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 |
|---|---|---|---|---|---|---|---|---|
12,843 | swimming | spider:train_spider.json:5627 | Find the names of the swimmers who have both "win" and "loss" results in the record. | SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id = t2.swimmer_id WHERE RESULT = 'Win' INTERSECT SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id = t2.swimmer_id WHERE RESULT = 'Loss' | [
"Find",
"the",
"names",
"of",
"the",
"swimmers",
"who",
"have",
"both",
"\"",
"win",
"\"",
"and",
"\"",
"loss",
"\"",
"results",
"in",
"the",
"record",
"."
] | [
{
"id": 7,
"type": "column",
"value": "swimmer_id"
},
{
"id": 1,
"type": "table",
"value": "swimmer"
},
{
"id": 2,
"type": "table",
"value": "record"
},
{
"id": 3,
"type": "column",
"value": "result"
},
{
"id": 0,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
19
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
... | [
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
12,844 | formula_1 | spider:train_spider.json:2194 | What are the names of all races held after 2000 in Spain? | SELECT DISTINCT T1.name FROM races AS T1 JOIN circuits AS T2 ON T1.circuitid = T2.circuitid WHERE T2.country = "Spain" AND T1.year > 2000 | [
"What",
"are",
"the",
"names",
"of",
"all",
"races",
"held",
"after",
"2000",
"in",
"Spain",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "circuitid"
},
{
"id": 2,
"type": "table",
"value": "circuits"
},
{
"id": 4,
"type": "column",
"value": "country"
},
{
"id": 1,
"type": "table",
"value": "races"
},
{
"id": 5,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"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",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
12,845 | european_football_1 | bird:train.json:2749 | Which 2 Scottish teams scored 10 goals playing as a local team and in which seasons? | SELECT T1.HomeTeam FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.country = 'Scotland' AND T1.FTHG = 10 | [
"Which",
"2",
"Scottish",
"teams",
"scored",
"10",
"goals",
"playing",
"as",
"a",
"local",
"team",
"and",
"in",
"which",
"seasons",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "divisions"
},
{
"id": 0,
"type": "column",
"value": "hometeam"
},
{
"id": 4,
"type": "column",
"value": "division"
},
{
"id": 6,
"type": "value",
"value": "Scotland"
},
{
"id": 5,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"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-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
12,847 | formula_1 | spider:train_spider.json:2169 | What is the id, forname and surname of the driver who had the first position in terms of laptime at least twice? | SELECT T1.driverid , T1.forename , T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE POSITION = '1' GROUP BY T1.driverid HAVING count(*) >= 2 | [
"What",
"is",
"the",
"i",
"d",
",",
"forname",
"and",
"surname",
"of",
"the",
"driver",
"who",
"had",
"the",
"first",
"position",
"in",
"terms",
"of",
"laptime",
"at",
"least",
"twice",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "driverid"
},
{
"id": 1,
"type": "column",
"value": "forename"
},
{
"id": 4,
"type": "table",
"value": "laptimes"
},
{
"id": 5,
"type": "column",
"value": "position"
},
{
"id": 2,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
20
]
},
{
"entit... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
12,848 | game_injury | spider:train_spider.json:1289 | Show the name, average attendance, total attendance for stadiums where no accidents happened. | SELECT name , average_attendance , total_attendance FROM stadium EXCEPT SELECT T2.name , T2.average_attendance , T2.total_attendance FROM game AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.id JOIN injury_accident AS T3 ON T1.id = T3.game_id | [
"Show",
"the",
"name",
",",
"average",
"attendance",
",",
"total",
"attendance",
"for",
"stadiums",
"where",
"no",
"accidents",
"happened",
"."
] | [
{
"id": 2,
"type": "column",
"value": "average_attendance"
},
{
"id": 3,
"type": "column",
"value": "total_attendance"
},
{
"id": 4,
"type": "table",
"value": "injury_accident"
},
{
"id": 8,
"type": "column",
"value": "stadium_id"
},
{
"id": 0,
... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
... | [
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
12,849 | superhero | bird:dev.json:751 | List down at least five superpowers of male superheroes. | 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 T3.id = T2.power_id INNER JOIN gender AS T4 ON T4.id = T1.gender_id WHERE T4.gender = 'Male' LIMIT 5 | [
"List",
"down",
"at",
"least",
"five",
"superpowers",
"of",
"male",
"superheroes",
"."
