAX(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 business_id FROM Reviews GROUP BY business_id ORDER BY COUNT(user_id) DESC LIMIT 1"}],"db_id":"public_review_platform"}784{"prompt":"Which trip had the longest duration? State the start and end station.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 trip had the longest duration? State the start and end station.\n        \n        ### Context:\n        #### Query Context:\n        start station refers to start_station_name; end station refers to end_station_name;\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 start_station_name, end_station_name FROM trip WHERE duration = ( SELECT MAX(duration) FROM trip )"}],"db_id":"bike_share_1"}785{"prompt":"For how many terms has current legislator Sherrod Brown served as a representative for district no.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        For how many terms has current legislator Sherrod Brown served as a representative for district no.13?\n        \n        ### Context:\n        #### Query Context:\n        district no. 13 refers to district = 13;\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 current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.official_full_name = 'Sherrod Brown' AND T2.district = 13"}],"db_id":"legislator"}786{"prompt":"What is the review with the title \"Hosts bring the show down\" 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        What is the review with the title \"Hosts bring the show down\" for?\n        \n        ### Context:\n        #### Query Context:\n        \"Hosts bring the show down\" refers to title of review\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 title FROM podcasts WHERE podcast_id = ( SELECT podcast_id FROM reviews WHERE title = 'Hosts bring the show down' )"}],"db_id":"music_platform_2"}787{"prompt":"Which position has a lower minimum salary, Account Representative or Trainee?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 position has a lower minimum salary, Account Representative or Trainee?\n        \n        ### Context:\n        #### Query Context:\n        position of Account Representative refers to positiontitle = 'Account Representative'; position of Trainee refers to positiontitle = 'Trainee'; lower minimum salary refers to MIN(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 positiontitle FROM position WHERE positiontitle = 'Account Representative' OR positiontitle = 'Trainee' ORDER BY minsalary ASC LIMIT 1"}],"db_id":"human_resources"}788{"prompt":"For the player who was drafted in the 1st round, 6th position in 1976, which team did he play in that 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        For the player who was drafted in the 1st round, 6th position in 1976, which team did he play in that year?\n        \n        ### Context:\n        #### Query Context:\n        drafted in the 1st round refers to draftRound = 1; 6th position refers to draftSelection = 6; in 1976 refers to year = 1976; team refers to tmID\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 draft AS T1 INNER JOIN teams AS T2 ON T1.tmID = T2.tmID AND T1.draftYear = T2.year WHERE T1.draftRound = 1 AND T1.draftSelection = 6 AND T1.draftYear = 1976"}],"db_id":"professional_basketball"}789{"prompt":"Which movie has the keyword 'extremis?'","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 has the keyword 'extremis?'\n        \n        ### Context:\n        #### Query Context:\n        Which movie refers to title; keyword 'extremis' refers to keyword_name = 'extremis'\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_keywords AS T2 ON T1.movie_id = T2.movie_id INNER JOIN keyword AS T3 ON T2.keyword_id = T3.keyword_id WHERE T3.keyword_name = 'extremis'"}],"db_id":"movies_4"}790{"prompt":"Among the students with payment due, how many of them are unemployed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 with payment due, how many of them are unemployed?\n        \n        ### Context:\n        #### Query Context:\n        with payment due refers to bool = 'pos';\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 no_payment_due AS T1 INNER JOIN unemployed AS T2 ON T2.name = T1.name WHERE T1.bool = 'pos'"}],"db_id":"student_loan"}791{"prompt":"In 2004, what are the names of the platforms where Codemasters publish its 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 2004, what are the names of the platforms where Codemasters publish its games?\n        \n        ### Context:\n        #### Query Context:\n        name of platform refers to platform_name; Codemasters refers to publisher_name = 'Codemasters'; in 2004 refers to release_year = 2004\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 T4.platform_name FROM publisher AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.publisher_id INNER JOIN game_platform AS T3 ON T2.id = T3.game_publisher_id INNER JOIN platform AS T4 ON T3.platform_id = T4.id WHERE T3.release_year = 2004 AND T1.publisher_name = 'Codemasters'"}],"db_id":"video_games"}792{"prompt":"What are the destinations of the flights with air carrier description \"Southeast Alaska Airlines: WEB\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 destinations of the flights with air carrier description \"Southeast Alaska Airlines: WEB\"?\n        \n        ### Context:\n        #### Query Context:\n        destinations refers to DEST; Southeast Alaska Airlines: WEB refers to Description = 'Southeast Alaska Airlines: WEB';\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.DEST FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Southeast Alaska Airlines: WEB'"}],"db_id":"airline"}793{"prompt":"State the inspection pass rate of Pockets Restaurant.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 inspection pass rate of Pockets Restaurant.\n        \n        ### Context:\n        #### Query Context:\n        Pockets refers to dba_name = 'POCKETS'; Restaurant refers to facility_type = 'Restaurant'; pass refers to results = 'Pass'; the inspection pass rate = divide(sum(inspection_id where results = 'Pass'), count(license_no)) where dba_name = 'POCKETS' and facility_type = 'Restaurant'\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 T2.results = 'Pass' THEN T2.inspection_id ELSE NULL END) AS REAL) * 100 \/ COUNT(T2.inspection_id) FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T1.dba_name = 'POCKETS' AND T1.facility_type = 'Restaurant'"}],"db_id":"food_inspection_2"}794{"prompt":"How many different clerks have served the customer with the address uFTe2u518et8Q8UC?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 clerks have served the customer with the address uFTe2u518et8Q8UC?\n        \n        ### Context:\n        #### Query Context:\n        clerk who have served the customer refers to o_clerk\n\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_clerk) FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey WHERE T2.c_address = 'uFTe2u518et8Q8UC'"}],"db_id":"retails"}795{"prompt":"How many of the users hold neutral attitude on \"10 Best Foods for You\" app and what category is this 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        How many of the users hold neutral attitude on \"10 Best Foods for You\" app and what category is this app?\n        \n        ### Context:\n        #### Query Context:\n        neutral attitude refers to Sentiment = 'Neutral';\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 COUNT(T2.App), T1.Category FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = '10 Best Foods for You' AND T2.Sentiment = 'Neutral'"}],"db_id":"app_store"}796{"prompt":"What is the category of the film Agent Truman?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the film Agent Truman?\n        \n        ### Context:\n        #### Query Context:\n        'Agent Truman' is a title of a film; category refers to 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 T3.name 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 T1.title = 'AGENT TRUMAN'"}],"db_id":"movie_3"}797{"prompt":"List the order for all in-store sales along with the products 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        List the order for all in-store sales along with the products sold.\n        \n        ### Context:\n        #### Query Context:\n        orders for all in-store sales refer to OrderNumber where Sales Channel = 'In-Store'; products refer 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 DISTINCT T1.OrderNumber, T2.`Product Name` FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID WHERE T1.`Sales Channel` = 'In-Store'"}],"db_id":"regional_sales"}798{"prompt":"What product was ordered in the Central region on April 26, 2018, and shipped by April 27, 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        What product was ordered in the Central region on April 26, 2018, and shipped by April 27, 2018?\n        \n        ### Context:\n        #### Query Context:\n        on April 26, 2018 refers to \"Order Date\" = date('2018-04-26'); shipped by April 27, 2018 refers to \"Ship Date\" = date('2018-04-27');\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.`Product Name` FROM central_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T1.`Order Date` = '2018-04-26' AND T1.`Ship Date` = '2018-04-27' AND T2.Region = 'Central'"}],"db_id":"superstore"}799{"prompt":"How many solutions are in \"https:\/\/github.com\/derickbailey\/presentations-and-training.git\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are in \"https:\/\/github.com\/derickbailey\/presentations-and-training.git\"?\n        \n        ### Context:\n        #### Query Context:\n        solutions refers to Solution.Id; https:\/\/github.com\/derickbailey\/presentations-and-training.git refers to Url; Url; = 'https:\/\/github.com\/derickbailey\/presentations-and-training.git';\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.Url = 'https:\/\/github.com\/derickbailey\/presentations-and-training.git'"}],"db_id":"codebase_comments"}800{"prompt":"What are the neighborhoods that are located in the North Center community 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 are the neighborhoods that are located in the North Center community area?\n        \n        ### Context:\n        #### Query Context:\n        \"North Center\" is the community_area_name; neighborhoods refers to neighborhood_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.neighborhood_name FROM Community_Area AS T1 INNER JOIN Neighborhood AS T2 ON T1.community_area_no = T2.community_area_no WHERE T1.community_area_name = 'North Center'"}],"db_id":"chicago_crime"}801{"prompt":"List at least 10 device models that male users over the age of 39 usually use.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 10 device models that male users over the age of 39 usually use.\n        \n        ### Context:\n        #### Query Context:\n        male refers to gender = 'M'; over the age of 39 refers to group = 'M39+';\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_model FROM phone_brand_device_model2 AS T1 INNER JOIN gender_age AS T2 ON T1.device_id = T2.device_id WHERE T2.`group` = 'M39+' AND T2.gender = 'M' LIMIT 10"}],"db_id":"talkingdata"}802{"prompt":"How many 2 stars rated business located in Phoenix, 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 2 stars rated business located in Phoenix, Arizona?\n        \n        ### Context:\n        #### Query Context:\n        located in Phoenix refers to city = 'Phoenix'; Arizona refers to state = 'AZ'\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 state = 'AZ' AND stars = 2"}],"db_id":"public_review_platform"}803{"prompt":"Among all the users who received the high number of compliments, what percent received the 'cute' type of compliment.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 users who received the high number of compliments, what percent received the 'cute' type of compliment.