question_id int64 0 16.1k | db_id stringclasses 259
values | dber_id stringlengths 15 29 | question stringlengths 16 325 | SQL stringlengths 18 1.25k | tokens listlengths 4 62 | entities listlengths 0 21 | entity_to_token listlengths 20 20 | dber_tags listlengths 4 62 |
|---|---|---|---|---|---|---|---|---|
3,647 | tracking_software_problems | spider:train_spider.json:5376 | For each product which has problems, what are the number of problems and the product id? | SELECT count(*) , T2.product_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id GROUP BY T2.product_id | [
"For",
"each",
"product",
"which",
"has",
"problems",
",",
"what",
"are",
"the",
"number",
"of",
"problems",
"and",
"the",
"product",
"i",
"d",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "product_id"
},
{
"id": 1,
"type": "table",
"value": "problems"
},
{
"id": 2,
"type": "table",
"value": "product"
}
] | [
{
"entity_id": 0,
"token_idxs": [
15,
16,
17
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
3,648 | legislator | bird:train.json:4905 | Provide the address of the legislator with the contact form of http://www.carper.senate.gov/contact/. | SELECT address FROM `current-terms` WHERE contact_form = 'http://www.carper.senate.gov/contact/' | [
"Provide",
"the",
"address",
"of",
"the",
"legislator",
"with",
"the",
"contact",
"form",
"of",
"http://www.carper.senate.gov/contact/."
] | [
{
"id": 3,
"type": "value",
"value": "http://www.carper.senate.gov/contact/"
},
{
"id": 0,
"type": "table",
"value": "current-terms"
},
{
"id": 2,
"type": "column",
"value": "contact_form"
},
{
"id": 1,
"type": "column",
"value": "address"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8,
9
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE"
] |
3,649 | e_government | spider:train_spider.json:6325 | What is the last name of the first individual contacted from the organization with the maximum UK Vat number across all organizations? | SELECT t3.individual_last_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id = t2.organization_id JOIN individuals AS t3 ON t2.individual_id = t3.individual_id WHERE t1.uk_vat_number = (SELECT max(uk_vat_number) FROM organizations) ORDER BY t2.date_contact_to ASC LIMIT... | [
"What",
"is",
"the",
"last",
"name",
"of",
"the",
"first",
"individual",
"contacted",
"from",
"the",
"organization",
"with",
"the",
"maximum",
"UK",
"Vat",
"number",
"across",
"all",
"organizations",
"?"
] | [
{
"id": 5,
"type": "table",
"value": "organization_contact_individuals"
},
{
"id": 0,
"type": "column",
"value": "individual_last_name"
},
{
"id": 3,
"type": "column",
"value": "date_contact_to"
},
{
"id": 7,
"type": "column",
"value": "organization_id"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
16,
17,
18
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
21
]... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
3,650 | retail_world | bird:train.json:6656 | Mention the first name of employee who took care the order id 10250. | SELECT T1.FirstName, T1.LastName FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T2.OrderID = 10250 | [
"Mention",
"the",
"first",
"name",
"of",
"employee",
"who",
"took",
"care",
"the",
"order",
"i",
"d",
"10250",
"."
] | [
{
"id": 6,
"type": "column",
"value": "employeeid"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 2,
"type": "table",
"value": "employees"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
11,
12
]
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
3,651 | retail_world | bird:train.json:6538 | Write down the full name of Vie President of Sales and his age when he was hired. | SELECT FirstName, LastName , TIMESTAMPDIFF(YEAR, BirthDate, HireDate) AS AGE FROM Employees WHERE Title = 'Vice President, Sales' | [
"Write",
"down",
"the",
"full",
"name",
"of",
"Vie",
"President",
"of",
"Sales",
"and",
"his",
"age",
"when",
"he",
"was",
"hired",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Vice President, Sales"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 6,
"type": "column",
"value": "birthdate"
},
{
"id": 2,
"type": "co... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
0
]
},
{
"entity_id": 4,
"token_idxs": [
6,
7,
8,
9
]
},
... | [
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
3,654 | movie_1 | spider:train_spider.json:2459 | What are the titles of all movies that have not been rated? | SELECT title FROM Movie WHERE mID NOT IN (SELECT mID FROM Rating) | [
"What",
"are",
"the",
"titles",
"of",
"all",
"movies",
"that",
"have",
"not",
"been",
"rated",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "rating"
},
{
"id": 0,
"type": "table",
"value": "movie"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 2,
"type": "column",
"value": "mid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,655 | film_rank | spider:train_spider.json:4159 | Return the titles and directors of films that were never in the market of China. | SELECT title , director FROM film WHERE film_id NOT IN (SELECT film_id FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.market_id = T2.Market_ID WHERE country = 'China') | [
"Return",
"the",
"titles",
"and",
"directors",
"of",
"films",
"that",
"were",
"never",
"in",
"the",
"market",
"of",
"China",
"."
] | [
{
"id": 4,
"type": "table",
"value": "film_market_estimation"
},
{
"id": 8,
"type": "column",
"value": "market_id"
},
{
"id": 2,
"type": "column",
"value": "director"
},
{
"id": 3,
"type": "column",
"value": "film_id"
},
{
"id": 6,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
3,656 | bike_share_1 | bird:train.json:9093 | What is the percentage ration of customers to subscribers that started their trips within the city of San Francisco? | SELECT CAST(SUM(CASE WHEN T1.subscription_type = 'Customer' THEN 1 ELSE 0 END) AS REAL) * 100 / SUM(CASE WHEN T1.subscription_type = 'Subscriber' THEN 1 ELSE 0 END) FROM trip AS T1 LEFT JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.city = 'San Francisco' | [
"What",
"is",
"the",
"percentage",
"ration",
"of",
"customers",
"to",
"subscribers",
"that",
"started",
"their",
"trips",
"within",
"the",
"city",
"of",
"San",
"Francisco",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "start_station_name"
},
{
"id": 9,
"type": "column",
"value": "subscription_type"
},
{
"id": 3,
"type": "value",
"value": "San Francisco"
},
{
"id": 10,
"type": "value",
"value": "Subscriber"
},
{
"id": 11,
... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
17,
18
]
},
{
"entity_id": 4,
"token_idxs": []
},
{... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
3,658 | movielens | bird:train.json:2310 | How many directors with average revenue of 4 have made either action or adventure films? | SELECT COUNT(T1.directorid) FROM directors AS T1 INNER JOIN movies2directors AS T2 ON T1.directorid = T2.directorid WHERE T1.avg_revenue = 4 AND (T2.genre = 'Adventure' OR T2.genre = 'Action') | [
"How",
"many",
"directors",
"with",
"average",
"revenue",
"of",
"4",
"have",
"made",
"either",
"action",
"or",
"adventure",
"films",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "movies2directors"
},
{
"id": 3,
"type": "column",
"value": "avg_revenue"
},
{
"id": 2,
"type": "column",
"value": "directorid"
},
{
"id": 0,
"type": "table",
"value": "directors"
},
{
"id": 6,
"type": "valu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O"
] |
3,659 | movies_4 | bird:train.json:528 | What is the original language of the movie with the tagline "An offer you can't refuse."? | SELECT T3.language_name FROM movie AS T1 INNER JOIN movie_languages AS T2 ON T1.movie_id = T2.movie_id INNER JOIN language AS T3 ON T2.language_id = T3.language_id INNER JOIN language_role AS T4 ON T2.language_role_id = T4.role_id WHERE T4.language_role = 'Original' AND T1.tagline LIKE 'An offer you can%t refuse.' | [
"What",
"is",
"the",
"original",
"language",
"of",
"the",
"movie",
"with",
"the",
"tagline",
"\"",
"An",
"offer",
"you",
"ca",
"n't",
"refuse",
".",
"\"",
"?"
] | [
{
"id": 8,
"type": "value",
"value": "An offer you can%t refuse."
},
{
"id": 3,
"type": "column",
"value": "language_role_id"
},
{
"id": 10,
"type": "table",
"value": "movie_languages"
},
{
"id": 0,
"type": "column",
"value": "language_name"
},
{
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
3,660 | student_1 | spider:train_spider.json:4095 | How many students does one classroom have? | SELECT count(*) , classroom FROM list GROUP BY classroom | [
"How",
"many",
"students",
"does",
"one",
"classroom",
"have",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "classroom"
},
{
"id": 0,
"type": "table",
"value": "list"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
3,661 | video_games | bird:train.json:3467 | Calculate the number of game publisher IDs for games released in 1984. | SELECT COUNT(T.game_publisher_id) FROM game_platform AS T WHERE T.release_year = 1984 | [
"Calculate",
"the",
"number",
"of",
"game",
"publisher",
"IDs",
"for",
"games",
"released",
"in",
"1984",
"."