] | [
{
"id": 0,
"type": "column",
"value": "power_name"
},
{
"id": 4,
"type": "table",
"value": "superpower"
},
{
"id": 8,
"type": "table",
"value": "hero_power"
},
{
"id": 6,
"type": "column",
"value": "gender_id"
},
{
"id": 7,
"type": "table",
... | [
{
"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",
"B-TABLE",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
12,850 | language_corpus | bird:train.json:5810 | What is the word that occurs the most in the Catalan language? | SELECT word FROM words WHERE occurrences = ( SELECT MAX(occurrences) FROM words ) | [
"What",
"is",
"the",
"word",
"that",
"occurs",
"the",
"most",
"in",
"the",
"Catalan",
"language",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "occurrences"
},
{
"id": 0,
"type": "table",
"value": "words"
},
{
"id": 1,
"type": "column",
"value": "word"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,851 | shipping | bird:train.json:5634 | What is the brand of truck used in shipment id 1011? | SELECT T1.make FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id WHERE T2.ship_id = '1011' | [
"What",
"is",
"the",
"brand",
"of",
"truck",
"used",
"in",
"shipment",
"i",
"d",
"1011",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "shipment"
},
{
"id": 5,
"type": "column",
"value": "truck_id"
},
{
"id": 3,
"type": "column",
"value": "ship_id"
},
{
"id": 1,
"type": "table",
"value": "truck"
},
{
"id": 0,
"type": "column",
"value": ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
12,852 | store_1 | spider:train_spider.json:618 | What is the composer who created the track "Fast As a Shark"? | SELECT composer FROM tracks WHERE name = "Fast As a Shark"; | [
"What",
"is",
"the",
"composer",
"who",
"created",
"the",
"track",
"\"",
"Fast",
"As",
"a",
"Shark",
"\"",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "Fast As a Shark"
},
{
"id": 1,
"type": "column",
"value": "composer"
},
{
"id": 0,
"type": "table",
"value": "tracks"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10,
11,
12
]
},
{
"entity_id": 4,
"token_idxs": []
},... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O"
] |
12,853 | sakila_1 | spider:train_spider.json:2964 | How many films are there in each category? List the genre name, genre id and the count. | SELECT T2.name , T1.category_id , count(*) FROM film_category AS T1 JOIN category AS T2 ON T1.category_id = T2.category_id GROUP BY T1.category_id | [
"How",
"many",
"films",
"are",
"there",
"in",
"each",
"category",
"?",
"List",
"the",
"genre",
"name",
",",
"genre",
"i",
"d",
"and",
"the",
"count",
"."
] | [
{
"id": 2,
"type": "table",
"value": "film_category"
},
{
"id": 0,
"type": "column",
"value": "category_id"
},
{
"id": 3,
"type": "table",
"value": "category"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"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",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,854 | art_1 | bird:test.json:1306 | List the painting id, location and title of the medium oil paintings ordered by year. | SELECT paintingID , title , LOCATION FROM paintings WHERE medium = "oil" ORDER BY YEAR | [
"List",
"the",
"painting",
"i",
"d",
",",
"location",
"and",
"title",
"of",
"the",
"medium",
"oil",
"paintings",
"ordered",
"by",
"year",
"."
] | [
{
"id": 1,
"type": "column",
"value": "paintingid"
},
{
"id": 0,
"type": "table",
"value": "paintings"
},
{
"id": 3,
"type": "column",
"value": "location"
},
{
"id": 4,
"type": "column",
"value": "medium"
},
{
"id": 2,
"type": "column",
"va... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
12,856 | address_1 | bird:test.json:773 | How many cities are in Canada? | SELECT count(*) FROM City WHERE country = "CANADA" | [
"How",
"many",
"cities",
"are",
"in",
"Canada",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 2,
"type": "column",
"value": "CANADA"
},
{
"id": 0,
"type": "table",
"value": "city"
}
] | [
{
"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": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
12,857 | hospital_1 | spider:train_spider.json:3939 | Find the number of distinct room types available. | SELECT count(DISTINCT roomtype) FROM room | [
"Find",
"the",
"number",
"of",
"distinct",
"room",
"types",
"available",
"."