\n        \n        ### Context:\n        #### Query Context:\n        high number of compliments refers to number_of_compliments = 'High'; percentage = divide(count(user_id where compliment_type = 'cute'), count(user_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 T1.compliment_type = 'cute' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T2.user_id) FROM Compliments AS T1 INNER JOIN Users_Compliments AS T2 ON T1.compliment_id = T2.compliment_id WHERE T2.number_of_compliments = 'High'"}],"db_id":"public_review_platform"}804{"prompt":"Which region has the highest number of restaurants?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 number of restaurants?\n        \n        ### Context:\n        #### Query Context:\n        the highest number refers to max(count(id_restaurant))\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.region FROM geographic AS T1 INNER JOIN location AS T2 ON T1.city = T2.city GROUP BY T1.region ORDER BY COUNT(T2.id_restaurant) DESC LIMIT 1"}],"db_id":"restaurant"}805{"prompt":"List out years that only have summer 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        List out years that only have summer games.\n        \n        ### Context:\n        #### Query Context:\n        years refer to games_year; only have summer games refer to games_year where COUNT(games_name) = 1 and 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 games_year FROM games WHERE season != 'Winter' GROUP BY games_year HAVING COUNT(season) = 1"}],"db_id":"olympics"}806{"prompt":"Provide the dimensions of the bounding box that contains the keyboard that was spotted in image no. 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        Provide the dimensions of the bounding box that contains the keyboard that was spotted in image no. 3.\n        \n        ### Context:\n        #### Query Context:\n        dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3\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 T1.W, T1.H FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T1.IMG_ID = 3 AND T2.OBJ_CLASS = 'keyboard'"}],"db_id":"image_and_language"}807{"prompt":"How many images have \"picture\" as their 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        How many images have \"picture\" as their attribute class?\n        \n        ### Context:\n        #### Query Context:\n        images have \"picture\" as their attribute class refer to IMG_ID where ATT_CLASS = 'picture';\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(T2.IMG_ID) FROM ATT_CLASSES AS T1 INNER JOIN IMG_OBJ_ATT AS T2 ON T1.ATT_CLASS_ID = T2.ATT_CLASS_ID WHERE T1.ATT_CLASS = 'picture'"}],"db_id":"image_and_language"}808{"prompt":"What is the project in which 320 students will be impacted if the project is funded? Name the project and state the project 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        What is the project in which 320 students will be impacted if the project is funded? Name the project and state the project cost.\n        \n        ### Context:\n        #### Query Context:\n        320 students will be impacted refers to students_reached = 320; name the project refers to title; project cost refers tp total_price_excluding_optional_support\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, T2.total_price_excluding_optional_support FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.students_reached = 320"}],"db_id":"donor"}809{"prompt":"Where was shipment no. 1002 headed?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 was shipment no. 1002 headed?\n        \n        ### Context:\n        #### Query Context:\n        shipment no. 1002 refers to ship_id = 1002; where shipment was headed 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_id = '1002'"}],"db_id":"shipping"}810{"prompt":"Please describe the menu sponsored by Noviomagus physically.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 describe the menu sponsored by Noviomagus physically.\n        \n        ### Context:\n        #### Query Context:\n        describe the menu physically refers to physical_description; sponsored by Noviomagus refers to sponsor = 'Noviomagus';\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 physical_description FROM Menu WHERE sponsor = 'Noviomagus'"}],"db_id":"menu"}811{"prompt":"Which nation has the smallest population, and where is its capital 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        Which nation has the smallest population, and where is its capital 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 Name, Capital FROM country ORDER BY Population ASC LIMIT 1"}],"db_id":"mondial_geo"}812{"prompt":"Who is the one representing the company \"Heli Swaren GmbH & Co. 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        Who is the one representing the company \"Heli Swaren GmbH & Co. KG\"?\n        \n        ### Context:\n        #### Query Context:\n        Heli Swaren GmbH & Co. KG is the name of the company; who is representing 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 ContactName FROM Suppliers WHERE CompanyName = 'Heli Swaren GmbH & Co. KG'"}],"db_id":"retail_world"}813{"prompt":"How many papers are published under the conference \"Mathematics of Program Construction\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        How many papers are published under the conference \"Mathematics of Program Construction\n\"?\n        \n        ### Context:\n        #### Query Context:\n        'Mathematics of Program Construction' is the FullName of conference\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(T1.Id) FROM Paper AS T1 INNER JOIN Conference AS T2 ON T1.ConferenceId = T2.Id WHERE T2.FullName = 'Mathematics of Program Construction'"}],"db_id":"authors"}814{"prompt":"What is the average of the white population in the cities with area code 920?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 of the white population in the cities with area code 920?\n        \n        ### Context:\n        #### Query Context:\n        AVG(white_population) where area_code = 920;\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 AVG(T2.white_population) FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T1.area_code = 920"}],"db_id":"address"}815{"prompt":"Find the root beer with the most and least amount of profit per unit and list the container types in which these root beers are 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        Find the root beer with the most and least amount of profit per unit and list the container types in which these root beers are sold.\n        \n        ### Context:\n        #### Query Context:\n        most amount of profit per unit refers to MAX(SUBTRACT(CurrentRetailPrice, WholesaleCost)); least amount of profit per unit refers to MIN(SUBTRACT(CurrentRetailPrice, WholesaleCost));\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 * FROM ( SELECT T1.BrandName, T2.ContainerType FROM rootbeerbrand AS T1 INNER JOIN rootbeer AS T2 ON T1.BrandID = T2.BrandID ORDER BY T1.CurrentRetailPrice - T1.WholesaleCost DESC LIMIT 1 ) UNION ALL SELECT * FROM ( SELECT T3.BrandName, T4.ContainerType FROM rootbeerbrand AS T3 INNER JOIN rootbeer AS T4 ON T3.BrandID = T4.BrandID ORDER BY T3.CurrentRetailPrice - T3.WholesaleCost ASC LIMIT 1 )"}],"db_id":"beer_factory"}816{"prompt":"What is the average amount of rent that Christy Vargas paid?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 rent that Christy Vargas paid?\n        \n        ### Context:\n        #### Query Context:\n        'Christy Vargas' is a full name of a customer; full name refers to first_name, last_name; average amount of rent refers to AVG(amount)\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(T2.amount) FROM customer AS T1 INNER JOIN payment AS T2 ON T1.customer_id = T2.customer_id WHERE T1.first_name = 'CHRISTY' AND T1.Last_name = 'VARGAS'"}],"db_id":"movie_3"}817{"prompt":"Is (206) 555-1189 the home phone number for Laura Callahan?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 (206) 555-1189 the home phone number for Laura Callahan?\n        \n        ### Context:\n        #### Query Context:\n        \"Laura Callahan\" refers to FirstName = 'Laura AND LastName = 'Callahan\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 CASE WHEN HomePhone = '(206) 555-1189' THEN 'YES' ELSE 'NO' END FROM Employees WHERE FirstName = 'Laura' AND LastName = 'Callahan'"}],"db_id":"retail_world"}818{"prompt":"What is the difference in the number of restaurants that passed and failed the canvass inspection 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 difference in the number of restaurants that passed and failed the canvass inspection type?\n        \n        ### Context:\n        #### Query Context:\n        canvass inspection type refers to inspection_type = 'Canvass'; restaurant refers to facility_type = 'Restaurant'; difference = subtract(count(inspection_id where results = 'Pass'), count(inspection_id where results = 'Fail'))\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(CASE WHEN T2.results = 'Pass' THEN T1.license_no END) - COUNT(CASE WHEN T2.results = 'Fail' THEN T1.license_no END) AS diff FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T2.inspection_type = 'Canvass' AND T1.facility_type = 'Restaurant'"}],"db_id":"food_inspection_2"}819{"prompt":"How many wheels do the long cars 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 wheels do the long cars have?\n        \n        ### Context:\n        #### Query Context:\n        long cars refers to len = 'long'\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(wheels) FROM cars WHERE len = 'long'"}],"db_id":"trains"}820{"prompt":"Which film rated by user 59988436 that received 21 comments?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 film rated by user 59988436 that received 21 comments?\n        \n        ### Context:\n        #### Query Context:\n        user 59988436 refers to user_id = 59988436; received 21 comments refers to critic_comments = 21; film refers to movie;\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 T1.user_id = 59988436 AND T1.critic_comments = 21"}],"db_id":"movie_platform"}821{"prompt":"Among the countries that use Italian as their language, what is the percentage of republic 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 countries that use Italian as their language, what is the percentage of republic countries?\n        \n        ### Context:\n        #### Query Context:\n        use Italian as their language refers to `Language` = 'Italian'; percentage = MULTIPLY(DIVIDE(SUM(`Language` = 'Italian' WHERE GovernmentForm = 'Republic'), COUNT(`Language` = 'Italian')), 100); use Italian as their language refers to `Language` = 'Italian'; republic countries refers to GovernmentForm = 'Republic';\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 CAST(SUM(CASE WHEN T2.GovernmentForm = 'Republic' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(*) FROM CountryLanguage AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T1.Language = 'Italian'"}],"db_id":"world"}822{"prompt":"From 5\/30\/2005 at 3:43:54 AM to 7\/31\/2005 at 10:08:29 PM, how many times did Susan Wilson pay for film rentals?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 5\/30\/2005 at 3:43:54 AM to 7\/31\/2005 at 10:08:29 PM, how many times did Susan Wilson pay for film rentals?\n        \n        ### Context:\n        #### Query Context:\n        from 5\/30\/2005 at 3:43:54 AM to 7\/31\/2005 at 10:08:29 PM refers to payment_date between '2005-05-30 03:43:54' and '2005-07-31 10:08:29'\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.customer_id) FROM payment AS T1 INNER JOIN customer AS T2 ON T1.customer_id = T2.customer_id WHERE T1.payment_date BETWEEN '2005-05-30 03:43:54' AND '2005-07-31 10:08:29'"}],"db_id":"movie_3"}823{"prompt":"Show the name of the earliest platform 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        Show the name of the earliest platform in the database.