] | [
{
"id": 3,
"type": "column",
"value": "game_publisher_id"
},
{
"id": 0,
"type": "table",
"value": "game_platform"
},
{
"id": 1,
"type": "column",
"value": "release_year"
},
{
"id": 2,
"type": "value",
"value": "1984"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"e... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
3,662 | superstore | bird:train.json:2369 | List the products ordered by Becky Martin around the Central region. | SELECT DISTINCT T3.`Product Name` FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.`Customer Name` = 'Becky Martin' AND T3.Region = 'Central' | [
"List",
"the",
"products",
"ordered",
"by",
"Becky",
"Martin",
"around",
"the",
"Central",
"region",
"."
] | [
{
"id": 3,
"type": "table",
"value": "central_superstore"
},
{
"id": 5,
"type": "column",
"value": "Customer Name"
},
{
"id": 0,
"type": "column",
"value": "Product Name"
},
{
"id": 6,
"type": "value",
"value": "Becky Martin"
},
{
"id": 9,
"typ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
3,663 | bike_share_1 | bird:train.json:9091 | How many bikes could Evelyn Park and Ride hold and how many users who started on that station are subscribers? | SELECT SUM(T2.dock_count), COUNT(T1.subscription_type) FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.name = 'Evelyn Park and Ride' AND T1.start_station_name = T2.name AND T1.subscription_type = 'Subscriber' | [
"How",
"many",
"bikes",
"could",
"Evelyn",
"Park",
"and",
"Ride",
"hold",
"and",
"how",
"many",
"users",
"who",
"started",
"on",
"that",
"station",
"are",
"subscribers",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "Evelyn Park and Ride"
},
{
"id": 5,
"type": "column",
"value": "start_station_name"
},
{
"id": 3,
"type": "column",
"value": "subscription_type"
},
{
"id": 2,
"type": "column",
"value": "dock_count"
},
{
"id": ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
16,
... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O"
] |
3,664 | video_games | bird:train.json:3455 | What are the names of games that were released in 2007? | SELECT T3.game_name FROM game_platform AS T1 INNER JOIN game_publisher AS T2 ON T1.game_publisher_id = T2.id INNER JOIN game AS T3 ON T2.game_id = T3.id WHERE T1.release_year = 2007 | [
"What",
"are",
"the",
"names",
"of",
"games",
"that",
"were",
"released",
"in",
"2007",
"?"
] | [
{
"id": 8,
"type": "column",
"value": "game_publisher_id"
},
{
"id": 5,
"type": "table",
"value": "game_publisher"
},
{
"id": 4,
"type": "table",
"value": "game_platform"
},
{
"id": 2,
"type": "column",
"value": "release_year"
},
{
"id": 0,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
3,665 | chicago_crime | bird:train.json:8604 | Among the crimes in all the districts in Chicago, what is the percentage of them happening in the Central district? | 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 | [
"Among",
"the",
"crimes",
"in",
"all",
"the",
"districts",
"in",
"Chicago",
",",
"what",
"is",
"the",
"percentage",
"of",
"them",
"happening",
"in",
"the",
"Central",
"district",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "district_name"
},
{
"id": 2,
"type": "column",
"value": "district_no"
},
{
"id": 4,
"type": "column",
"value": "case_number"
},
{
"id": 1,
"type": "table",
"value": "district"
},
{
"id": 8,
"type": "value"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
20
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
3,666 | company_office | spider:train_spider.json:4575 | List the names of buildings that have no company office. | SELECT name FROM buildings WHERE id NOT IN (SELECT building_id FROM Office_locations) | [
"List",
"the",
"names",
"of",
"buildings",
"that",
"have",
"no",
"company",
"office",
"."
] | [
{
"id": 3,
"type": "table",
"value": "office_locations"
},
{
"id": 4,
"type": "column",
"value": "building_id"
},
{
"id": 0,
"type": "table",
"value": "buildings"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,667 | tracking_orders | spider:train_spider.json:6927 | List the ids of all distinct orders ordered by placed date. | SELECT DISTINCT order_id FROM orders ORDER BY date_order_placed | [
"List",
"the",
"ids",
"of",
"all",
"distinct",
"orders",
"ordered",
"by",
"placed",
"date",
"."
] | [
{
"id": 2,
"type": "column",
"value": "date_order_placed"
},
{
"id": 1,
"type": "column",
"value": "order_id"
},
{
"id": 0,
"type": "table",
"value": "orders"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
8,
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
3,668 | wrestler | spider:train_spider.json:1867 | Which teams had more than 3 eliminations? | SELECT Team FROM elimination GROUP BY Team HAVING COUNT(*) > 3 | [
"Which",
"teams",
"had",
"more",
"than",
"3",
"eliminations",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "elimination"
},
{
"id": 1,
"type": "column",
"value": "team"
},
{
"id": 2,
"type": "value",
"value": "3"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
3,669 | regional_sales | bird:train.json:2631 | Which sales team name has the least orders in 2019? | SELECT T2.`Sales Team` FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T1.OrderDate LIKE '%/%/19' GROUP BY T2.`Sales Team` ORDER BY COUNT(T1.OrderNumber) ASC LIMIT 1 | [
"Which",
"sales",
"team",
"name",
"has",
"the",
"least",
"orders",
"in",
"2019",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "Sales Orders"
},
{
"id": 6,
"type": "column",
"value": "_salesteamid"
},
{
"id": 5,
"type": "column",
"value": "salesteamid"
},
{
"id": 7,
"type": "column",
"value": "ordernumber"
},
{
"id": 0,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
1,
2
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"O",
"O"
] |
3,670 | sakila_1 | spider:train_spider.json:2962 | What is the genre name of the film HUNGER ROOF? | SELECT T1.name FROM category AS T1 JOIN film_category AS T2 ON T1.category_id = T2.category_id JOIN film AS T3 ON T2.film_id = T3.film_id WHERE T3.title = 'HUNGER ROOF' | [
"What",
"is",
"the",
"genre",
"name",
"of",
"the",
"film",
"HUNGER",
"ROOF",
"?"
] | [
{
"id": 5,
"type": "table",
"value": "film_category"
},
{
"id": 3,
"type": "value",
"value": "HUNGER ROOF"
},
{
"id": 7,
"type": "column",
"value": "category_id"
},
{
"id": 4,
"type": "table",
"value": "category"
},
{
"id": 6,
"type": "column",... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O"
] |
3,671 | activity_1 | spider:train_spider.json:6792 | Tell me the first and last name of the student who has the most activities. | SELECT T1.fname , T1.lname FROM Student AS T1 JOIN Participates_in AS T2 ON T1.StuID = T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1 | [
"Tell",
"me",
"the",
"first",
"and",
"last",
"name",
"of",
"the",
"student",
"who",
"has",
"the",
"most",
"activities",
"."
] | [
{
"id": 4,
"type": "table",
"value": "participates_in"
},
{
"id": 3,
"type": "table",
"value": "student"
},
{
"id": 0,
"type": "column",
"value": "stuid"
},
{
"id": 1,
"type": "column",
"value": "fname"
},
{
"id": 2,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,672 | formula_1 | bird:dev.json:866 | Who was the player that got the lap time of 0:01:27 in the race No. 161? Show his introduction website. | SELECT DISTINCT T2.forename, T2.surname, T2.url FROM lapTimes AS T1 INNER JOIN drivers AS T2 ON T2.driverId = T1.driverId WHERE T1.raceId = 161 AND T1.time LIKE '1:27%' | [
"Who",
"was",
"the",
"player",
"that",
"got",
"the",
"lap",
"time",
"of",
"0:01:27",
"in",
"the",
"race",
"No",
".",
"161",
"?",
"Show",
"his",
"introduction",
"website",
"."
] | [
{
"id": 0,
"type": "column",
"value": "forename"
},
{
"id": 3,
"type": "table",
"value": "laptimes"
},
{
"id": 5,
"type": "column",
"value": "driverid"
},
{
"id": 1,
"type": "column",
"value": "surname"
},
{
"id": 4,
"type": "table",
"value... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,674 | aan_1 | bird:test.json:1051 | Find the name of authors who have never published a paper in ACL. | SELECT name FROM Author WHERE author_id NOT IN (SELECT T1.author_id FROM Author_list AS T1 JOIN Paper AS T2 ON T1.paper_id = T2.paper_id WHERE T2.venue = "ACL") | [
"Find",
"the",
"name",
"of",
"authors",
"who",
"have",
"never",
"published",
"a",
"paper",
"in",
"ACL",
"."