] | [
{
"id": 1,
"type": "column",
"value": "roomtype"
},
{
"id": 0,
"type": "table",
"value": "room"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"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",
"B-COLUMN",
"O",
"O"
] |
12,858 | public_review_platform | bird:train.json:3832 | What are the attributes that business number 56 have? | SELECT T1.attribute_name FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T2.attribute_value LIKE 'TRUE' AND T2.business_id = 56 | [
"What",
"are",
"the",
"attributes",
"that",
"business",
"number",
"56",
"have",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "business_attributes"
},
{
"id": 4,
"type": "column",
"value": "attribute_value"
},
{
"id": 0,
"type": "column",
"value": "attribute_name"
},
{
"id": 3,
"type": "column",
"value": "attribute_id"
},
{
"id": 6,
... | [
{
"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-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
12,859 | legislator | bird:train.json:4776 | What are the Facebook, Twitter and YouTube usernames of Adam Kinzinger? | SELECT T2.facebook FROM current AS T1 INNER JOIN `social-media` AS T2 ON T2.bioguide = T1.bioguide_id WHERE T1.official_full_name = 'Adam Kinzinger' | [
"What",
"are",
"the",
"Facebook",
",",
"Twitter",
"and",
"YouTube",
"usernames",
"of",
"Adam",
"Kinzinger",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "official_full_name"
},
{
"id": 4,
"type": "value",
"value": "Adam Kinzinger"
},
{
"id": 2,
"type": "table",
"value": "social-media"
},
{
"id": 6,
"type": "column",
"value": "bioguide_id"
},
{
"id": 0,
"typ... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10,
11
]
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
12,860 | simpson_episodes | bird:train.json:4276 | Please list all of the episodes that aired in 2008 that have the highest number of votes for the maximum star rating. | SELECT T1.episode_id FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE SUBSTR(T1.air_date, 1, 4) = '2008' ORDER BY T2.votes DESC LIMIT 1; | [
"Please",
"list",
"all",
"of",
"the",
"episodes",
"that",
"aired",
"in",
"2008",
"that",
"have",
"the",
"highest",
"number",
"of",
"votes",
"for",
"the",
"maximum",
"star",
"rating",
"."
] | [
{
"id": 0,
"type": "column",
"value": "episode_id"
},
{
"id": 5,
"type": "column",
"value": "air_date"
},
{
"id": 1,
"type": "table",
"value": "episode"
},
{
"id": 4,
"type": "column",
"value": "votes"
},
{
"id": 2,
"type": "table",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,861 | olympics | bird:train.json:4958 | What was the medal that Coleen Dufresne got? | SELECT T4.medal_name FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id INNER JOIN competitor_event AS T3 ON T2.id = T3.competitor_id INNER JOIN medal AS T4 ON T3.medal_id = T4.id WHERE T1.full_name = 'Coleen Dufresne (-Stewner)' | [
"What",
"was",
"the",
"medal",
"that",
"Coleen",
"Dufresne",
"got",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Coleen Dufresne (-Stewner)"
},
{
"id": 4,
"type": "table",
"value": "competitor_event"
},
{
"id": 8,
"type": "table",
"value": "games_competitor"
},
{
"id": 9,
"type": "column",
"value": "competitor_id"
},
{
"i... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
12,862 | activity_1 | spider:train_spider.json:6754 | Which students are advised by Michael Goodrich? Give me their first and last names. | SELECT T2.fname , T2.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID = T2.advisor WHERE T1.fname = "Michael" AND T1.lname = "Goodrich" | [
"Which",
"students",
"are",
"advised",
"by",
"Michael",
"Goodrich",
"?",
"Give",
"me",
"their",
"first",
"and",
"last",
"names",
"."