\n        \n        ### Context:\n        #### Query Context:\n        the earliest refers to min(release_year); name of platform refers to platform_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 T2.platform_name FROM game_platform AS T1 INNER JOIN platform AS T2 ON T1.platform_id = T2.id ORDER BY T1.release_year ASC LIMIT 1"}],"db_id":"video_games"}824{"prompt":"Please list the countries that got the footnote \"Data are classified as official aid.\" on the series code DC.DAC.AUSL.CD in 2002.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 countries that got the footnote \"Data are classified as official aid.\" on the series code DC.DAC.AUSL.CD in 2002.\n        \n        ### Context:\n        #### Query Context:\n        countries are the Countrycode; footnote refers to Description = 'Data are classified as official aid'\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.SHORTNAME FROM Country AS T1 INNER JOIN FootNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T2.Description = 'Data are classified as official aid.' AND T2.Seriescode = 'DC.DAC.AUSL.CD' AND T2.Year LIKE '%2002%'"}],"db_id":"world_development_indicators"}825{"prompt":"Give the number of territories in the \"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        Give the number of territories in the \"Northern\" region.\n        \n        ### Context:\n        #### Query Context:\n        \"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 COUNT(T1.TerritoryID) FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID WHERE T2.RegionDescription = 'Northern'"}],"db_id":"retail_world"}826{"prompt":"What is the awarded category that the awarded character Lenny won?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 awarded category that the awarded character Lenny won?\n        \n        ### Context:\n        #### Query Context:\n        \n\nawarded category refers to award_category\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.award_category FROM Award AS T1 INNER JOIN Character_Award AS T2 ON T1.award_id = T2.award_id WHERE T2.character = 'Lenny';"}],"db_id":"simpson_episodes"}827{"prompt":"For the goalies whose weight are above 190, who had most goal againsts in 1978 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        For the goalies whose weight are above 190, who had most goal againsts in 1978 season?\n        \n        ### Context:\n        #### Query Context:\n        Weight of above 190 refers to weight >190; 1978 season refers to the year played\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'], 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[290, 13]]}\n        \n        ### Response:\n        "},{"role":"assistant","content":"SELECT T1.playerID FROM Goalies AS T1 INNER JOIN Master AS T2 ON T1.playerID = T2.playerID WHERE T1.year = '1978' AND T2.weight > 190 ORDER BY T1.GA DESC LIMIT 1"}],"db_id":"hockey"}828{"prompt":"Among the salable products from the mountain product line, how many of them have the most 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        Among the salable products from the mountain product line, how many of them have the most reviews?\n        \n        ### Context:\n        #### Query Context:\n        salable product refers to FinishedGoodsFlag = 1; mountain product line refers to 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', 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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 athletes from region 151 have won a medal?\n        \n        ### Context:\n        #### Query Context:\n        athletes refer to person_id; region 151 refers to region_id = 151; won a medal refers to 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 COUNT(T3.person_id) FROM competitor_event AS T1 INNER JOIN games_competitor AS T2 ON T1.competitor_id = T2.id INNER JOIN person_region AS T3 ON T2.person_id = T3.person_id WHERE T3.region_id = 151 AND T1.medal_id != 4"}],"db_id":"olympics"}830{"prompt":"List the order IDs, product IDs and unit price of orders which total payment is greater than 15000.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 order IDs, product IDs and unit price of orders which total payment is greater than 15000.\n        \n        ### Context:\n        #### Query Context:\n        total payment is greater than 15000 refers to MULTIPLY((Quantity * UnitPrice * (1 - Discount))) > 15000\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 ProductID, OrderID, UnitPrice FROM `Order Details` WHERE UnitPrice * Quantity * (1 - Discount) > 15000"}],"db_id":"retail_world"}831{"prompt":"How many Italian patients have the care plan code of 304510005?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Italian patients have the care plan code of 304510005?\n        \n        ### Context:\n        #### Query Context:\n        Italian patients refers to ethnicity = 'italian';\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 careplans AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T2.ethnicity = 'italian' AND T1.CODE = '304510005'"}],"db_id":"synthea"}832{"prompt":"Name the title of the episode that was nominated for Emmy's Outstanding Animated Program 21 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        Name the title of the episode that was nominated for Emmy's Outstanding Animated Program 21 times.\n        \n        ### Context:\n        #### Query Context:\n        nominated for refers to result = 'Nominee'; Outstanding Animated Program refers to award = 'Outstanding Animated Program (For Programming Less Than One Hour)'; Emmy's refers to organization = 'Primetime Emmy Awards'; 21 times refers to COUNT(episode_id) = 21;\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.title FROM Award AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T1.organization = 'Primetime Emmy Awards' AND T1.award = 'Outstanding Animated Program (For Programming Less Than One Hour)' AND T1.result = 'Nominee' GROUP BY T1.episode_id HAVING COUNT(T1.episode_id) = 21;"}],"db_id":"simpson_episodes"}833{"prompt":"Provide the contact email of Moss Zarb.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 contact email of Moss Zarb.\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 email FROM customer WHERE first_name = 'Moss' AND last_name = 'Zarb'"}],"db_id":"books"}834{"prompt":"Mention the titile of paper writen by Joe Lograsso.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Mention the titile of paper writen by Joe Lograsso.\n        \n        ### Context:\n        #### Query Context:\n        'Joe Lograsso' is name of paper 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 T1.Title FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId WHERE T2.Name = 'Joe Lograsso'"}],"db_id":"authors"}835{"prompt":"What are the match IDs that were played at Brabourne Stadium?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 match IDs that were played at Brabourne Stadium?\n        \n        ### Context:\n        #### Query Context:\n        at Brabourne Stadium refers to Venue_Name = 'Brabourne Stadium'\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.Match_Id FROM Match AS T1 INNER JOIN Venue AS T2 ON T2.Venue_Id = T1.Venue_Id WHERE T2.Venue_Name = 'Brabourne Stadium'"}],"db_id":"soccer_2016"}836{"prompt":"Who was the man of the series in 2013? 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 was the man of the series in 2013? Give the full name.\n        \n        ### Context:\n        #### Query Context:\n        full name refers to Player_Name; in 2013 refers to Season_Year = 2013\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.Player_Name FROM Season AS T1 INNER JOIN Player AS T2 ON T1.Man_of_the_Series = T2.Player_Id WHERE T1.Season_Year = 2013"}],"db_id":"soccer_2016"}837{"prompt":"Which category does the film Working Microcosmos 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        Which category does the film Working Microcosmos belong to?\n        \n        ### Context:\n        #### Query Context:\n        \"WORKING MICROCOSMOS\" is the title of film; category refers to 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 T3.name 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 T1.title = 'WORKING MICROCOSMOS'"}],"db_id":"movie_3"}838{"prompt":"Which image has the highest number of \"white\" class 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        Which image has the highest number of \"white\" class attributes?\n        \n        ### Context:\n        #### Query Context:\n        \"white\" class attributes refers to ATT_CLASS = 'white'; highest number 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 T1.IMG_ID AS IMGID FROM IMG_OBJ_att AS T1 INNER JOIN ATT_CLASSES AS T2 ON T1.ATT_CLASS_ID = T2.ATT_CLASS_ID WHERE T2.ATT_CLASS = 'white' GROUP BY T1.IMG_ID ORDER BY COUNT(T1.ATT_CLASS_ID) DESC LIMIT 1"}],"db_id":"image_and_language"}839{"prompt":"Show the sea level status recorded by the weather station of store no.19 on 2013\/2\/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        Show the sea level status recorded by the weather station of store no.19 on 2013\/2\/24.\n        \n        ### Context:\n        #### Query Context:\n        store no.19 refers to store_nbr = 19; on 2013\/2\/24 refers to date = '2013-02-24'; sea level status refers to sealevel\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.sealevel FROM weather AS T1 INNER JOIN relation AS T2 ON T1.station_nbr = T2.station_nbr WHERE T1.`date` = '2013-02-24' AND T2.store_nbr = 19"}],"db_id":"sales_in_weather"}840{"prompt":"How many legislators have not been registered in Federal Election Commission data?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 legislators have not been registered in Federal Election Commission data?\n        \n        ### Context:\n        #### Query Context:\n        have not been registered in Federal Election Commission data refers to fec_id is null OR fec_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 COUNT(*) FROM current WHERE fec_id IS NULL OR fec_id = ''"}],"db_id":"legislator"}841{"prompt":"Calculate the average weight in pounds of all players drafted by Arizona Coyotes.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 weight in pounds of all players drafted by Arizona Coyotes.\n        \n        ### Context:\n        #### Query Context:\n        average weight in pounds = AVG(weight_in_lbs); weight in pounds refers to weight_in_lbs; players refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes';\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(SUM(T1.weight_in_lbs) AS REAL) \/ COUNT(T2.ELITEID) FROM weight_info AS T1 INNER JOIN PlayerInfo AS T2 ON T1.weight_id = T2.weight WHERE T2.overallby = 'Arizona Coyotes'"}],"db_id":"ice_hockey_draft"}842{"prompt":"Which sales representatives in New York city whose leader is Anthony Bow with the employee number is 1143? Indicate their employee numbers.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 representatives in New York city whose leader is Anthony Bow with the employee number is 1143? Indicate their employee numbers.\n        \n        ### Context:\n        #### Query Context:\n        reportsTO' is the leader of the 'employeeNumber';\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.employeeNumber FROM employees AS T1 INNER JOIN offices AS T2 ON T1.officeCode = T2.officeCode WHERE T1.reportsTo = 1143 AND T2.city = 'NYC'"}],"db_id":"car_retails"}843{"prompt":"Calculate the total quantity of products that are gifts.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 quantity of products that are gifts.\n        \n        ### Context:\n        #### Query Context:\n        total quantity = SUM(Quantity); gifts refers to Price = 0;\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.Price = 0"}],"db_id":"sales"}844{"prompt":"Please list the case numbers of all the crimes with no arrest made in Central 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        Please list the case numbers of all the crimes with no arrest made in Central Chicago.