] | [
{
"id": 3,
"type": "table",
"value": "author_list"
},
{
"id": 2,
"type": "column",
"value": "author_id"
},
{
"id": 7,
"type": "column",
"value": "paper_id"
},
{
"id": 0,
"type": "table",
"value": "author"
},
{
"id": 4,
"type": "table",
"val... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O"
] |
3,675 | cre_Students_Information_Systems | bird:test.json:491 | Which students have a student loan more than the average amount? List the students' biographical data and the details. | SELECT T1.bio_data , T1.student_details FROM Students AS T1 JOIN Student_Loans AS T2 ON T1.student_id = T2.student_id WHERE T2.amount_of_loan > ( SELECT avg(amount_of_loan) FROM Student_Loans ) | [
"Which",
"students",
"have",
"a",
"student",
"loan",
"more",
"than",
"the",
"average",
"amount",
"?",
"List",
"the",
"students",
"'",
"biographical",
"data",
"and",
"the",
"details",
"."
] | [
{
"id": 1,
"type": "column",
"value": "student_details"
},
{
"id": 4,
"type": "column",
"value": "amount_of_loan"
},
{
"id": 3,
"type": "table",
"value": "student_loans"
},
{
"id": 5,
"type": "column",
"value": "student_id"
},
{
"id": 0,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
17
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
3,676 | mental_health_survey | bird:train.json:4579 | Which country have the least number of users being surveyed? Indicate the name of the country. If there are multiple countries having the same number of users, indicate all of their names. | SELECT T1.AnswerText FROM Answer AS T1 INNER JOIN Question AS T2 ON T1.QuestionID = T2.questionid WHERE T2.questionid = 3 GROUP BY T1.AnswerText ORDER BY COUNT(T1.UserID) DESC LIMIT 1 | [
"Which",
"country",
"have",
"the",
"least",
"number",
"of",
"users",
"being",
"surveyed",
"?",
"Indicate",
"the",
"name",
"of",
"the",
"country",
".",
"If",
"there",
"are",
"multiple",
"countries",
"having",
"the",
"same",
"number",
"of",
"users",
",",
"in... | [
{
"id": 0,
"type": "column",
"value": "answertext"
},
{
"id": 3,
"type": "column",
"value": "questionid"
},
{
"id": 2,
"type": "table",
"value": "question"
},
{
"id": 1,
"type": "table",
"value": "answer"
},
{
"id": 5,
"type": "column",
"va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
7
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,677 | human_resources | bird:train.json:8941 | How much higher is James Johnson's salary from the minimum salary of his title? | SELECT CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL) - CAST(REPLACE(SUBSTR(T2.minsalary, 4), ',', '') AS REAL) AS diff FROM employee AS T1 INNER JOIN position AS T2 ON T1.positionID = T2.positionID WHERE T1.lastname = 'Johnson' AND T1.firstname = 'James' | [
"How",
"much",
"higher",
"is",
"James",
"Johnson",
"'s",
"salary",
"from",
"the",
"minimum",
"salary",
"of",
"his",
"title",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "positionid"
},
{
"id": 5,
"type": "column",
"value": "firstname"
},
{
"id": 10,
"type": "column",
"value": "minsalary"
},
{
"id": 0,
"type": "table",
"value": "employee"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"token_idxs": [
3
... | [
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
3,678 | retail_complains | bird:train.json:303 | List by name all customers who provided consent for the tag Older American. | SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.Tags = 'Older American' AND T2.`Consumer consent provided?` != 'N/A' AND T2.`Consumer consent provided?` IS NOT NULL AND T2.`Consumer consent provided?` != '' | [
"List",
"by",
"name",
"all",
"customers",
"who",
"provided",
"consent",
"for",
"the",
"tag",
"Older",
"American",
"."
] | [
{
"id": 8,
"type": "column",
"value": "Consumer consent provided?"
},
{
"id": 7,
"type": "value",
"value": "Older American"
},
{
"id": 5,
"type": "column",
"value": "client_id"
},
{
"id": 1,
"type": "column",
"value": "middle"
},
{
"id": 3,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
0
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"O"
] |
3,679 | simpson_episodes | bird:train.json:4162 | Please list the names of all the awards won by the crew member whose nickname is Doofus. | SELECT T2.award FROM Person AS T1 INNER JOIN Award AS T2 ON T1.name = T2.person WHERE T1.nickname = 'Doofus' AND T2.result = 'Winner'; | [
"Please",
"list",
"the",
"names",
"of",
"all",
"the",
"awards",
"won",
"by",
"the",
"crew",
"member",
"whose",
"nickname",
"is",
"Doofus",
"."
] | [
{
"id": 5,
"type": "column",
"value": "nickname"
},
{
"id": 1,
"type": "table",
"value": "person"
},
{
"id": 4,
"type": "column",
"value": "person"
},
{
"id": 6,
"type": "value",
"value": "Doofus"
},
{
"id": 7,
"type": "column",
"value": "r... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
3,682 | hockey | bird:train.json:7676 | Players born in which year have received the most awards in total? | SELECT T1.birthYear FROM Master AS T1 INNER JOIN AwardsPlayers AS T2 ON T1.playerID = T2.playerID GROUP BY T1.birthYear ORDER BY COUNT(T2.award) DESC LIMIT 1 | [
"Players",
"born",
"in",
"which",
"year",
"have",
"received",
"the",
"most",
"awards",
"in",
"total",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "awardsplayers"
},
{
"id": 0,
"type": "column",
"value": "birthyear"
},
{
"id": 3,
"type": "column",
"value": "playerid"
},
{
"id": 1,
"type": "table",
"value": "master"
},
{
"id": 4,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
0
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
... | [
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
3,683 | hockey | bird:train.json:7751 | How many bench minor penalties did the team St. Louis Blues got in total in all the games? | SELECT CAST(SUM(BenchMinor) AS REAL) / 2 FROM Teams WHERE name = 'St. Louis Blues' | [
"How",
"many",
"bench",
"minor",
"penalties",
"did",
"the",
"team",
"St.",
"Louis",
"Blues",
"got",
"in",
"total",
"in",
"all",
"the",
"games",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "St. Louis Blues"
},
{
"id": 4,
"type": "column",
"value": "benchminor"
},
{
"id": 0,
"type": "table",
"value": "teams"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"val... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
8,
9,
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2,
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
3,684 | student_loan | bird:train.json:4536 | How many students are enlisted to the air force? | SELECT COUNT(name) FROM enlist WHERE organ = 'air_force' | [
"How",
"many",
"students",
"are",
"enlisted",
"to",
"the",
"air",
"force",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "air_force"
},
{
"id": 0,
"type": "table",
"value": "enlist"
},
{
"id": 1,
"type": "column",
"value": "organ"
},
{
"id": 3,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
3,685 | college_2 | spider:train_spider.json:1483 | What are the distinct salaries of all instructors who earned less than the maximum salary? | SELECT DISTINCT salary FROM instructor WHERE salary < (SELECT max(salary) FROM instructor) | [
"What",
"are",
"the",
"distinct",
"salaries",
"of",
"all",
"instructors",
"who",
"earned",
"less",
"than",
"the",
"maximum",
"salary",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "instructor"
},
{
"id": 1,
"type": "column",
"value": "salary"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
3,686 | movie_3 | bird:train.json:9191 | What is the complete address of store id 1? | SELECT T3.address, T3.address2, T3.district FROM country AS T1 INNER JOIN city AS T2 ON T1.country_id = T2.country_id INNER JOIN address AS T3 ON T2.city_id = T3.city_id INNER JOIN store AS T4 ON T3.address_id = T4.address_id WHERE T4.store_id = 1 | [
"What",
"is",
"the",
"complete",
"address",
"of",
"store",
"i",
"d",
"1",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "address_id"
},
{
"id": 11,
"type": "column",
"value": "country_id"
},
{
"id": 1,
"type": "column",
"value": "address2"
},
{
"id": 2,
"type": "column",
"value": "district"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
7,
8
]
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
3,687 | cre_Theme_park | spider:train_spider.json:5886 | Find the total number of available hotels. | SELECT count(*) FROM HOTELS | [
"Find",
"the",
"total",
"number",
"of",
"available",
"hotels",
"."