] | [
{
"id": 7,
"type": "column",
"value": "Goodrich"
},
{
"id": 2,
"type": "table",
"value": "faculty"
},
{
"id": 3,
"type": "table",
"value": "student"
},
{
"id": 5,
"type": "column",
"value": "advisor"
},
{
"id": 6,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
12,864 | works_cycles | bird:train.json:7415 | Among the store contact employees, how many of them have a Vista credit card? | SELECT COUNT(T1.FirstName) FROM Person AS T1 INNER JOIN PersonCreditCard AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN CreditCard AS T3 ON T2.CreditCardID = T3.CreditCardID WHERE T3.CardType = 'Vista' AND T1.PersonType = 'SC' | [
"Among",
"the",
"store",
"contact",
"employees",
",",
"how",
"many",
"of",
"them",
"have",
"a",
"Vista",
"credit",
"card",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "personcreditcard"
},
{
"id": 9,
"type": "column",
"value": "businessentityid"
},
{
"id": 4,
"type": "column",
"value": "creditcardid"
},
{
"id": 0,
"type": "table",
"value": "creditcard"
},
{
"id": 7,
"type... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"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": [
14
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O"
] |
12,865 | donor | bird:train.json:3252 | Which school district was Future Einsteins Of America project located at? | SELECT T1.school_district FROM projects AS T1 INNER JOIN essays AS T2 ON T1.projectid = T2.projectid WHERE T2.title LIKE 'Future Einsteins Of America' | [
"Which",
"school",
"district",
"was",
"Future",
"Einsteins",
"Of",
"America",
"project",
"located",
"at",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Future Einsteins Of America"
},
{
"id": 0,
"type": "column",
"value": "school_district"
},
{
"id": 5,
"type": "column",
"value": "projectid"
},
{
"id": 1,
"type": "table",
"value": "projects"
},
{
"id": 2,
... | [
{
"entity_id": 0,
"token_idxs": [
1,
2
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4,
5,
6,
7
... | [
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"O",
"O"
] |
12,866 | customers_and_addresses | spider:train_spider.json:6089 | Find the city with post code 255. | SELECT city FROM addresses WHERE zip_postcode = 255 | [
"Find",
"the",
"city",
"with",
"post",
"code",
"255",
"."
] | [
{
"id": 2,
"type": "column",
"value": "zip_postcode"
},
{
"id": 0,
"type": "table",
"value": "addresses"
},
{
"id": 1,
"type": "column",
"value": "city"
},
{
"id": 3,
"type": "value",
"value": "255"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
12,867 | dorm_1 | spider:train_spider.json:5711 | What are the different dorm amenity names in alphabetical order? | SELECT amenity_name FROM dorm_amenity ORDER BY amenity_name | [
"What",
"are",
"the",
"different",
"dorm",
"amenity",
"names",
"in",
"alphabetical",
"order",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "dorm_amenity"
},
{
"id": 1,
"type": "column",
"value": "amenity_name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
12,868 | online_exams | bird:test.json:211 | Please show the email addresses of students and the dates of their answers in descending order of dates. | SELECT T2.Email_Adress , T1.Date_of_Answer FROM Student_Answers AS T1 JOIN Students AS T2 ON T1.Student_ID = T2.Student_ID ORDER BY T1.Date_of_Answer DESC | [
"Please",
"show",
"the",
"email",
"addresses",
"of",
"students",
"and",
"the",
"dates",
"of",
"their",
"answers",
"in",
"descending",
"order",
"of",
"dates",
"."
] | [
{
"id": 2,
"type": "table",
"value": "student_answers"
},
{
"id": 1,
"type": "column",
"value": "date_of_answer"
},
{
"id": 0,
"type": "column",
"value": "email_adress"
},
{
"id": 4,
"type": "column",
"value": "student_id"
},
{
"id": 3,
"type":... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
9,
10,
11,
12
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs"... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,869 | architecture | spider:train_spider.json:6945 | How many architects are female? | SELECT count(*) FROM architect WHERE gender = 'female' | [
"How",
"many",
"architects",
"are",
"female",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "architect"
},
{
"id": 1,
"type": "column",
"value": "gender"
},
{
"id": 2,
"type": "value",
"value": "female"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
12,870 | sports_competition | spider:train_spider.json:3338 | What is the total number of clubs? | SELECT count(*) FROM club | [
"What",
"is",
"the",
"total",
"number",
"of",
"clubs",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "club"
}
] | [
{
"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"
] |
12,871 | company_office | spider:train_spider.json:4561 | List the name of a building along with the name of a company whose office is in the building. | SELECT T3.name , T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id = T2.id JOIN Companies AS T3 ON T1.company_id = T3.id | [
"List",
"the",
"name",
"of",
"a",
"building",
"along",
"with",
"the",
"name",
"of",
"a",
"company",
"whose",
"office",
"is",
"in",
"the",
"building",
"."
] | [
{
"id": 2,
"type": "table",
"value": "office_locations"
},
{
"id": 6,
"type": "column",
"value": "building_id"
},
{
"id": 4,
"type": "column",
"value": "company_id"
},
{
"id": 1,
"type": "table",
"value": "companies"
},
{
"id": 3,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
14,
15
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
12,872 | college_1 | spider:train_spider.json:3308 | What are the first names and office of the professors who are in the history department and have a Ph.D? | SELECT T1.emp_fname , T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num = T2.emp_num JOIN department AS T3 ON T3.dept_code = T2.dept_code WHERE T3.dept_name = 'History' AND T2.prof_high_degree = 'Ph.D.' | [
"What",
"are",
"the",
"first",
"names",
"and",
"office",
"of",
"the",
"professors",
"who",
"are",
"in",
"the",
"history",
"department",
"and",
"have",
"a",
"Ph",
".",
"D",
"?"