\n        \n        ### Context:\n        #### Query Context:\n        no arrest made refers to arrest = 'FALSE'; 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 COUNT(*) FROM Crime AS T1 INNER JOIN District AS T2 ON T1.district_no = T2.district_no WHERE T2.district_name = 'Central' AND T1.arrest = 'FALSE'"}],"db_id":"chicago_crime"}845{"prompt":"List the ID and years in program for students taught by advisor with ID 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        List the ID and years in program for students taught by advisor with ID 5.\n        \n        ### Context:\n        #### Query Context:\n        advisor with ID 5 refers to p_id_dummy = 5\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.yearsInProgram FROM advisedBy AS T1 INNER JOIN person AS T2 ON T1.p_id = T2.p_id WHERE T1.p_id_dummy = 5"}],"db_id":"computer_student"}846{"prompt":"Lists all genes by identifier number located in the cytoplasm and whose function is metabolism.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, and the schema of one or more tables. \n\n        You must output the SQL query that answers the question.\n        \n        ### Input:\n        Lists all genes by identifier number located in the cytoplasm and whose function is metabolism.\n        \n        ### Context:\n        #### Query Context:\n        \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 DISTINCT GeneID FROM Genes WHERE Localization = 'cytoplasm' AND Function = 'METABOLISM'"}],"db_id":"genes"}847{"prompt":"Calculate the average amount of payments made by customers during the first half of 2004.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 amount of payments made by customers during the first half of 2004.\n        \n        ### Context:\n        #### Query Context:\n        average amount of payments = DIVIDE(SUM(amount), COUNT(customerNumber); first half of 2014 refers to paymentDate > = '2004-01-01' AND paymentDate < '2004-07-01;\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 AVG(amount) FROM payments WHERE paymentDate BETWEEN '2004-01-01' AND '2004-06-30'"}],"db_id":"car_retails"}848{"prompt":"What's the value of the indicator whose long definition is \"Adolescent fertility rate is the number of births per 1,000 women ages 15-19.\" for the Arab World in 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's the value of the indicator whose long definition is \"Adolescent fertility rate is the number of births per 1,000 women ages 15-19.\" for the Arab World in 1960?\n        \n        ### Context:\n        #### Query Context:\n        in 1960 refers to Year = 1960; for the Arab World refers to CountryName = 'Arab World'\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 Series AS T2 ON T1.IndicatorName = T2.IndicatorName INNER JOIN Country AS T3 ON T1.CountryCode = T3.CountryCode WHERE T2.LongDefinition = 'Adolescent fertility rate is the number of births per 1,000 women ages 15-19.' AND T3.ShortName = 'Arab World' AND T1.Year = 1960"}],"db_id":"world_development_indicators"}849{"prompt":"On the day with the highest max temperature in 2012, how many items in store no.3 had no 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        On the day with the highest max temperature in 2012, how many items in store no.3 had no sales?\n        \n        ### Context:\n        #### Query Context:\n        highest max temperature refers to Max(tmax); in 2012 refers to SUBSTR(date, 1, 4) = '2012'; store no.3 refers to store_nbr = 3; had no sale refers to units = 0\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 COUNT(DISTINCT 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 AND T1.store_nbr = 3 AND SUBSTR(T1.`date`, 1, 4) = '2012' AND T1.units = 0 GROUP BY T3.tmax ORDER BY T3.tmax DESC LIMIT 1"}],"db_id":"sales_in_weather"}850{"prompt":"How many critics were given to the movie that got the most movie popularity 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        How many critics were given to the movie that got the most movie popularity number.\n        \n        ### Context:\n        #### Query Context:\n        most movie popularity number refers to MAX(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 COUNT(T1.critic) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_popularity = ( SELECT MAX(movie_popularity) FROM movies )"}],"db_id":"movie_platform"}851{"prompt":"Among the solutions that contain files within the repository needing the longest processed time to download, how many of them doesn't need to be\ncompiled if user wants to implement 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        Among the solutions that contain files within the repository needing the longest processed time to download, how many of them doesn't need to be\ncompiled if user wants to implement it?\n        \n        ### Context:\n        #### Query Context:\n        longest processed time refers to max(Solution.ProcessedTime); needs to be compiled if user wants to implement it refers to WasCompiled = 0\uff1b\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.ProcessedTime = ( SELECT MAX(ProcessedTime) FROM Repo ) AND T2.WasCompiled = 1"}],"db_id":"codebase_comments"}852{"prompt":"How many flights operated by American Airlines Inc. on 2018\/8\/1 were faster than scheduled?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 flights operated by American Airlines Inc. on 2018\/8\/1 were faster than scheduled?\n        \n        ### Context:\n        #### Query Context:\n        American Airlines Inc. refers to Description = 'American Airlines Inc.: AA'; on 2018\/8\/1 refers to FL_DATE = '2018\/8\/1'; faster than scheduled refers to ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME;\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.ACTUAL_ELAPSED_TIME < CRS_ELAPSED_TIME THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN INNER JOIN `Air Carriers` AS T3 ON T2.OP_CARRIER_AIRLINE_ID = T3.Code WHERE T2.FL_DATE = '2018\/8\/1' AND T3.Description = 'American Airlines Inc.: AA'"}],"db_id":"airline"}853{"prompt":"What is the elevation of the city belonging to Hampden, Massachusetts?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 elevation of the city belonging to Hampden, Massachusetts?\n        \n        ### Context:\n        #### Query Context:\n        the county of Hampden is located in the state of Massachusetts.\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 T3.elevation FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state INNER JOIN zip_data AS T3 ON T2.zip_code = T3.zip_code WHERE T1.name = 'Massachusetts' AND T2.county = 'HAMPDEN' GROUP BY T3.elevation"}],"db_id":"address"}854{"prompt":"What is the full address of the customers who, having received a timely response from the company, have dispute about that 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        What is the full address of the customers who, having received a timely response from the company, have dispute about that response?\n        \n        ### Context:\n        #### Query Context:\n        full address = address_1, address_2; received a timely response refers to Timely response? = 'Yes'; have dispute refers to \"Consumer disputed?\" = 'Yes';\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.address_1, T1.address_2 FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.`Timely response?` = 'Yes' AND T2.`Consumer disputed?` = 'Yes'"}],"db_id":"retail_complains"}855{"prompt":"List down the sales, profit, and subcategories of the product ordered in the order ID US-2011-126571 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        List down the sales, profit, and subcategories of the product ordered in the order ID US-2011-126571 in the East 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 T1.Sales, T1.Profit, T2.`Sub-Category` FROM east_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T1.`Order ID` = 'US-2011-126571' AND T2.Region = 'East'"}],"db_id":"superstore"}856{"prompt":"How much is her GDP in agriculture for the country with the least 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        How much is her GDP in agriculture for the country with the least area?\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.GDP * T2.Agriculture FROM country AS T1 INNER JOIN economy AS T2 ON T1.Code = T2.Country ORDER BY T1.Area ASC LIMIT 1"}],"db_id":"mondial_geo"}857{"prompt":"How many votes did the episode titled Juvenile 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        How many votes did the episode titled Juvenile get?\n        \n        ### Context:\n        #### Query Context:\n        the episode titled Juvenile refers to title = 'Juvenile'\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 = 'Juvenile'"}],"db_id":"law_episode"}858{"prompt":"Calculate the average review star from users in businesses located in South Carolina and 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        Calculate the average review star from users in businesses located in South Carolina and California state.\n        \n        ### Context:\n        #### Query Context:\n        \"South Carolina\" and \"California\" are both state; average review stars from users = Divide((Sum(review_stars(state = 'SC')) + Sum(review_stars(state = 'CA'))), Sum(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 1.0 * (( SELECT SUM(T1.stars) FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id WHERE T1.state = 'SC' ) + ( SELECT SUM(T1.stars) FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id WHERE T1.state = 'CA' )) \/ ( SELECT SUM(T1.stars) FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id ) AS reslut"}],"db_id":"public_review_platform"}859{"prompt":"List all the titles and year to date sales by author who are not on contract.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 and year to date sales by author who are not on contract.\n        \n        ### Context:\n        #### Query Context:\n        year to date sales refers to ytd_sales; not on contract refers to contract = 0\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_id, T1.ytd_sales FROM titles AS T1 INNER JOIN titleauthor AS T2 ON T1.title_id = T2.title_id INNER JOIN authors AS T3 ON T2.au_id = T3.au_id WHERE T3.contract = 0"}],"db_id":"book_publishing_company"}860{"prompt":"Please list the top three shortest black 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        Please list the top three shortest black players.\n        \n        ### Context:\n        #### Query Context:\n        shortest player refers to Min(height); black players refers to race = 'B'\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 firstName, lastName FROM players WHERE race = 'B' AND height > 0 ORDER BY height ASC LIMIT 3"}],"db_id":"professional_basketball"}861{"prompt":"Provide the name of the artist who released his or her Single-Table in 2012 with the highest number of downloads. Name the Single-Table title as well.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 the artist who released his or her Single-Table in 2012 with the highest number of downloads. Name the Single-Table title as well.\n        \n        ### Context:\n        #### Query Context:\n        title refers to groupName; the highest number of downloads refers to MAX(totalSnatched where groupYear = 2012 and 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 artist, groupName FROM torrents WHERE groupYear = 2012 AND releaseType LIKE 'Single' ORDER BY totalSnatched DESC LIMIT 1"}],"db_id":"music_tracker"}862{"prompt":"Indicate the name of the actors of the films rated as 'Parents Strongly Precautioned' 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        Indicate the name of the actors of the films rated as 'Parents Strongly Precautioned' with the highest replacement cost.