] | [
{
"id": 0,
"type": "table",
"value": "hotels"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,688 | driving_school | spider:train_spider.json:6705 | What is the total time for all lessons taught by Janessa Sawayn? | SELECT sum(lesson_time) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = "Janessa" AND T2.last_name = "Sawayn"; | [
"What",
"is",
"the",
"total",
"time",
"for",
"all",
"lessons",
"taught",
"by",
"Janessa",
"Sawayn",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "lesson_time"
},
{
"id": 4,
"type": "column",
"value": "first_name"
},
{
"id": 6,
"type": "column",
"value": "last_name"
},
{
"id": 3,
"type": "column",
"value": "staff_id"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
3,689 | flight_4 | spider:train_spider.json:6845 | List all countries and their number of airlines in the descending order of number of airlines. | SELECT country , count(*) FROM airlines GROUP BY country ORDER BY count(*) DESC | [
"List",
"all",
"countries",
"and",
"their",
"number",
"of",
"airlines",
"in",
"the",
"descending",
"order",
"of",
"number",
"of",
"airlines",
"."
] | [
{
"id": 0,
"type": "table",
"value": "airlines"
},
{
"id": 1,
"type": "column",
"value": "country"
}
] | [
{
"entity_id": 0,
"token_idxs": [
15
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,690 | movies_4 | bird:train.json:465 | Write the titles of horror films with a vote average of more than 7. | SELECT T1.title FROM movie AS T1 INNER JOIN movie_genres AS T2 ON T1.movie_id = T2.movie_id INNER JOIN genre AS T3 ON T2.genre_id = T3.genre_id WHERE T3.genre_name = 'Horror' AND vote_average > 7 | [
"Write",
"the",
"titles",
"of",
"horror",
"films",
"with",
"a",
"vote",
"average",
"of",
"more",
"than",
"7",
"."
] | [
{
"id": 3,
"type": "table",
"value": "movie_genres"
},
{
"id": 7,
"type": "column",
"value": "vote_average"
},
{
"id": 5,
"type": "column",
"value": "genre_name"
},
{
"id": 4,
"type": "column",
"value": "genre_id"
},
{
"id": 9,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
3,691 | planet_1 | bird:test.json:1874 | How many packages sent by Ogden Wernstrom and received by Leo Wong? | SELECT T1.PackageNumber FROM PACKAGE AS T1 JOIN Client AS T2 ON T1.Sender = T2.AccountNumber WHERE T2.Name = "Ogden Wernstrom" INTERSECT SELECT T1.PackageNumber FROM PACKAGE AS T1 JOIN Client AS T2 ON T1.Recipient = T2.AccountNumber WHERE T2.Name = "Leo Wong" | [
"How",
"many",
"packages",
"sent",
"by",
"Ogden",
"Wernstrom",
"and",
"received",
"by",
"Leo",
"Wong",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "Ogden Wernstrom"
},
{
"id": 0,
"type": "column",
"value": "packagenumber"
},
{
"id": 7,
"type": "column",
"value": "accountnumber"
},
{
"id": 8,
"type": "column",
"value": "recipient"
},
{
"id": 5,
"type":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5,
6
]
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
3,692 | talkingdata | bird:train.json:1197 | State the number of users who are under 50 and above 20 use device model of Galaxy Premier. | SELECT COUNT(T1.device_id) FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T1.age BETWEEN 20 AND 50 AND T2.device_model = 'Galaxy Premier' | [
"State",
"the",
"number",
"of",
"users",
"who",
"are",
"under",
"50",
"and",
"above",
"20",
"use",
"device",
"model",
"of",
"Galaxy",
"Premier",
"."
] | [
{
"id": 1,
"type": "table",
"value": "phone_brand_device_model2"
},
{
"id": 7,
"type": "value",
"value": "Galaxy Premier"
},
{
"id": 6,
"type": "column",
"value": "device_model"
},
{
"id": 0,
"type": "table",
"value": "gender_age"
},
{
"id": 2,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
3,693 | world | bird:train.json:7861 | What is the form of government that the city of Manila has? | SELECT T1.GovernmentForm FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode WHERE T2.Name = 'Manila' | [
"What",
"is",
"the",
"form",
"of",
"government",
"that",
"the",
"city",
"of",
"Manila",
"has",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "governmentform"
},
{
"id": 6,
"type": "column",
"value": "countrycode"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 4,
"type": "value",
"value": "Manila"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O"
] |
3,694 | dorm_1 | spider:train_spider.json:5717 | What are the first and last names of all students who are not living in the city HKG and order the results by age? | SELECT fname , lname FROM student WHERE city_code != 'HKG' ORDER BY age | [
"What",
"are",
"the",
"first",
"and",
"last",
"names",
"of",
"all",
"students",
"who",
"are",
"not",
"living",
"in",
"the",
"city",
"HKG",
"and",
"order",
"the",
"results",
"by",
"age",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "city_code"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "fname"
},
{
"id": 2,
"type": "column",
"value": "lname"
},
{
"id": 4,
"type": "value",
"value": "H... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entit... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
3,695 | simpson_episodes | bird:train.json:4209 | How many 10 star votes did the top 4 episodes with the highest rating received? | SELECT SUM(T1.votes) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE T2.stars = 10 ORDER BY T1.rating DESC LIMIT 4; | [
"How",
"many",
"10",
"star",
"votes",
"did",
"the",
"top",
"4",
"episodes",
"with",
"the",
"highest",
"rating",
"received",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "episode_id"
},
{
"id": 0,
"type": "table",
"value": "episode"
},
{
"id": 4,
"type": "column",
"value": "rating"
},
{
"id": 2,
"type": "column",
"value": "stars"
},
{
"id": 5,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
... | [
"O",
"O",
"B-VALUE",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
3,696 | toxicology | bird:dev.json:247 | List all the elements of atoms that can not bond with any other atoms. | SELECT DISTINCT T.element FROM atom AS T WHERE T.element NOT IN ( SELECT DISTINCT T1.element FROM atom AS T1 INNER JOIN connected AS T2 ON T1.atom_id = T2.atom_id ) | [
"List",
"all",
"the",
"elements",
"of",
"atoms",
"that",
"can",
"not",
"bond",
"with",
"any",
"other",
"atoms",
"."
] | [
{
"id": 2,
"type": "table",
"value": "connected"
},
{
"id": 1,
"type": "column",
"value": "element"
},
{
"id": 3,
"type": "column",
"value": "atom_id"
},
{
"id": 0,
"type": "table",
"value": "atom"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,697 | medicine_enzyme_interaction | spider:train_spider.json:944 | What are the names of enzymes whose product is not 'Heme'? | SELECT name FROM enzyme WHERE product != 'Heme' | [
"What",
"are",
"the",
"names",
"of",
"enzymes",
"whose",
"product",
"is",
"not",
"'",
"Heme",
"'",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "product"
},
{
"id": 0,
"type": "table",
"value": "enzyme"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"value": "Heme"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
3,698 | body_builder | spider:train_spider.json:1154 | What are the clean and jerk score of the body builder with the highest total score? | SELECT Clean_Jerk FROM body_builder ORDER BY Total DESC LIMIT 1 | [
"What",
"are",
"the",
"clean",
"and",
"jerk",
"score",
"of",
"the",
"body",
"builder",
"with",
"the",
"highest",
"total",
"score",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "body_builder"
},
{
"id": 1,
"type": "column",
"value": "clean_jerk"
},
{
"id": 2,
"type": "column",
"value": "total"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9,
10
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
3,699 | food_inspection_2 | bird:train.json:6187 | Calculate the total amount of fine under the food equipment and utensil category. | SELECT SUM(T2.fine) FROM inspection_point AS T1 INNER JOIN violation AS T2 ON T1.point_id = T2.point_id WHERE T1.category = 'Food Equipment and Utensil' | [
"Calculate",
"the",
"total",
"amount",
"of",
"fine",
"under",
"the",
"food",
"equipment",
"and",
"utensil",
"category",
"."
] | [
{
"id": 3,
"type": "value",
"value": "Food Equipment and Utensil"
},
{
"id": 0,
"type": "table",
"value": "inspection_point"
},
{
"id": 1,
"type": "table",
"value": "violation"
},
{
"id": 2,
"type": "column",
"value": "category"
},
{
"id": 5,
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
3,700 | address | bird:train.json:5123 | What is the area code of Bishopville, SC? | SELECT T1.area_code FROM area_code AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code WHERE T2.city = 'Bishopville' AND T2.state = 'SC' | [
"What",
"is",
"the",
"area",
"code",
"of",
"Bishopville",
",",
"SC",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Bishopville"
},
{
"id": 0,
"type": "column",
"value": "area_code"
},
{
"id": 1,
"type": "table",
"value": "area_code"
},
{
"id": 2,
"type": "table",
"value": "zip_data"
},
{
"id": 3,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
3,701 | online_exams | bird:test.json:210 | For each student answer, find the first name of the student and the date of the answer. | SELECT T2.First_Name , T1.Date_of_Answer FROM Student_Answers AS T1 JOIN Students AS T2 ON T1.Student_ID = T2.Student_ID | [
"For",
"each",
"student",
"answer",
",",
"find",
"the",
"first",
"name",
"of",
"the",
"student",
"and",
"the",
"date",
"of",
"the",
"answer",
"."