] | [
{
"id": 8,
"type": "column",
"value": "prof_high_degree"
},
{
"id": 1,
"type": "column",
"value": "prof_office"
},
{
"id": 2,
"type": "table",
"value": "department"
},
{
"id": 0,
"type": "column",
"value": "emp_fname"
},
{
"id": 4,
"type": "tab... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
12,874 | store_1 | spider:train_spider.json:584 | What is the title of the album that was released by the artist whose name has the phrase 'Led'? | SELECT T2.title FROM artists AS T1 JOIN albums AS T2 ON T1.id = T2.artist_id WHERE T1.name LIKE '%Led%' | [
"What",
"is",
"the",
"title",
"of",
"the",
"album",
"that",
"was",
"released",
"by",
"the",
"artist",
"whose",
"name",
"has",
"the",
"phrase",
"'",
"Led",
"'",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "artist_id"
},
{
"id": 1,
"type": "table",
"value": "artists"
},
{
"id": 2,
"type": "table",
"value": "albums"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 4,
"type": "value",
"value": "%... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
12,875 | college_3 | spider:train_spider.json:4677 | Find the rank of the faculty that the fewest faculties belong to. | SELECT Rank FROM FACULTY GROUP BY Rank ORDER BY count(*) ASC LIMIT 1 | [
"Find",
"the",
"rank",
"of",
"the",
"faculty",
"that",
"the",
"fewest",
"faculties",
"belong",
"to",
"."
] | [
{
"id": 0,
"type": "table",
"value": "faculty"
},
{
"id": 1,
"type": "column",
"value": "rank"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,876 | student_1 | spider:train_spider.json:4080 | For each grade, report the grade, the number of classrooms in which it is taught and the total number of students in the grade. | SELECT grade , count(DISTINCT classroom) , count(*) FROM list GROUP BY grade | [
"For",
"each",
"grade",
",",
"report",
"the",
"grade",
",",
"the",
"number",
"of",
"classrooms",
"in",
"which",
"it",
"is",
"taught",
"and",
"the",
"total",
"number",
"of",
"students",
"in",
"the",
"grade",
"."
] | [
{
"id": 2,
"type": "column",
"value": "classroom"
},
{
"id": 1,
"type": "column",
"value": "grade"
},
{
"id": 0,
"type": "table",
"value": "list"
}
] | [
{
"entity_id": 0,
"token_idxs": [
15
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,877 | college_1 | spider:train_spider.json:3246 | What are the first names of all students who took ACCT-211 and received a C? | SELECT T3.stu_fname FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code = T2.class_code JOIN student AS T3 ON T2.stu_num = T3.stu_num WHERE T1.crs_code = 'ACCT-211' AND T2.enroll_grade = 'C' | [
"What",
"are",
"the",
"first",
"names",
"of",
"all",
"students",
"who",
"took",
"ACCT-211",
"and",
"received",
"a",
"C",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "enroll_grade"
},
{
"id": 9,
"type": "column",
"value": "class_code"
},
{
"id": 0,
"type": "column",
"value": "stu_fname"
},
{
"id": 5,
"type": "column",
"value": "crs_code"
},
{
"id": 6,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
12,878 | shooting | bird:train.json:2468 | Which are the cases where the subject are female. List the case number, subject status and weapon. | SELECT T1.case_number, T1.subject_statuses, T1.subject_weapon FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T2.gender = 'F' | [
"Which",
"are",
"the",
"cases",
"where",
"the",
"subject",
"are",
"female",
".",
"List",
"the",
"case",
"number",
",",
"subject",
"status",
"and",
"weapon",
"."