\n        \n        ### Context:\n        #### Query Context:\n        name refers to first_name, last_name; Parents Strongly Precautioned' refers to rating = 'PG-13'; \nhighest 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 T1.first_name, T1.last_name 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 T3.rating = 'PG-13' ORDER BY T3.replacement_cost DESC LIMIT 1"}],"db_id":"movie_3"}863{"prompt":"Give the post name of \"Idaho\" 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        Give the post name of \"Idaho\" state.\n        \n        ### Context:\n        #### Query Context:\n        post name refers to state_post;\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 state = 'Idaho' THEN state_post ELSE NULL END AS T FROM state_sector_details ) WHERE T IS NOT NULL"}],"db_id":"college_completion"}864{"prompt":"How many word appeared 8 times? State the language id of the 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 word appeared 8 times? State the language id of the page.\n        \n        ### Context:\n        #### Query Context:\n        appeared 8 times refers to occurrences = 8;\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(T2.wid), T1.lid FROM pages AS T1 INNER JOIN pages_words AS T2 ON T1.pid = T2.pid WHERE T2.occurrences = 8"}],"db_id":"language_corpus"}865{"prompt":"Which business was the first one to get a low risk violation because of \"Permit license or inspection report not posted\"? Give the name of the 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        Which business was the first one to get a low risk violation because of \"Permit license or inspection report not posted\"? Give the name of the business.\n        \n        ### Context:\n        #### Query Context:\n        low risk violation because of \"Permit license or inspection report not posted\" refers to risk_category = 'Low Risk' where description = 'Permit license or inspection report not posted'; business was the first one refers to name where MIN(date);\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 T2.name FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.`date` = ( SELECT MIN(`date`) FROM violations WHERE risk_category = 'Low Risk' AND description = 'Permit license or inspection report not posted' ) AND T1.risk_category = 'Low Risk' AND T1.description = 'Permit license or inspection report not posted'"}],"db_id":"food_inspection"}866{"prompt":"What is the total number of bikes that can be hold in Redwood City before 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 total number of bikes that can be hold in Redwood City before 2014.\n        \n        ### Context:\n        #### Query Context:\n        total number of bikes that can be hold = MAX(dock_count); before 2014 refers to year(installation_date)<2014;\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 SUM(CASE WHEN city = 'Redwood City' AND SUBSTR(installation_date, -4) < '2014' THEN dock_count ELSE 0 END) NUM FROM station"}],"db_id":"bike_share_1"}867{"prompt":"Provide the title, director, and release date of the movie voice-acted by Freddie Jones.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 title, director, and release date of the movie voice-acted by Freddie Jones.\n        \n        ### Context:\n        #### Query Context:\n        Freddie Jones refers to voice-actor = 'Freddie Jones'; title refers to movie_title;\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, T3.director, T2.release_date FROM `voice-actors` AS T1 INNER JOIN characters AS T2 ON T1.movie = T2.movie_title INNER JOIN director AS T3 ON T3.name = T2.movie_title WHERE T1.`voice-actor` = 'Freddie Jones'"}],"db_id":"disney"}868{"prompt":"How much is the size of Browser 4G and how many users have a pretty positive favorability on 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 much is the size of Browser 4G and how many users have a pretty positive favorability on it?\n        \n        ### Context:\n        #### Query Context:\n        Browser 4G is the App; pretty positive favorability refers to Sentiment_Polarity score = 0.5\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 T1.Size, COUNT(T1.App) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Browser 4G' AND T2.Sentiment_Polarity >= 0.5"}],"db_id":"app_store"}869{"prompt":"How many days did it take to end the work order \"425\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 days did it take to end the work order \"425\"?\n        \n        ### Context:\n        #### Query Context:\n        days to end a work order = SUBTRACT(ActualEndDate, ActualStartDate);\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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[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', 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'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 365 * (STRFTIME('%Y', ActualEndDate) - STRFTIME('%Y', ActualStartDate)) + 30 * (STRFTIME('%m', ActualEndDate) - STRFTIME('%m', ActualStartDate)) + STRFTIME('%d', ActualEndDate) - STRFTIME('%d', ActualStartDate) FROM WorkOrderRouting WHERE WorkOrderID = 425"}],"db_id":"works_cycles"}870{"prompt":"How many orders of more than 10 items have been returned?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 more than 10 items have been returned?\n        \n        ### Context:\n        #### Query Context:\n        more than 10 items have been returned refer to l_returnflag = 'R' where l_quantity > 10; orders refer to l_orderkey;\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_quantity > 10 AND l_returnflag = 'R'"}],"db_id":"retails"}871{"prompt":"Among the products manufactured by manufacturer 5 that have a retail price of no more than 1,000, how many products were shipped via rail?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 products manufactured by manufacturer 5 that have a retail price of no more than 1,000, how many products were shipped via rail?\n        \n        ### Context:\n        #### Query Context:\n        manufacturer 5 refers to p_mfgr = 'Manufacturer#5'; retail price of no more than 1,000 refers to p_retailprice < 1000; shipped via rail refers to shipmode = 'RAIL';\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.ps_partkey) FROM partsupp AS T1 INNER JOIN lineitem AS T2 ON T1.ps_suppkey = T2.l_suppkey INNER JOIN part AS T3 ON T1.ps_partkey = T3.p_partkey WHERE T3.p_mfgr = 'Manufacturer#5' AND T3.p_retailprice < 1000 AND T2.l_shipmode = 'RAIL'"}],"db_id":"retails"}872{"prompt":"How many complaints were served in 5 minutes or less by DORIT and responded to the customer with an explanation, were made by phone?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 were served in 5 minutes or less by DORIT and responded to the customer with an explanation, were made by phone?\n        \n        ### Context:\n        #### Query Context:\n        served in 5 minutes or less refers to ser_time < '00:05:00'; DORIT refers to server = 'DORIT'; responded with an explanation refers to \"Company response to consumer\" = 'Closed with explanation'; made by 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 COUNT(T1.`Complaint ID`) FROM callcenterlogs AS T1 INNER JOIN events AS T2 ON T1.`Complaint ID` = T2.`Complaint ID` WHERE T1.ser_time < '00:05:00' AND T1.server = 'DORIT' AND T2.`Submitted via` = 'Phone' AND T2.`Company response to consumer` = 'Closed with explanation'"}],"db_id":"retail_complains"}873{"prompt":"How many people were considered as prospective recipients of the \"Animation\" 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        How many people were considered as prospective recipients of the \"Animation\" award?\n        \n        ### Context:\n        #### Query Context:\n        prospective recipients refers to result = 'Nominee'\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 Award WHERE award = 'Animation' AND result = 'Nominee';"}],"db_id":"simpson_episodes"}874{"prompt":"List all of the information about the music department's casts and crews.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 information about the music department's casts and crews.\n        \n        ### Context:\n        #### Query Context:\n        information about the music department refers to category = 'Music Department'\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 person, name, birthdate, birth_name, birth_place , birth_region, birth_country, height_meters, nickname FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.name = T2.person WHERE T2.category = 'Music Department';"}],"db_id":"simpson_episodes"}875{"prompt":"How many Wikipedia pages with over 4000 different words are there on 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        How many Wikipedia pages with over 4000 different words are there on the Catalan language?\n        \n        ### Context:\n        #### Query Context:\n        lid = 1 means it's Catalan language; over 4000 different words means words > 4000; Wikipedia pages refers to pid\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(lid) FROM pages WHERE lid = 1 AND words > 4000"}],"db_id":"language_corpus"}876{"prompt":"How many bankrupt students are there in the Marines?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 bankrupt students are there in the Marines?\n        \n        ### Context:\n        #### Query Context:\n        Marines refers to organ = 'marines';\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 filed_for_bankrupcy AS T1 INNER JOIN enlist AS T2 ON T1.name = T2.name WHERE T2.organ = 'marines'"}],"db_id":"student_loan"}877{"prompt":"Describe the full names and cities of the customers who rented \"DREAM PICKUP\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 full names and cities of the customers who rented \"DREAM PICKUP\".\n        \n        ### Context:\n        #### Query Context:\n        full name refers to first_name, last_name; 'DREAM PICKUP' is a title of film\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.first_name, T4.last_name, T6.city FROM film AS T1 INNER JOIN inventory AS T2 ON T1.film_id = T2.film_id INNER JOIN rental AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN customer AS T4 ON T3.customer_id = T4.customer_id INNER JOIN address AS T5 ON T4.address_id = T5.address_id INNER JOIN city AS T6 ON T5.city_id = T6.city_id WHERE T1.title = 'DREAM PICKUP'"}],"db_id":"movie_3"}878{"prompt":"What is the total donated amount for projects 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 total donated amount for projects 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'; total donated amount refers to 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) FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.school_city = 'Brooklyn'"}],"db_id":"donor"}879{"prompt":"Please list the IDs of all the matches in the year 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 IDs of all the matches in the year 2008.\n        \n        ### Context:\n        #### Query Context:\n        ID of matches refers to Match_Id; in the year 2008 refers to SUBSTR(Match_Date, 1, 4) = '2008'\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 Match_Id FROM `Match` WHERE SUBSTR(Match_Date, 1, 4) = '2008'"}],"db_id":"soccer_2016"}880{"prompt":"How many universities are located in Japan?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 universities are located in Japan?\n        \n        ### Context:\n        #### Query Context:\n        located in Japan refers to country_name = 'Japan';\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 COUNT(*) FROM university AS T1 INNER JOIN country AS T2 ON T1.country_id = T2.id WHERE T2.country_name = 'Japan'"}],"db_id":"university"}881{"prompt":"Which character is the villain of the most popular 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        Which character is the villain of the most popular movie?