] | [
{
"id": 2,
"type": "table",
"value": "student_answers"
},
{
"id": 1,
"type": "column",
"value": "date_of_answer"
},
{
"id": 0,
"type": "column",
"value": "first_name"
},
{
"id": 4,
"type": "column",
"value": "student_id"
},
{
"id": 3,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
7,
8
]
},
{
"entity_id": 1,
"token_idxs": [
14,
15,
16,
17
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
... | [
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
3,702 | sales_in_weather | bird:train.json:8137 | How many units of item no.9 were sold in store no.1 on 2012/1/1? | SELECT units FROM sales_in_weather WHERE `date` = '2012-01-01' AND store_nbr = 1 AND item_nbr = 9 | [
"How",
"many",
"units",
"of",
"item",
"no.9",
"were",
"sold",
"in",
"store",
"no.1",
"on",
"2012/1/1",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "sales_in_weather"
},
{
"id": 3,
"type": "value",
"value": "2012-01-01"
},
{
"id": 4,
"type": "column",
"value": "store_nbr"
},
{
"id": 6,
"type": "column",
"value": "item_nbr"
},
{
"id": 1,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
3,703 | ship_mission | spider:train_spider.json:4020 | Show names of ships involved in a mission launched after 1928. | SELECT T2.Name FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID = T2.Ship_ID WHERE T1.Launched_Year > 1928 | [
"Show",
"names",
"of",
"ships",
"involved",
"in",
"a",
"mission",
"launched",
"after",
"1928",
"."
] | [
{
"id": 3,
"type": "column",
"value": "launched_year"
},
{
"id": 1,
"type": "table",
"value": "mission"
},
{
"id": 5,
"type": "column",
"value": "ship_id"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "table",
"value... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
... | [
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
3,704 | store_1 | spider:train_spider.json:565 | List the state in the US with the most invoices. | SELECT billing_state , COUNT(*) FROM invoices WHERE billing_country = "USA" GROUP BY billing_state ORDER BY COUNT(*) DESC LIMIT 1; | [
"List",
"the",
"state",
"in",
"the",
"US",
"with",
"the",
"most",
"invoices",
"."
] | [
{
"id": 2,
"type": "column",
"value": "billing_country"
},
{
"id": 1,
"type": "column",
"value": "billing_state"
},
{
"id": 0,
"type": "table",
"value": "invoices"
},
{
"id": 3,
"type": "column",
"value": "USA"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,705 | superhero | bird:dev.json:799 | How many superheroes didn't have any publisher? | SELECT COUNT(T1.id) FROM superhero AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T2.id = 1 | [
"How",
"many",
"superheroes",
"did",
"n't",
"have",
"any",
"publisher",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "publisher_id"
},
{
"id": 0,
"type": "table",
"value": "superhero"
},
{
"id": 1,
"type": "table",
"value": "publisher"
},
{
"id": 2,
"type": "column",
"value": "id"
},
{
"id": 3,
"type": "value",
"value... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,706 | works_cycles | bird:train.json:7215 | What is the full name of the Vice President of Production? | SELECT T2.FirstName, T2.MiddleName, T2.LastName FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.JobTitle = 'Vice President of Production' | [
"What",
"is",
"the",
"full",
"name",
"of",
"the",
"Vice",
"President",
"of",
"Production",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "Vice President of Production"
},
{
"id": 7,
"type": "column",
"value": "businessentityid"
},
{
"id": 1,
"type": "column",
"value": "middlename"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 2,... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
3,707 | cre_Theme_park | spider:train_spider.json:5949 | Show different ways to get to attractions and the number of attractions that can be accessed in the corresponding way. | SELECT How_to_Get_There , COUNT(*) FROM Tourist_Attractions GROUP BY How_to_Get_There | [
"Show",
"different",
"ways",
"to",
"get",
"to",
"attractions",
"and",
"the",
"number",
"of",
"attractions",
"that",
"can",
"be",
"accessed",
"in",
"the",
"corresponding",
"way",
"."
] | [
{
"id": 0,
"type": "table",
"value": "tourist_attractions"
},
{
"id": 1,
"type": "column",
"value": "how_to_get_there"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5,
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,708 | european_football_2 | bird:dev.json:1027 | Indicate the full names of the top 10 players with the highest number of penalties. | SELECT t2.player_name FROM Player_Attributes AS t1 INNER JOIN Player AS t2 ON t1.id = t2.id ORDER BY t1.penalties DESC LIMIT 10 | [
"Indicate",
"the",
"full",
"names",
"of",
"the",
"top",
"10",
"players",
"with",
"the",
"highest",
"number",
"of",
"penalties",
"."
] | [
{
"id": 1,
"type": "table",
"value": "player_attributes"
},
{
"id": 0,
"type": "column",
"value": "player_name"
},
{
"id": 3,
"type": "column",
"value": "penalties"
},
{
"id": 2,
"type": "table",
"value": "player"
},
{
"id": 4,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
3,709 | musical | spider:train_spider.json:247 | Return the names of musicals who have the nominee Bob Fosse. | SELECT Name FROM musical WHERE Nominee = "Bob Fosse" | [
"Return",
"the",
"names",
"of",
"musicals",
"who",
"have",
"the",
"nominee",
"Bob",
"Fosse",
"."
] | [
{
"id": 3,
"type": "column",
"value": "Bob Fosse"
},
{
"id": 0,
"type": "table",
"value": "musical"
},
{
"id": 2,
"type": "column",
"value": "nominee"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O"
] |
3,710 | retails | bird:train.json:6693 | Give customer No.106936's region name. | SELECT T3.r_name FROM nation AS T1 INNER JOIN customer AS T2 ON T1.n_nationkey = T2.c_nationkey INNER JOIN region AS T3 ON T1.n_regionkey = T3.r_regionkey WHERE T2.c_custkey = 106936 | [
"Give",
"customer",
"No.106936",
"'s",
"region",
"name",
"."
] | [
{
"id": 6,
"type": "column",
"value": "n_regionkey"
},
{
"id": 7,
"type": "column",
"value": "r_regionkey"
},
{
"id": 8,
"type": "column",
"value": "n_nationkey"
},
{
"id": 9,
"type": "column",
"value": "c_nationkey"
},
{
"id": 2,
"type": "colu... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-TABLE",
"B-VALUE",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
3,711 | app_store | bird:train.json:2531 | What is the rating for "Draw A Stickman"? | SELECT Rating FROM playstore WHERE APP = 'Draw A Stickman' | [
"What",
"is",
"the",
"rating",
"for",
"\"",
"Draw",
"A",
"Stickman",
"\"",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Draw A Stickman"
},
{
"id": 0,
"type": "table",
"value": "playstore"
},
{
"id": 1,
"type": "column",
"value": "rating"
},
{
"id": 2,
"type": "column",
"value": "app"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
3,712 | advertising_agencies | bird:test.json:2077 | Show all client ids and details with sic code "Bad". | SELECT client_id , client_details FROM Clients WHERE sic_code = "Bad"; | [
"Show",
"all",
"client",
"ids",
"and",
"details",
"with",
"sic",
"code",
"\"",
"Bad",
"\"",
"."
] | [
{
"id": 2,
"type": "column",
"value": "client_details"
},
{
"id": 1,
"type": "column",
"value": "client_id"
},
{
"id": 3,
"type": "column",
"value": "sic_code"
},
{
"id": 0,
"type": "table",
"value": "clients"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
3,713 | musical | spider:train_spider.json:253 | What are the names of actors who have been in the musical titled The Phantom of the Opera? | SELECT T1.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID = T2.Musical_ID WHERE T2.Name = "The Phantom of the Opera" | [
"What",
"are",
"the",
"names",
"of",
"actors",
"who",
"have",
"been",
"in",
"the",
"musical",
"titled",
"The",
"Phantom",
"of",
"the",
"Opera",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "The Phantom of the Opera"
},
{
"id": 4,
"type": "column",
"value": "musical_id"
},
{
"id": 2,
"type": "table",
"value": "musical"
},
{
"id": 1,
"type": "table",
"value": "actor"
},
{
"id": 0,
"type": "colu... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
13,
14,
15,
16,
17
]
},
{
"entity_id": 4,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
3,714 | public_review_platform | bird:train.json:4003 | How many businesses are registered in the database under 'Banks & Credit Unions' category? | SELECT COUNT(T2.business_id) FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id WHERE T1.category_name = 'Banks & Credit Unions' | [
"How",
"many",
"businesses",
"are",
"registered",
"in",
"the",
"database",
"under",
"'",
"Banks",
"&",
"Credit",
"Unions",
"'",
"category",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Banks & Credit Unions"
},
{
"id": 1,
"type": "table",
"value": "business_categories"
},
{
"id": 2,
"type": "column",
"value": "category_name"
},
{
"id": 4,
"type": "column",
"value": "business_id"
},
{
"id": 5,... | [
{
"entity_id": 0,
"token_idxs": [
15
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10,
11,
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-TABLE",
"O"
] |
3,715 | student_club | bird:dev.json:1415 | List out the position of members who joined major of Business. | SELECT T2.position FROM major AS T1 INNER JOIN member AS T2 ON T1.major_id = T2.link_to_major WHERE T1.major_name = 'Business' | [
"List",
"out",
"the",
"position",
"of",
"members",
"who",
"joined",
"major",
"of",
"Business",
"."