] | [
{
"id": 1,
"type": "column",
"value": "subject_statuses"
},
{
"id": 2,
"type": "column",
"value": "subject_weapon"
},
{
"id": 0,
"type": "column",
"value": "case_number"
},
{
"id": 3,
"type": "table",
"value": "incidents"
},
{
"id": 4,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
12,
13
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
16,
17,
18
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
15
]
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
12,879 | soccer_3 | bird:test.json:37 | How many different countries are players from? | SELECT COUNT (DISTINCT Country) FROM player | [
"How",
"many",
"different",
"countries",
"are",
"players",
"from",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 0,
"type": "table",
"value": "player"
}
] | [
{
"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"
] |
12,880 | dorm_1 | spider:train_spider.json:5692 | Find the name of dorms that do not have any amenity | SELECT dorm_name FROM dorm WHERE dormid NOT IN (SELECT dormid FROM has_amenity) | [
"Find",
"the",
"name",
"of",
"dorms",
"that",
"do",
"not",
"have",
"any",
"amenity"
] | [
{
"id": 3,
"type": "table",
"value": "has_amenity"
},
{
"id": 1,
"type": "column",
"value": "dorm_name"
},
{
"id": 2,
"type": "column",
"value": "dormid"
},
{
"id": 0,
"type": "table",
"value": "dorm"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE"
] |
12,881 | coffee_shop | spider:train_spider.json:795 | Which address has both members younger than 30 and members older than 40? | SELECT address FROM member WHERE age < 30 INTERSECT SELECT address FROM member WHERE age > 40 | [
"Which",
"address",
"has",
"both",
"members",
"younger",
"than",
"30",
"and",
"members",
"older",
"than",
"40",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "address"
},
{
"id": 0,
"type": "table",
"value": "member"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "30"
},
{
"id": 4,
"type": "value",
"value": "40"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity... | [
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
12,882 | icfp_1 | spider:train_spider.json:2907 | Which papers have the substring "Database" in their titles? Show the titles of the papers. | SELECT title FROM papers WHERE title LIKE "%Database%" | [
"Which",
"papers",
"have",
"the",
"substring",
"\"",
"Database",
"\"",
"in",
"their",
"titles",
"?",
"Show",
"the",
"titles",
"of",
"the",
"papers",
"."
] | [
{
"id": 2,
"type": "column",
"value": "%Database%"
},
{
"id": 0,
"type": "table",
"value": "papers"
},
{
"id": 1,
"type": "column",
"value": "title"
}
] | [
{
"entity_id": 0,
"token_idxs": [
17
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
12,883 | financial | bird:dev.json:155 | How many percent of 'Gold' cards were issued prior to 1998? | SELECT CAST(SUM(type = 'gold' AND STRFTIME('%Y', issued) < '1998') AS REAL) * 100 / COUNT(card_id) FROM card | [
"How",
"many",
"percent",
"of",
"'",
"Gold",
"'",
"cards",
"were",
"issued",
"prior",
"to",
"1998",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "card_id"
},
{
"id": 7,
"type": "column",
"value": "issued"
},
{
"id": 0,
"type": "table",
"value": "card"
},
{
"id": 3,
"type": "column",
"value": "type"
},
{
"id": 4,
"type": "value",
"value": "gold"
... | [
{
"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": [
5
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
12,885 | donor | bird:train.json:3180 | Which project in Brooklyn that have the highest request of resources? State the the project ID including all of the name of item requested. | SELECT T1.projectid, T1.item_name FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.school_city LIKE 'Brooklyn' GROUP BY T1.item_name ORDER BY COUNT(T1.item_name) DESC LIMIT 1 | [
"Which",
"project",
"in",
"Brooklyn",
"that",
"have",
"the",
"highest",
"request",
"of",
"resources",
"?",
"State",
"the",
"the",
"project",
"ID",
"including",
"all",
"of",
"the",
"name",
"of",
"item",
"requested",
"."