\n        \n        ### Context:\n        #### Query Context:\n        the most popular movie refers to movie_title where MAX(total_gross);\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 T2.villian FROM `movies_total_gross` AS T1 INNER JOIN characters AS T2 ON T1.movie_title = T2.movie_title ORDER BY T1.total_gross DESC LIMIT 1"}],"db_id":"disney"}882{"prompt":"Among the coaches who have served more than 2 NBA teams, during which coach's period of coaching, a team has the least numbers of games lost in the post-season 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        Among the coaches who have served more than 2 NBA teams, during which coach's period of coaching, a team has the least numbers of games lost in the post-season games?\n        \n        ### Context:\n        #### Query Context:\n        served more than 2 NBA teams refers to count (tmID) > = 2; least number of game lost in post season refers to Min(post_losses)\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 coachID FROM coaches WHERE lgID = 'NBA' AND post_wins != 0 AND post_losses != 0 AND coachID IN ( SELECT coachID FROM coaches WHERE lgID = 'NBA' GROUP BY coachID HAVING COUNT(tmID) > 2 ) ORDER BY post_losses ASC LIMIT 1"}],"db_id":"professional_basketball"}883{"prompt":"Calculate the difference in sales between the games released in 1990 and 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        Calculate the difference in sales between the games released in 1990 and 2000.\n        \n        ### Context:\n        #### Query Context:\n        difference = SUBTRACT(SUM(num_sales WHERE release_year = 2000), SUM(num_sales WHERE release_year = 1990));\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(CASE WHEN T2.release_year = 2000 THEN T1.num_sales ELSE 0 END) - SUM(CASE WHEN T2.release_year = 1990 THEN T1.num_sales ELSE 0 END) FROM region_sales AS T1 INNER JOIN game_platform AS T2 ON T1.game_platform_id = T2.id"}],"db_id":"video_games"}884{"prompt":"Please list the event IDs of the events that have happened on the device of the oldest 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        Please list the event IDs of the events that have happened on the device of the oldest user.\n        \n        ### Context:\n        #### Query Context:\n        oldest user refers to MAX(age);\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 gender_age AS T1 INNER JOIN events AS T2 ON T1.device_id = T2.device_id ORDER BY T1.age DESC LIMIT 1"}],"db_id":"talkingdata"}885{"prompt":"What was the number of goals did player Id \"dyeba01\" make in the 1921 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 was the number of goals did player Id \"dyeba01\" make in the 1921 Stanley Cup finals?\n        \n        ### Context:\n        #### Query Context:\n        the number of goals refers to G\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'], 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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 G FROM ScoringSC WHERE playerID = 'dyeba01' AND year = 1921"}],"db_id":"hockey"}886{"prompt":"Which 2 Scottish teams scored 10 goals playing as a local team and in which seasons?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 2 Scottish teams scored 10 goals playing as a local team and in which seasons?\n        \n        ### Context:\n        #### Query Context:\n        local team refers to HomeTeam; Scottish means belong to the country = 'Scotland'; scored 10 goals refer to FTHG = 10, which is short name for Final-time Away-team Goals;\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.country = 'Scotland' AND T1.FTHG = 10"}],"db_id":"european_football_1"}887{"prompt":"How many products that take more than 2 days to make are out of 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        How many products that take more than 2 days to make are out of stock?\n        \n        ### Context:\n        #### Query Context:\n        take more than 2 days to make refers to DaysToManufacture>2; out of stock refers to OnOrderQty = 0 or OnOrderQty 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', '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', '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.ProductID) FROM ProductVendor AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID WHERE T1.OnOrderQty IS NULL OR T1.OnOrderQty = 0"}],"db_id":"works_cycles"}888{"prompt":"How many patients are allergic to eggs?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 patients are allergic to eggs?\n        \n        ### Context:\n        #### Query Context:\n        allergic to eggs refer to DESCRIPTION = 'Allergy to eggs' 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 COUNT(PATIENT) FROM allergies WHERE DESCRIPTION = 'Allergy to eggs'"}],"db_id":"synthea"}889{"prompt":"How many countries have notes on the indicator Stocks traded, turnover ratio of domestic shares?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 have notes on the indicator Stocks traded, turnover ratio of domestic shares?\n        \n        ### Context:\n        #### Query Context:\n        indicator Stocks traded, turnover ratio of domestic shares refers to IndicatorName = 'Stocks traded, turnover ratio of domestic shares (%)'\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 CountryNotes AS T1 INNER JOIN Series AS T2 ON T1.Seriescode = T2.SeriesCode WHERE T2.IndicatorName = 'Stocks traded, turnover ratio of domestic shares (%)'"}],"db_id":"world_development_indicators"}890{"prompt":"Describe the capital city and languages used in the country with the shortest life expectancy.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 capital city and languages used in the country with the shortest life expectancy.\n        \n        ### Context:\n        #### Query Context:\n        capital city refers to Capital; shortest life expectancy refers to MIN(LifeExpectancy);\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.Capital, T2.Language FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode INNER JOIN City AS T3 ON T1.Code = T3.CountryCode ORDER BY T1.LifeExpectancy LIMIT 1"}],"db_id":"world"}891{"prompt":"Please list the titles of all the books in British 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        Please list the titles of all the books in British English.\n        \n        ### Context:\n        #### Query Context:\n        \"British English\" is the language_name of the book\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_language AS T2 ON T1.language_id = T2.language_id WHERE T2.language_name = 'British English'"}],"db_id":"books"}892{"prompt":"How many students enlisted in the Navy?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 Navy?\n        \n        ### Context:\n        #### Query Context:\n        Navy 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"}893{"prompt":"What is the full name of the employees who report to the 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        What is the full name of the employees who report to the Sales Manager?\n        \n        ### Context:\n        #### Query Context:\n        full name refers to LastName, FirstName; the Sales Manager refers to Title = 'Sales Manager'; report to refers to ReportsTo is not NULL;\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 ReportsTo = ( SELECT EmployeeID FROM Employees WHERE Title = 'Sales Manager' )"}],"db_id":"retail_world"}894{"prompt":"What is the average time for a disabled student to be 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        What is the average time for a disabled student to be absent from school?\n        \n        ### Context:\n        #### Query Context:\n        average time 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"}895{"prompt":"What is the percentage of male athletes from Estonia?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 male athletes from Estonia?\n        \n        ### Context:\n        #### Query Context:\n        DIVIDE(COUNT(id where gender = 'M'), COUNT(id)) as percentage where region_name = 'Estonia';\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 CAST(COUNT(CASE WHEN T3.gender = 'M' THEN 1 ELSE NULL END) AS REAL) * 100 \/ 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 = 'Estonia'"}],"db_id":"olympics"}896{"prompt":"List the full name of customers who spend more than 50,000 in descending order the amount spend.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 customers who spend more than 50,000 in descending order the amount spend.\n        \n        ### Context:\n        #### Query Context:\n        full name of the customer = FirstName, MiddleInitial, LastName; more than 50,000 in the amount refers to MULTIPLY(Quantity, Price) > 50000;\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 T3.FirstName, T3.MiddleInitial, T3.LastName FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID INNER JOIN Customers AS T3 ON T2.CustomerID = T3.CustomerID WHERE T2.Quantity * T1.Price > 50000"}],"db_id":"sales"}897{"prompt":"In total, how many Hispanic male students graduated from Amridge University?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 total, how many Hispanic male students graduated from Amridge University?\n        \n        ### Context:\n        #### Query Context:\n        Hispanic refers to race = 'H'; male refers to gender = 'M'; Amridge University refers to chronname = 'Amridge 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 SUM(T2.grad_cohort) FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T1.unitid = T2.unitid WHERE T1.chronname = 'Amridge University' AND T2.gender = 'M' AND T2.race = 'H'"}],"db_id":"college_completion"}898{"prompt":"Provide the name, located country, and life expectancy of the most 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        Provide the name, located country, and life expectancy of the most populated city\n        \n        ### Context:\n        #### Query Context:\n        most populated 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 T2.Name, T1.Code, T1.LifeExpectancy FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode ORDER BY T2.Population DESC LIMIT 1"}],"db_id":"world"}899{"prompt":"How many mountains are there in Indonesia?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 mountains are there in Indonesia?\n        \n        ### Context:\n        #### Query Context:\n        Indonesia refers to one 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 COUNT(DISTINCT T2.Mountain) FROM country AS T1 INNER JOIN geo_mountain AS T2 ON T1.Code = T2.Country WHERE T1.Name = 'Indonesia'"}],"db_id":"mondial_geo"}900{"prompt":"How many devices are of the brand vivo?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are of the brand vivo?\n        \n        ### Context:\n        #### Query Context:\n        brand vivo 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 COUNT(device_id) FROM phone_brand_device_model2 WHERE phone_brand = 'vivo'"}],"db_id":"talkingdata"}901{"prompt":"Among the students who filed for bankruptcy, how many students are disabled?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 filed for bankruptcy, how many students are disabled?\n        \n        ### Context:\n        #### Query Context:\n        \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 filed_for_bankrupcy AS T2 ON T1.name = T2.name"}],"db_id":"student_loan"}902{"prompt":"Who is the director for the movie 'Transformers?'","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 for the movie 'Transformers?'\n        \n        ### Context:\n        #### Query Context:\n        the director refers to person_name where job = 'Director'; movie 'Transformers' refers to title = 'Transformers'\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 = 'Transformers' AND T2.job = 'Director'"}],"db_id":"movies_4"}903{"prompt":"When does Yelp_Business no.1 open on Tuesdays?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 does Yelp_Business no.1 open on Tuesdays?