] | [
{
"id": 6,
"type": "column",
"value": "link_to_major"
},
{
"id": 3,
"type": "column",
"value": "major_name"
},
{
"id": 0,
"type": "column",
"value": "position"
},
{
"id": 4,
"type": "value",
"value": "Business"
},
{
"id": 5,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
3,716 | movielens | bird:train.json:2320 | How many separate 35 year-old uesers have rated the movie from UK? | SELECT COUNT(DISTINCT T2.userid) FROM movies AS T1 INNER JOIN u2base AS T2 ON T1.movieid = T2.movieid INNER JOIN users AS T3 ON T2.userid = T3.userid WHERE T1.country = 'UK' AND T3.age = 35 | [
"How",
"many",
"separate",
"35",
"year",
"-",
"old",
"uesers",
"have",
"rated",
"the",
"movie",
"from",
"UK",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "country"
},
{
"id": 8,
"type": "column",
"value": "movieid"
},
{
"id": 1,
"type": "column",
"value": "userid"
},
{
"id": 2,
"type": "table",
"value": "movies"
},
{
"id": 3,
"type": "table",
"value": "u... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
3,717 | card_games | bird:dev.json:460 | Which artist designed the card whose promotional name is "Battra, Dark Destroyer"? | SELECT artist FROM cards WHERE flavorName = 'Battra, Dark Destroyer' | [
"Which",
"artist",
"designed",
"the",
"card",
"whose",
"promotional",
"name",
"is",
"\"",
"Battra",
",",
"Dark",
"Destroyer",
"\"",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Battra, Dark Destroyer"
},
{
"id": 2,
"type": "column",
"value": "flavorname"
},
{
"id": 1,
"type": "column",
"value": "artist"
},
{
"id": 0,
"type": "table",
"value": "cards"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10,
11,
12,
13
]
},
{
"entity_id": 4,
"token_idxs": []
}... | [
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
3,718 | superhero | bird:dev.json:821 | List down Ajax's superpowers. | SELECT T3.power_name FROM superhero AS T1 INNER JOIN hero_power AS T2 ON T1.id = T2.hero_id INNER JOIN superpower AS T3 ON T2.power_id = T3.id WHERE T1.superhero_name = 'Ajax' | [
"List",
"down",
"Ajax",
"'s",
"superpowers",
"."
] | [
{
"id": 2,
"type": "column",
"value": "superhero_name"
},
{
"id": 0,
"type": "column",
"value": "power_name"
},
{
"id": 1,
"type": "table",
"value": "superpower"
},
{
"id": 5,
"type": "table",
"value": "hero_power"
},
{
"id": 4,
"type": "table"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"O"
] |
3,719 | soccer_2016 | bird:train.json:2019 | What type did match ID 336000 win? | SELECT T2.Win_Type FROM Match AS T1 INNER JOIN Win_By AS T2 ON T1.Win_Type = T2.Win_Id WHERE T1.Match_Id = 336000 | [
"What",
"type",
"did",
"match",
"ID",
"336000",
"win",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "win_type"
},
{
"id": 3,
"type": "column",
"value": "match_id"
},
{
"id": 2,
"type": "table",
"value": "win_by"
},
{
"id": 4,
"type": "value",
"value": "336000"
},
{
"id": 5,
"type": "column",
"value": ... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_... | [
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"B-COLUMN",
"O"
] |
3,720 | student_club | bird:dev.json:1344 | What was the notes of the fundraising on 2019/9/14? | SELECT notes FROM income WHERE source = 'Fundraising' AND date_received = '2019-09-14' | [
"What",
"was",
"the",
"notes",
"of",
"the",
"fundraising",
"on",
"2019/9/14",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "date_received"
},
{
"id": 3,
"type": "value",
"value": "Fundraising"
},
{
"id": 5,
"type": "value",
"value": "2019-09-14"
},
{
"id": 0,
"type": "table",
"value": "income"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
3,721 | food_inspection | bird:train.json:8794 | Give the description of the moderate risk violation which "Chez Fayala, Inc." had on 2016/7/1. | SELECT T1.description FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T2.name = 'Chez Fayala, Inc.' AND T1.`date` = '2016-07-01' AND T1.risk_category = 'Moderate Risk' | [
"Give",
"the",
"description",
"of",
"the",
"moderate",
"risk",
"violation",
"which",
"\"",
"Chez",
"Fayala",
",",
"Inc.",
"\"",
"had",
"on",
"2016/7/1",
"."
] | [
{
"id": 5,
"type": "value",
"value": "Chez Fayala, Inc."
},
{
"id": 8,
"type": "column",
"value": "risk_category"
},
{
"id": 9,
"type": "value",
"value": "Moderate Risk"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 3,
"type... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
3,722 | codebase_comments | bird:train.json:567 | What is the task of the method whose tokenized name is "online median filter test median window filling"? | SELECT SUBSTR(SUBSTR(Name, INSTR(Name, '.') + 1), 1, INSTR(SUBSTR(Name, INSTR(Name, '.') + 1), '.') - 1) task FROM Method WHERE NameTokenized = 'online median filter test median window filling' | [
"What",
"is",
"the",
"task",
"of",
"the",
"method",
"whose",
"tokenized",
"name",
"is",
"\"",
"online",
"median",
"filter",
"test",
"median",
"window",
"filling",
"\"",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "online median filter test median window filling"
},
{
"id": 1,
"type": "column",
"value": "nametokenized"
},
{
"id": 0,
"type": "table",
"value": "method"
},
{
"id": 4,
"type": "column",
"value": "name"
},
{
"i... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
12,
13,
14,
15,
16,
17,
18
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
3,723 | bakery_1 | bird:test.json:1561 | Find the top three dates with the most receipts. | SELECT date FROM receipts GROUP BY date ORDER BY count(*) DESC LIMIT 3 | [
"Find",
"the",
"top",
"three",
"dates",
"with",
"the",
"most",
"receipts",
"."
] | [
{
"id": 0,
"type": "table",
"value": "receipts"
},
{
"id": 1,
"type": "column",
"value": "date"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,724 | riding_club | spider:train_spider.json:1734 | How many coaches does each club has? List the club id, name and the number of coaches. | SELECT T1.club_id , T1.club_name, count(*) FROM club AS T1 JOIN coach AS T2 ON T1.club_id = T2.club_id GROUP BY T1.club_id | [
"How",
"many",
"coaches",
"does",
"each",
"club",
"has",
"?",
"List",
"the",
"club",
"i",
"d",
",",
"name",
"and",
"the",
"number",
"of",
"coaches",
"."
] | [
{
"id": 1,
"type": "column",
"value": "club_name"
},
{
"id": 0,
"type": "column",
"value": "club_id"
},
{
"id": 3,
"type": "table",
"value": "coach"
},
{
"id": 2,
"type": "table",
"value": "club"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10,
11,
12
]
},
{
"entity_id": 1,
"token_idxs": [
13,
14
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
19
]
},
{
"entity_id": 4,
"toke... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,725 | icfp_1 | spider:train_spider.json:2919 | Sort the first names of all the authors in alphabetical order. | SELECT fname FROM authors ORDER BY fname | [
"Sort",
"the",
"first",
"names",
"of",
"all",
"the",
"authors",
"in",
"alphabetical",
"order",
"."