] | [
{
"id": 4,
"type": "column",
"value": "school_city"
},
{
"id": 0,
"type": "column",
"value": "item_name"
},
{
"id": 1,
"type": "column",
"value": "projectid"
},
{
"id": 2,
"type": "table",
"value": "resources"
},
{
"id": 3,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
20,
21
]
},
{
"entity_id": 1,
"token_idxs": [
16
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
... | [
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
12,886 | film_rank | spider:train_spider.json:4127 | What are the countries for each market, ordered alphabetically? | SELECT Country FROM market ORDER BY Country ASC | [
"What",
"are",
"the",
"countries",
"for",
"each",
"market",
",",
"ordered",
"alphabetically",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 0,
"type": "table",
"value": "market"
}
] | [
{
"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"
] |
12,887 | mondial_geo | bird:train.json:8275 | Which country is Mountain Cerro Chirripo located in? Give the full name of the country. | SELECT DISTINCT T1.Name FROM country AS T1 INNER JOIN geo_mountain AS T2 ON T1.Code = T2.Country WHERE T2.Mountain = 'Cerro Chirripo' | [
"Which",
"country",
"is",
"Mountain",
"Cerro",
"Chirripo",
"located",
"in",
"?",
"Give",
"the",
"full",
"name",
"of",
"the",
"country",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Cerro Chirripo"
},
{
"id": 2,
"type": "table",
"value": "geo_mountain"
},
{
"id": 3,
"type": "column",
"value": "mountain"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
4,
5
]
},
{
"entity_id"... | [
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
12,888 | bike_share_1 | bird:train.json:9065 | How many trips made by a subscriber started in August, 2013 from a station that can hold more than 20 bikes? | SELECT COUNT(T2.id) FROM station AS T1 INNER JOIN trip AS T2 ON T1.id = T2.start_station_id WHERE T2.subscription_type = 'Subscriber' AND T2.start_date LIKE '8/%/2013%' AND T1.dock_count > 20 | [
"How",
"many",
"trips",
"made",
"by",
"a",
"subscriber",
"started",
"in",
"August",
",",
"2013",
"from",
"a",
"station",
"that",
"can",
"hold",
"more",
"than",
"20",
"bikes",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "subscription_type"
},
{
"id": 3,
"type": "column",
"value": "start_station_id"
},
{
"id": 5,
"type": "value",
"value": "Subscriber"
},
{
"id": 6,
"type": "column",
"value": "start_date"
},
{
"id": 8,
"type... | [
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
12,889 | student_assessment | spider:train_spider.json:68 | What details do we have on the students who registered for courses most recently? | SELECT T2.student_details FROM student_course_registrations AS T1 JOIN students AS T2 ON T1.student_id = T2.student_id ORDER BY T1.registration_date DESC LIMIT 1 | [
"What",
"details",
"do",
"we",
"have",
"on",
"the",
"students",
"who",
"registered",
"for",
"courses",
"most",
"recently",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "student_course_registrations"
},
{
"id": 3,
"type": "column",
"value": "registration_date"
},
{
"id": 0,
"type": "column",
"value": "student_details"
},
{
"id": 4,
"type": "column",
"value": "student_id"
},
{
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
12,890 | culture_company | spider:train_spider.json:6971 | How many books are there for each publisher? | SELECT publisher , count(*) FROM book_club GROUP BY publisher | [
"How",
"many",
"books",
"are",
"there",
"for",
"each",
"publisher",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "book_club"
},
{
"id": 1,
"type": "column",
"value": "publisher"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
12,891 | student_loan | bird:train.json:4557 | What is the name of the organization which most of the students are enlisted in? | SELECT organ FROM ( SELECT organ, COUNT(organ) AS num FROM enlist GROUP BY organ ) T ORDER BY T.num DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"organization",
"which",
"most",
"of",
"the",
"students",
"are",
"enlisted",
"in",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "enlist"
},
{
"id": 0,
"type": "column",
"value": "organ"
},
{
"id": 1,
"type": "column",
"value": "num"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"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",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
12,892 | hockey | bird:train.json:7812 | Calculate the total points scored by team ID ANA and list down the coashes of the team. | SELECT SUM(T2.Pts), T1.coachID FROM Coaches AS T1 INNER JOIN Teams AS T2 ON T2.tmID = T1.tmID WHERE T2.tmID = 'ANA' GROUP BY T1.coachID | [
"Calculate",
"the",
"total",
"points",
"scored",
"by",
"team",
"ID",
"ANA",
"and",
"list",
"down",
"the",
"coashes",
"of",
"the",
"team",
"."
] | [
{
"id": 0,
"type": "column",
"value": "coachid"
},
{
"id": 1,
"type": "table",
"value": "coaches"
},
{
"id": 2,
"type": "table",
"value": "teams"
},
{
"id": 3,
"type": "column",
"value": "tmid"
},
{
"id": 4,
"type": "value",
"value": "ANA"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6,
7
]
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
12,893 | wrestler | spider:train_spider.json:1877 | What are the names of wrestlers who have never been eliminated? | SELECT Name FROM wrestler WHERE Wrestler_ID NOT IN (SELECT Wrestler_ID FROM elimination) | [
"What",
"are",
"the",
"names",
"of",
"wrestlers",
"who",
"have",
"never",
"been",
"eliminated",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "wrestler_id"
},
{
"id": 3,
"type": "table",
"value": "elimination"
},
{
"id": 0,
"type": "table",
"value": "wrestler"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"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": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
12,894 | retail_world | bird:train.json:6297 | How many employees have obtained a doctorate? | SELECT COUNT(EmployeeID) FROM Employees WHERE TitleOfCourtesy = 'Dr.' | [
"How",
"many",
"employees",
"have",
"obtained",
"a",
"doctorate",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "titleofcourtesy"
},
{
"id": 3,
"type": "column",
"value": "employeeid"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 2,
"type": "value",
"value": "Dr."