\n        \n        ### Context:\n        #### Query Context:\n        Yelp_Business no.1 refers to business_id = 1; open on refers to opening_time; Tuesdays refers to day_of_week = 'Tuesday'\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.opening_time FROM Business_Hours AS T1 INNER JOIN Days AS T2 ON T1.day_id = T2.day_id WHERE T2.day_of_week LIKE 'Tuesday' AND T1.business_id = 1"}],"db_id":"public_review_platform"}904{"prompt":"Calculate the average salary for employees who did inspection on License Re-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        Calculate the average salary for employees who did inspection on License Re-Inspection.\n        \n        ### Context:\n        #### Query Context:\n        inspection on License Re-Inspection refers to inspection_type = 'License Re-Inspection'; average salary = avg(salary)\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 AVG(T2.salary) FROM inspection AS T1 INNER JOIN employee AS T2 ON T1.employee_id = T2.employee_id WHERE T1.inspection_type = 'License Re-Inspection'"}],"db_id":"food_inspection_2"}905{"prompt":"Who was the coach for the team which had the most bench minors penalty in 2003\uff1f","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 coach for the team which had the most bench minors penalty in 2003\uff1f\n        \n        ### Context:\n        #### Query Context:\n        Coach of the team refers to firstName+lastName; 2003 refers to the year\n\n\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 DISTINCT T3.firstName, T3.lastName FROM Teams AS T1 INNER JOIN Coaches AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year INNER JOIN Master AS T3 ON T2.coachID = T3.coachID WHERE T1.year = '2003' GROUP BY T3.firstName, T3.lastName ORDER BY SUM(T1.BenchMinor) DESC LIMIT 1"}],"db_id":"hockey"}906{"prompt":"What is the highest amount of bonus earned by the sales person 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        What is the highest amount of bonus earned by the sales person in Canada?\n        \n        ### Context:\n        #### Query Context:\n        Canada is name of a sales territory\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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'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, 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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 businesses are opened for 24 hours?\n        \n        ### Context:\n        #### Query Context:\n        opened for 24 hours refers to attribute_name = 'Open 24 Hours' 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, 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[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 T2.attribute_value LIKE 'TRUE' AND T1.attribute_name LIKE 'Open 24 Hours'"}],"db_id":"public_review_platform"}908{"prompt":"In the state with the highest state appropriations to higher education in fiscal year 2011 per resident, which institution has the lowest number of undergraduates in 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        In the state with the highest state appropriations to higher education in fiscal year 2011 per resident, which institution has the lowest number of undergraduates in 2010?\n        \n        ### Context:\n        #### Query Context:\n        highest state appropriations to higher education in fiscal year 2011 per resident refers to MAX(state_appr_value); lowest number of undergraduates refers to MIN(student_count); in 2010 refers to year = 2010;\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, 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SELECT MIN(T1.student_count) FROM institution_details AS T1 INNER JOIN state_sector_details AS T2 ON T2.state = T1.state INNER JOIN institution_grads AS T3 ON T3.unitid = T1.unitid WHERE T3.year = 2010 ) AND T3.year = 2010 GROUP BY T1.state ORDER BY SUM(T2.state_appr_value) DESC LIMIT 1"}],"db_id":"college_completion"}909{"prompt":"Among all publications containing keywords 'Turbulent Fluids', what percentage of them was published in the journal named 'Physics of Fluids'?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 publications containing keywords 'Turbulent Fluids', what percentage of them was published in the journal named 'Physics of Fluids'?\n        \n        ### Context:\n        #### Query Context:\n        'Physics of Fluids' is the FullName of journal; percentage = DIVIDE(SUM(Keyword = 'Turbulent Fluids'), SUM(FullName = 'Physics of Fluids')) as percentage\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.Keyword = 'Turbulent Fluids' THEN 1 ELSE 0 END) AS REAL) \/ SUM(CASE WHEN T2.FullName = 'Physics of Fluids' THEN 1 ELSE 0 END) FROM Paper AS T1 INNER JOIN Journal AS T2 ON T1.JournalId = T2.Id"}],"db_id":"authors"}910{"prompt":"What is the profit on net of the products that have exactly 200 maximum order quantity? Indicate the name of the vendors to which these products were purchased 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 profit on net of the products that have exactly 200 maximum order quantity? Indicate the name of the vendors to which these products were purchased from.\n        \n        ### Context:\n        #### Query Context:\n        maximum orders refers to MaxOrderQty; MaxOrderQty = 200; profit on net = SUBTRACT(LastReceiptCost, 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', 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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', 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'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.LastReceiptCost - T1.StandardPrice, T2.Name FROM ProductVendor AS T1 INNER JOIN Vendor AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.MaxOrderQty = 200"}],"db_id":"works_cycles"}911{"prompt":"What is the average calorie count for all recipes using coarsely ground black pepper?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 calorie count for all recipes using coarsely ground black pepper?\n        \n        ### Context:\n        #### Query Context:\n        coarsely ground black pepper is a name of an ingredient; calculation = AVG(calories)\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(T3.calories) FROM Ingredient AS T1 INNER JOIN Quantity AS T2 ON T2.ingredient_id = T1.ingredient_id INNER JOIN Nutrition AS T3 ON T3.recipe_id = T2.recipe_id WHERE T1.name = 'coarsely ground black pepper'"}],"db_id":"cookbook"}912{"prompt":"List out the start date of the care plan of alive 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        List out the start date of the care plan of alive patients.\n        \n        ### Context:\n        #### Query Context:\n        start of the care plan refers to careplans.START; alive patients refers to deathdate is null;\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.START FROM careplans AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T2.deathdate IS NULL"}],"db_id":"synthea"}913{"prompt":"What was the temperature range of station no.1 on 2012\/1\/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 was the temperature range of station no.1 on 2012\/1\/1?\n        \n        ### Context:\n        #### Query Context:\n        on 2012\/1\/1 refers to date = '2012-01-01'; temperature range refers to Subtract (tmax, tmin); station no.1 refers to station_nbr = 1\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 tmax - tmin AS temrange FROM weather WHERE station_nbr = 1 AND `date` = '2012-01-01'"}],"db_id":"sales_in_weather"}914{"prompt":"Give the name of the venue where the most number of matches are 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        Give the name of the venue where the most number of matches are held.\n        \n        ### Context:\n        #### Query Context:\n        name of the venue refers to Venue_Name; most number of matches refers to max(count(Venue_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 T2.Venue_Name FROM `Match` AS T1 INNER JOIN Venue AS T2 ON T1.Venue_Id = T2.Venue_Id GROUP BY T2.Venue_Name ORDER BY COUNT(T2.Venue_Id) DESC LIMIT 1"}],"db_id":"soccer_2016"}915{"prompt":"When did event number 2 happen and how many users were active?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 did event number 2 happen and how many users were active?\n        \n        ### Context:\n        #### Query Context:\n        event number refers to event_id; event_id = 2; active refers to is_active = 1;\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) AS num FROM app_events AS T1 INNER JOIN events AS T2 ON T1.event_id = T2.event_id WHERE T1.event_id = 2 AND T1.is_active = 1 GROUP BY T2.timestamp"}],"db_id":"talkingdata"}916{"prompt":"How many movie lists were created by user 83373278 when he or she was a 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 were created by user 83373278 when he or she was a subscriber?\n        \n        ### Context:\n        #### Query Context:\n        the user was a subscriber when he created the list refers to user_subscriber = 1; user 83373278 refers to user_id = 83373278;\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_users WHERE user_id = 83373278 AND user_subscriber = 1"}],"db_id":"movie_platform"}917{"prompt":"For the latest passing player who could play all the positions in the court, how many points did he have in his career?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 latest passing player who could play all the positions in the court, how many points did he have in his career?\n        \n        ### Context:\n        #### Query Context:\n        the latest passing refers to max(season_id); play all the positions refers to pos like '%C%' or pos like '%F%' or pos like '%G%'\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 SUM(T2.points) FROM players AS T1 INNER JOIN players_teams AS T2 ON T1.playerID = T2.playerID WHERE T1.pos = 'C-F-G' GROUP BY T2.playerID, T2.year ORDER BY T2.year DESC LIMIT 1"}],"db_id":"professional_basketball"}918{"prompt":"For the movies in English that are the oldest, how many of them have 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        For the movies in English that are the oldest, how many of them have 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\uff1bisEnglish = 'T' means English movie\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(DISTINCT T1.movieid) FROM movies AS T1 INNER JOIN u2base AS T2 ON T1.movieid = T2.movieid WHERE T1.year = 1 AND T2.rating = 1 AND T1.isEnglish = 'T'"}],"db_id":"movielens"}919{"prompt":"What is the percentage of Hispanic patients who stopped their care plan 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 percentage of Hispanic patients who stopped their care plan in 2011?\n        \n        ### Context:\n        #### Query Context:\n        Hispanic patients refers to ethnicity = 'hispanic'; percentage = MULTIPLY(DIVIDE(COUNT(careplans.PATIENT WHERE ethnicity = 'hispanic'), COUNT(careplans.PATIENT)), 1.0); stopped their care plan in 2011 refers to substr(careplans.stop, 1, 4) = '2011';\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.race = 'hispanic' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.PATIENT) FROM careplans AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE strftime('%Y', T1.stop) = '2011'"}],"db_id":"synthea"}920{"prompt":"Who was the earliest user created a list but didn't get any followers? Give the user 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        Who was the earliest user created a list but didn't get any followers? Give the user ID.\n        \n        ### Context:\n        #### Query Context:\n        earliest user created a list refers to MIN(list_creation_date_utc); didn't get any followers refers to user_subscriber = 0\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_users WHERE user_subscriber = 0 ORDER BY list_creation_date_utc LIMIT 1"}],"db_id":"movie_platform"}921{"prompt":"In 1997, how many loss did the coach have who temporary coached Tampa Bay Lightning? Indicate his\/her coach 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 1997, how many loss did the coach have who temporary coached Tampa Bay Lightning? Indicate his\/her coach ID.