] | [
{
"id": 0,
"type": "table",
"value": "authors"
},
{
"id": 1,
"type": "column",
"value": "fname"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
3,726 | public_review_platform | bird:train.json:4111 | How many businesses with music_karaoke attribute are closed? | SELECT COUNT(T2.business_id) FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T1.attribute_name = 'music_karaoke' AND T3.active = 'false' AND T2.attribute_value IN ('none', 'no', 'false') | [
"How",
"many",
"businesses",
"with",
"music_karaoke",
"attribute",
"are",
"closed",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "business_attributes"
},
{
"id": 8,
"type": "column",
"value": "attribute_value"
},
{
"id": 4,
"type": "column",
"value": "attribute_name"
},
{
"id": 5,
"type": "value",
"value": "music_karaoke"
},
{
"id": 11,
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"O"
] |
3,727 | cre_Drama_Workshop_Groups | spider:train_spider.json:5114 | What is the phone number of the performer Ashley? | SELECT Customer_Phone FROM PERFORMERS WHERE Customer_Name = "Ashley" | [
"What",
"is",
"the",
"phone",
"number",
"of",
"the",
"performer",
"Ashley",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "customer_phone"
},
{
"id": 2,
"type": "column",
"value": "customer_name"
},
{
"id": 0,
"type": "table",
"value": "performers"
},
{
"id": 3,
"type": "column",
"value": "Ashley"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
1,
2,
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"en... | [
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
3,729 | card_games | bird:dev.json:471 | What is the expansion type of the set "Hauptset Zehnte Edition"? | SELECT T1.type FROM sets AS T1 INNER JOIN set_translations AS T2 ON T2.setCode = T1.code WHERE T2.translation = 'Hauptset Zehnte Edition' | [
"What",
"is",
"the",
"expansion",
"type",
"of",
"the",
"set",
"\"",
"Hauptset",
"Zehnte",
"Edition",
"\"",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Hauptset Zehnte Edition"
},
{
"id": 2,
"type": "table",
"value": "set_translations"
},
{
"id": 3,
"type": "column",
"value": "translation"
},
{
"id": 5,
"type": "column",
"value": "setcode"
},
{
"id": 0,
"t... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
9,
10,
11
]
... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
3,730 | flight_1 | spider:train_spider.json:378 | What are the names of all employees who have a salary higher than average? | SELECT name FROM Employee WHERE salary > (SELECT avg(salary) FROM Employee) | [
"What",
"are",
"the",
"names",
"of",
"all",
"employees",
"who",
"have",
"a",
"salary",
"higher",
"than",
"average",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "employee"
},
{
"id": 2,
"type": "column",
"value": "salary"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
3,731 | soccer_2016 | bird:train.json:1945 | Write down the player names and IDs of the English umpires. | SELECT T1.Umpire_Name, T1.Umpire_Id FROM Umpire AS T1 INNER JOIN Country AS T2 ON T1.Umpire_Country = T2.Country_Id WHERE T2.Country_Name = 'England' | [
"Write",
"down",
"the",
"player",
"names",
"and",
"IDs",
"of",
"the",
"English",
"umpires",
"."
] | [
{
"id": 6,
"type": "column",
"value": "umpire_country"
},
{
"id": 4,
"type": "column",
"value": "country_name"
},
{
"id": 0,
"type": "column",
"value": "umpire_name"
},
{
"id": 7,
"type": "column",
"value": "country_id"
},
{
"id": 1,
"type": "c... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
5
... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,732 | address | bird:train.json:5169 | Among the daylight savings areas in the Midwest region, how many postal points are there in Illinois? | SELECT COUNT(T2.zip_code) FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T1.name = 'Illinois' AND T2.daylight_savings = 'Yes' AND T2.region = 'Midwest' | [
"Among",
"the",
"daylight",
"savings",
"areas",
"in",
"the",
"Midwest",
"region",
",",
"how",
"many",
"postal",
"points",
"are",
"there",
"in",
"Illinois",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "daylight_savings"
},
{
"id": 3,
"type": "column",
"value": "abbreviation"
},
{
"id": 1,
"type": "table",
"value": "zip_data"
},
{
"id": 2,
"type": "column",
"value": "zip_code"
},
{
"id": 6,
"type": "value... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4,
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
3,734 | conference | bird:test.json:1069 | How many institution are founded between 1850 and 1900? | SELECT count(*) FROM institution WHERE founded BETWEEN 1850 AND 1900 | [
"How",
"many",
"institution",
"are",
"founded",
"between",
"1850",
"and",
"1900",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "institution"
},
{
"id": 1,
"type": "column",
"value": "founded"
},
{
"id": 2,
"type": "value",
"value": "1850"
},
{
"id": 3,
"type": "value",
"value": "1900"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
3,735 | image_and_language | bird:train.json:7487 | To which predicted relation class does the self-relation of the object sample in image no.5 belong? | 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 | [
"To",
"which",
"predicted",
"relation",
"class",
"does",
"the",
"self",
"-",
"relation",
"of",
"the",
"object",
"sample",
"in",
"image",
"no.5",
"belong",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "obj1_sample_id"
},
{
"id": 7,
"type": "column",
"value": "obj2_sample_id"
},
{
"id": 3,
"type": "column",
"value": "pred_class_id"
},
{
"id": 1,
"type": "table",
"value": "pred_classes"
},
{
"id": 0,
"type... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
3,736 | superhero | bird:dev.json:814 | List the skin colour of the superheroes with 100 attribute value. | SELECT DISTINCT T2.colour FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.skin_colour_id = T2.id INNER JOIN hero_attribute AS T3 ON T1.id = T3.hero_id WHERE T3.attribute_value = 100 | [
"List",
"the",
"skin",
"colour",
"of",
"the",
"superheroes",
"with",
"100",
"attribute",
"value",
"."
] | [
{
"id": 2,
"type": "column",
"value": "attribute_value"
},
{
"id": 1,
"type": "table",
"value": "hero_attribute"
},
{
"id": 8,
"type": "column",
"value": "skin_colour_id"
},
{
"id": 4,
"type": "table",
"value": "superhero"
},
{
"id": 7,
"type":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity... | [
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O"
] |
3,737 | student_1 | spider:train_spider.json:4056 | Report the first name and last name of all the students. | SELECT DISTINCT firstname , lastname FROM list | [
"Report",
"the",
"first",
"name",
"and",
"last",
"name",
"of",
"all",
"the",
"students",
"."
] | [
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 2,
"type": "column",
"value": "lastname"
},
{
"id": 0,
"type": "table",
"value": "list"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
3,738 | app_store | bird:train.json:2570 | What is the rating of Dragon Ball Legends and how many users dislike this App? | SELECT T1.Rating, COUNT(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Dragon Ball Legends' AND CAST(Sentiment_Polarity AS INTEGER) < -0.5 | [
"What",
"is",
"the",
"rating",
"of",
"Dragon",
"Ball",
"Legends",
"and",
"how",
"many",
"users",
"dislike",
"this",
"App",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Dragon Ball Legends"
},
{
"id": 3,
"type": "column",
"value": "sentiment_polarity"
},
{
"id": 2,
"type": "table",
"value": "user_reviews"
},
{
"id": 1,
"type": "table",
"value": "playstore"
},
{
"id": 0,
"t... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
3,739 | chicago_crime | bird:train.json:8637 | What is the name of the community that has the highest number of crimes related to prostitution? | SELECT T3.community_area_name FROM Crime AS T1 INNER JOIN IUCR AS T2 ON T1.iucr_no = T2.iucr_no INNER JOIN Community_Area AS T3 ON T1.community_area_no = T3.community_area_no WHERE T2.primary_description = 'PROSTITUTION' GROUP BY T1.iucr_no ORDER BY T1.case_number DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"community",
"that",
"has",
"the",
"highest",
"number",
"of",
"crimes",
"related",
"to",
"prostitution",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "community_area_name"
},
{
"id": 3,
"type": "column",
"value": "primary_description"
},
{
"id": 8,
"type": "column",
"value": "community_area_no"
},
{
"id": 2,
"type": "table",
"value": "community_area"
},
{
"i... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
3,740 | soccer_2 | spider:train_spider.json:4990 | What are the names of schools with the top 3 largest size? | SELECT cName FROM college ORDER BY enr DESC LIMIT 3 | [
"What",
"are",
"the",
"names",
"of",
"schools",
"with",
"the",
"top",
"3",
"largest",
"size",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "college"
},
{
"id": 1,
"type": "column",
"value": "cname"
},
{
"id": 2,
"type": "column",
"value": "enr"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,741 | cre_Doc_Workflow | bird:test.json:2028 | Show all author names and number of documents corresponding to each. | SELECT author_name , count(*) FROM Documents GROUP BY author_name | [
"Show",
"all",
"author",
"names",
"and",
"number",
"of",
"documents",
"corresponding",
"to",
"each",
"."
] | [
{
"id": 1,
"type": "column",
"value": "author_name"
},
{
"id": 0,
"type": "table",
"value": "documents"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
3,742 | art_1 | bird:test.json:1235 | In what locations and on what mediums are the paintings created by the artist with the first name Pablo? | SELECT T2.location , T2.medium FROM artists AS T1 JOIN paintings AS T2 ON T1.artistID = T2.painterID WHERE T1.fname = "Pablo" | [
"In",
"what",
"locations",
"and",
"on",
"what",
"mediums",
"are",
"the",
"paintings",
"created",
"by",
"the",
"artist",
"with",
"the",
"first",
"name",
"Pablo",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "paintings"
},
{
"id": 7,
"type": "column",
"value": "painterid"
},
{
"id": 0,
"type": "column",
"value": "location"
},
{
"id": 6,
"type": "column",
"value": "artistid"
},
{
"id": 2,
"type": "table",
"va... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entit... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
3,743 | world | bird:train.json:7913 | What is the life expectancy of the people living in Calama city? | SELECT T2.LifeExpectancy FROM City AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T1.Name = 'Calama' | [
"What",
"is",
"the",
"life",
"expectancy",
"of",
"the",
"people",
"living",
"in",
"Calama",
"city",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "lifeexpectancy"
},
{
"id": 5,
"type": "column",
"value": "countrycode"
},
{
"id": 2,
"type": "table",
"value": "country"
},
{
"id": 4,
"type": "value",
"value": "Calama"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
3,744 | flight_1 | spider:train_spider.json:408 | What place has the most flights coming from there? | SELECT origin FROM Flight GROUP BY origin ORDER BY count(*) DESC LIMIT 1 | [
"What",
"place",
"has",
"the",
"most",
"flights",
"coming",
"from",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "flight"
},
{
"id": 1,
"type": "column",
"value": "origin"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
3,745 | ice_hockey_draft | bird:train.json:6921 | Among the players that weigh more than 90 kg, how many of them have a position of defense? | SELECT COUNT(T1.ELITEID) FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id WHERE T2.weight_in_kg > 90 AND T1.position_info = 'D' | [
"Among",
"the",
"players",
"that",
"weigh",
"more",
"than",
"90",
"kg",
",",
"how",
"many",
"of",
"them",
"have",
"a",
"position",
"of",
"defense",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "position_info"
},
{
"id": 5,
"type": "column",
"value": "weight_in_kg"
},
{
"id": 1,
"type": "table",
"value": "weight_info"
},
{
"id": 0,
"type": "table",
"value": "playerinfo"
},
{
"id": 4,
"type": "colu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
3,746 | music_1 | spider:train_spider.json:3626 | What are the names of the different artists that have produced a song in English but have never receieved a rating higher than 8? | SELECT DISTINCT artist_name FROM song WHERE languages = "english" EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating > 8 | [
"What",
"are",
"the",
"names",
"of",
"the",
"different",
"artists",
"that",
"have",
"produced",
"a",
"song",
"in",
"English",
"but",
"have",
"never",
"receieved",
"a",
"rating",
"higher",
"than",
"8",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "artist_name"
},
{
"id": 2,
"type": "column",
"value": "languages"
},
{
"id": 3,
"type": "column",
"value": "english"
},
{
"id": 4,
"type": "column",
"value": "rating"
},
{
"id": 0,
"type": "table",
"va... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": [
20
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
3,747 | olympics | bird:train.json:5050 | Which sport did John Aalberg participate in? | SELECT DISTINCT T1.sport_name FROM sport AS T1 INNER JOIN event AS T2 ON T1.id = T2.sport_id INNER JOIN competitor_event AS T3 ON T2.id = T3.event_id INNER JOIN games_competitor AS T4 ON T3.competitor_id = T4.id INNER JOIN person AS T5 ON T4.person_id = T5.id WHERE T5.full_name = 'John Aalberg' | [
"Which",
"sport",
"did",
"John",
"Aalberg",
"participate",
"in",
"?"
] | [
{
"id": 4,
"type": "table",
"value": "games_competitor"
},
{
"id": 7,
"type": "table",
"value": "competitor_event"
},
{
"id": 8,
"type": "column",
"value": "competitor_id"
},
{
"id": 3,
"type": "value",
"value": "John Aalberg"
},
{
"id": 0,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3,
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O"
] |
3,748 | country_language | bird:test.json:1393 | What are the names of languages that are not the official language of any country? | SELECT name FROM languages WHERE id NOT IN (SELECT language_id FROM official_languages) | [
"What",
"are",
"the",
"names",
"of",
"languages",
"that",
"are",
"not",
"the",
"official",
"language",
"of",
"any",
"country",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "official_languages"
},
{
"id": 4,
"type": "column",
"value": "language_id"
},
{
"id": 0,
"type": "table",
"value": "languages"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
3,749 | card_games | bird:dev.json:411 | To which artist does the card with the text "Das perfekte Gegenmittel zu einer dichten Formation" belong? | SELECT DISTINCT T1.artist FROM cards AS T1 INNER JOIN foreign_data AS T2 ON T1.uuid = T2.uuid WHERE T2.flavorText LIKE '%DAS perfekte Gegenmittel zu einer dichten Formation%' | [
"To",
"which",
"artist",
"does",
"the",
"card",
"with",
"the",
"text",
"\"",
"Das",
"perfekte",
"Gegenmittel",
"zu",
"einer",
"dichten",
"Formation",
"\"",
"belong",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "%DAS perfekte Gegenmittel zu einer dichten Formation%"
},
{
"id": 2,
"type": "table",
"value": "foreign_data"
},
{
"id": 3,
"type": "column",
"value": "flavortext"
},
{
"id": 0,
"type": "column",
"value": "artist"
},... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10,
11,
12,
13,
14... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O"
] |
3,751 | bike_share_1 | bird:train.json:9038 | Find the average ride duration during the rain of more than 0.8 inches. | SELECT AVG(T1.duration) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code WHERE (T2.events = 'Rain' AND T2.precipitation_inches > 0.8) OR (T2.events = 'rain' AND T2.precipitation_inches > 0.8) | [
"Find",
"the",
"average",
"ride",
"duration",
"during",
"the",
"rain",
"of",
"more",
"than",
"0.8",
"inches",
"."
] | [
{
"id": 6,
"type": "column",
"value": "precipitation_inches"
},
{
"id": 2,
"type": "column",
"value": "duration"
},
{
"id": 3,
"type": "column",
"value": "zip_code"
},
{
"id": 1,
"type": "table",
"value": "weather"
},
{
"id": 4,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
3,753 | student_loan | bird:train.json:4455 | List all the navy students who are disabled. | SELECT T1.name FROM disabled AS T1 INNER JOIN enlist AS T2 ON T1.name = T2.name WHERE T2.organ = 'navy' | [
"List",
"all",
"the",
"navy",
"students",
"who",
"are",
"disabled",
"."
] | [
{
"id": 1,
"type": "table",
"value": "disabled"
},
{
"id": 2,
"type": "table",
"value": "enlist"
},
{
"id": 3,
"type": "column",
"value": "organ"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "value",
"value": "navy"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
0
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"... | [
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
3,754 | customers_and_orders | bird:test.json:273 | What is the customer address, customer phone, and customer email for Jeromy? | SELECT customer_address , customer_phone , customer_email FROM Customers WHERE customer_name = "Jeromy" | [
"What",
"is",
"the",
"customer",
"address",
",",
"customer",
"phone",
",",
"and",
"customer",
"email",
"for",
"Jeromy",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "customer_address"
},
{
"id": 2,
"type": "column",
"value": "customer_phone"
},
{
"id": 3,
"type": "column",
"value": "customer_email"
},
{
"id": 4,
"type": "column",
"value": "customer_name"
},
{
"id": 0,
... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
6
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"O"
] |
3,755 | hockey | bird:train.json:7762 | Which team got the most wins in the Stanley Cup finals? | SELECT T2.name FROM TeamsSC AS T1 INNER JOIN Teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year GROUP BY T2.name ORDER BY SUM(T1.W) DESC LIMIT 1 | [
"Which",
"team",
"got",
"the",
"most",
"wins",
"in",
"the",
"Stanley",
"Cup",
"finals",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "teamssc"
},
{
"id": 2,
"type": "table",
"value": "teams"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "column",
"value": "tmid"
},
{
"id": 5,
"type": "column",
"value": "year"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
3,756 | hockey | bird:train.json:7717 | Who was the coach for the team which had the most bench minors penalty in 2003? | 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 | [
"Who",
"was",
"the",
"coach",
"for",
"the",
"team",
"which",
"had",
"the",
"most",
"bench",
"minors",
"penalty",
"in",
"2003",
"?"
] | [
{
"id": 8,
"type": "column",
"value": "benchminor"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 6,
"type": "table",
"value": "coaches"
},
{
"id": 7,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1,
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id": 5,
"tok... | [
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.