\n        \n        ### Context:\n        #### Query Context:\n        temporary coached refers to notes = 'interim'; number of loss refers to L (to add)\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, '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', 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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 SUM(T1.l), T1.coachID FROM Coaches AS T1 INNER JOIN Teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.notes = 'interim' AND T1.year = '1997' AND T2.name = 'Tampa Bay Lightning' GROUP BY T1.coachID"}],"db_id":"hockey"}922{"prompt":"Among the players born in 1977, what is the percentage of the players with a role as a captain?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 born in 1977, what is the percentage of the players with a role as a captain?\n        \n        ### Context:\n        #### Query Context:\n        born in 1977 refers to DOB LIKE '1977%'; a role as a captain refers to Role_Desc = 'Captain'; percentage = divide(count(Role_Id where Role_Desc = 'Captain'), count(Role_Id)) * 100% where DOB LIKE '1977%'\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.Role_Desc = 'Captain' THEN 1 ELSE 0 END) AS REAL) * 100 \/ COUNT(T1.Role_Id) FROM Rolee AS T1 INNER JOIN Player_Match AS T2 ON T2.Role_Id = T1.Role_Id INNER JOIN Player AS T3 ON T3.Player_Id = T2.Player_Id WHERE T3.DOB LIKE '1977%'"}],"db_id":"soccer_2016"}923{"prompt":"What is the shipment ID of the heaviest shipment that Zachery Hicks transported?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 shipment ID of the heaviest shipment that Zachery Hicks transported?\n        \n        ### Context:\n        #### Query Context:\n        shipment ID refers to ship_id; heaviest shipment refers to Max(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.ship_id FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id WHERE T2.first_name = 'Zachery' AND T2.last_name = 'Hicks' ORDER BY T1.weight DESC LIMIT 1"}],"db_id":"shipping"}924{"prompt":"What are the names of players that have run scored less 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        What are the names of players that have run scored less than 3?\n        \n        ### Context:\n        #### Query Context:\n        scored less than 3 refers to Runs_Scored < 3; name of player 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 Player_Match AS T2 ON T1.Player_Id = T2.Player_Id INNER JOIN Batsman_Scored AS T3 ON T2.Match_ID = T3.Match_ID WHERE T3.Runs_Scored < 3 GROUP BY T1.Player_Name"}],"db_id":"soccer_2016"}925{"prompt":"For the client who made the complaint call \"CR0217298\", what was his\/her birthday\uff1f","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 client who made the complaint call \"CR0217298\", what was his\/her birthday\uff1f\n        \n        ### Context:\n        #### Query Context:\n        complaint call refers to Complaint ID; birthday = year, month, day;\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.month, T1.day FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.`Complaint ID` = 'CR0217298'"}],"db_id":"retail_complains"}926{"prompt":"What was the price of 1 Bitcoin in 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        What was the price of 1 Bitcoin in 2016?\n        \n        ### Context:\n        #### Query Context:\n        2016 refers to historical date where year(date) = 2016\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 AVG(T2.price) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T1.name = 'Bitcoin' AND STRFTIME('%Y', T2.date) = '2016'"}],"db_id":"coinmarketcap"}927{"prompt":"Give the time stamp for event No.887711.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 time stamp for event No.887711.\n        \n        ### Context:\n        #### Query Context:\n        event no. refers to event_id; event_id = '887711';\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 timestamp FROM events WHERE event_id = '887711'"}],"db_id":"talkingdata"}928{"prompt":"Provide the supplier company name in Sydney and its homepage address if available.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 supplier company name in Sydney and its homepage address if available.\n        \n        ### Context:\n        #### Query Context:\n        in Sydney refers to City = 'Sydney';\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 CompanyName, HomePage FROM Suppliers WHERE City = 'Sydney'"}],"db_id":"retail_world"}929{"prompt":"Among the male students, list down the names of students with 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        Among the male students, list down the names of students with payment due.\n        \n        ### Context:\n        #### Query Context:\n        male students are mentioned in male.name; with payment due refers to bool = 'pos';\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 male AS T1 INNER JOIN no_payment_due AS T2 ON T1.name = T2.name WHERE T2.bool = 'pos'"}],"db_id":"student_loan"}930{"prompt":"Name all products supplied by Zaanse Snoepfabriek.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 products supplied by Zaanse Snoepfabriek.\n        \n        ### Context:\n        #### Query Context:\n        products refers to ProductName; 'Zaanse Snoepfabriek' is a 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 T1.ProductName FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T2.CompanyName = 'Zaanse Snoepfabriek'"}],"db_id":"retail_world"}931{"prompt":"Find the vendor with the least average lead time for Product ID 348.","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 vendor with the least average lead time for Product ID 348.\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, 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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 ProductVendor AS T1 INNER JOIN Vendor AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.ProductID = 348 ORDER BY T1.AverageLeadTime ASC LIMIT 1"}],"db_id":"works_cycles"}932{"prompt":"Name the countries that belong in the region with comment description \"furiously express accounts wake sly\".","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 countries that belong in the region with comment description \"furiously express accounts wake sly\".\n        \n        ### Context:\n        #### Query Context:\n        r_comment = 'furiously express accounts wake sly';\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.n_name FROM nation AS T1 INNER JOIN region AS T2 ON T1.n_regionkey = T2.r_regionkey WHERE T2.r_comment = 'furiously express accounts wake sly'"}],"db_id":"retails"}933{"prompt":"List the restaurant's ID that has a review greater than the 70% of average review of all American restaurants with street number greater than 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        List the restaurant's ID that has a review greater than the 70% of average review of all American restaurants with street number greater than 2000.\n        \n        ### Context:\n        #### Query Context:\n        American restaurant refers to food_type = 'american'; street number greater than 2000 refers to street_num > 2000; review greater than the 70% of average review refers to review > multiply(avg(review), 0.7)\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 generalinfo AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T2.food_type = 'american' AND T1.street_num > 2000 GROUP BY T1.id_restaurant ORDER BY AVG(T2.review) * 0.7 DESC"}],"db_id":"restaurant"}934{"prompt":"What horror movies have a running time of at least 2? Please list movie 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        What horror movies have a running time of at least 2? Please list movie IDs.\n        \n        ### Context:\n        #### Query Context:\n        Higher value of running time means running time is longer\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 T1.movieid FROM movies2directors AS T1 INNER JOIN movies AS T2 ON T1.movieid = T2.movieid WHERE T2.runningtime >= 2 AND T1.genre = 'Horror'"}],"db_id":"movielens"}935{"prompt":"How many awards had been held 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 had been held in 2009?\n        \n        ### Context:\n        #### Query Context:\n        had been held 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';"}],"db_id":"simpson_episodes"}936{"prompt":"What is the full name of customers who dealt with sales person with id 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 full name of customers who dealt with sales person with id 5?\n        \n        ### Context:\n        #### Query Context:\n        full name = FirstName, MiddleInitial, LastName;\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.FirstName, T1.MiddleInitial, T1.LastName FROM Customers AS T1 INNER JOIN Sales AS T2 ON T1.CustomerID = T2.CustomerID WHERE T2.SalesPersonID = 5"}],"db_id":"sales"}937{"prompt":"How many owners are located in Mexico?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 owners are located in Mexico?\n        \n        ### Context:\n        #### Query Context:\n        owners in Mexico refer to ContactTitle where Country = 'Mexico';\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(ContactTitle) FROM Customers WHERE Country = 'Mexico' AND ContactTitle = 'Owner'"}],"db_id":"retail_world"}938{"prompt":"How many dishes are there on the menu \"Zentral Theater Terrace\"?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 are there on the menu \"Zentral Theater Terrace\"?\n        \n        ### Context:\n        #### Query Context:\n        Zentral Theater Terrace is a name of menu;\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 T3.name = 'Zentral Theater Terrace' THEN 1 ELSE 0 END) FROM MenuItem AS T1 INNER JOIN MenuPage AS T2 ON T1.menu_page_id = T2.id INNER JOIN Menu AS T3 ON T2.menu_id = T3.id"}],"db_id":"menu"}939{"prompt":"How many students does Ogdon Zywicki advise?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 does Ogdon Zywicki advise?\n        \n        ### Context:\n        #### Query Context:\n        Ogdon Zywicki is a professor;\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 prof AS T2 ON T1.prof_id = T2.prof_id WHERE T2.first_name = 'Ogdon' AND T2.last_name = 'Zywicki'"}],"db_id":"cs_semester"}940{"prompt":"How many crime against society were reported in Englewood?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 society were reported in Englewood?\n        \n        ### Context:\n        #### Query Context:\n        \"Englewood\" is the community_area_name; 'Society' is the crime_against\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 T3.community_area_name = 'Englewood' THEN 1 ELSE 0 END) FROM FBI_Code AS T1 INNER JOIN Crime AS T2 ON T1.fbi_code_no = T2.fbi_code_no INNER JOIN Community_Area AS T3 ON T2.community_area_no = T3.community_area_no WHERE T1.crime_against = 'Society'"}],"db_id":"chicago_crime"}941{"prompt":"What is the prediction relationship class id of the tallest 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        What is the prediction relationship class id of the tallest image?\n        \n        ### Context:\n        #### Query Context:\n        prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H)\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 T1.PRED_CLASS_ID FROM IMG_REL AS T1 INNER JOIN IMG_OBJ AS T2 ON T1.IMG_ID = T2.IMG_ID ORDER BY T2.H DESC LIMIT 1"}],"db_id":"image_and_language"}942{"prompt":"What is the truck's model year used to ship the ship ID 1245?","messages":[{"role":"user","content":"\n        You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question, context regarding the query, 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 truck's model year used to ship the ship ID 1245?\n        \n        ### Context:\n        #### Query Context:\n        \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.model_year FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id WHERE T2.ship_id = '1245'"}],"db_id":"shipping"}943