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
12,895 | book_press | bird:test.json:2007 | Find the number of books that are sold more than 1000 for each publisher. List the press name as well. | SELECT count(*) , t2.name FROM book AS t1 JOIN press AS t2 ON t1.press_id = t2.press_id WHERE sale_amount > 1000 GROUP BY t2.name | [
"Find",
"the",
"number",
"of",
"books",
"that",
"are",
"sold",
"more",
"than",
"1000",
"for",
"each",
"publisher",
".",
"List",
"the",
"press",
"name",
"as",
"well",
"."
] | [
{
"id": 3,
"type": "column",
"value": "sale_amount"
},
{
"id": 5,
"type": "column",
"value": "press_id"
},
{
"id": 2,
"type": "table",
"value": "press"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 1,
"type": "table",
"value": ... | [
{
"entity_id": 0,
"token_idxs": [
18
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
12,896 | music_1 | spider:train_spider.json:3572 | What are the names of the songs that have a lower rating than at least one blues song? | SELECT song_name FROM song WHERE rating < (SELECT max(rating) FROM song WHERE genre_is = "blues") | [
"What",
"are",
"the",
"names",
"of",
"the",
"songs",
"that",
"have",
"a",
"lower",
"rating",
"than",
"at",
"least",
"one",
"blues",
"song",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "song_name"
},
{
"id": 3,
"type": "column",
"value": "genre_is"
},
{
"id": 2,
"type": "column",
"value": "rating"
},
{
"id": 4,
"type": "column",
"value": "blues"
},
{
"id": 0,
"type": "table",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
17
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
12,897 | movie_2 | bird:test.json:1852 | What is all the information about the unrated movies? | SELECT * FROM movies WHERE rating = 'null' | [
"What",
"is",
"all",
"the",
"information",
"about",
"the",
"unrated",
"movies",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "movies"
},
{
"id": 1,
"type": "column",
"value": "rating"
},
{
"id": 2,
"type": "value",
"value": "null"
}
] | [
{
"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"
] |
12,898 | talkingdata | bird:train.json:1132 | Which category does the app id No.894384172610331000 belong to? | SELECT T1.category FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id WHERE T2.app_id = '894384172610331000' | [
"Which",
"category",
"does",
"the",
"app",
"i",
"d",
"No.894384172610331000",
"belong",
"to",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "894384172610331000"
},
{
"id": 1,
"type": "table",
"value": "label_categories"
},
{
"id": 2,
"type": "table",
"value": "app_labels"
},
{
"id": 0,
"type": "column",
"value": "category"
},
{
"id": 5,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
}... | [
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O"
] |
12,899 | thrombosis_prediction | bird:dev.json:1214 | List ID, sex and date of birth of patient whose total protein (TP) below the lower range of the normal index. | SELECT DISTINCT T1.ID, T1.SEX, T1.Birthday FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.TP < 6.0 | [
"List",
"ID",
",",
"sex",
"and",
"date",
"of",
"birth",
"of",
"patient",
"whose",
"total",
"protein",
"(",
"TP",
")",
"below",
"the",
"lower",
"range",
"of",
"the",
"normal",
"index",
"."
] | [
{
"id": 4,
"type": "table",
"value": "laboratory"
},
{
"id": 2,
"type": "column",
"value": "birthday"
},
{
"id": 3,
"type": "table",
"value": "patient"
},
{
"id": 1,
"type": "column",
"value": "sex"
},
{
"id": 6,
"type": "value",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,901 | car_road_race | bird:test.json:1332 | Please show the names of drivers and the number of races they participate in. | SELECT T1.Driver_Name , COUNT(*) FROM driver AS T1 JOIN race AS T2 ON T1.Driver_ID = T2.Driver_ID GROUP BY T1.Driver_ID | [
"Please",
"show",
"the",
"names",
"of",
"drivers",
"and",
"the",
"number",
"of",
"races",
"they",
"participate",
"in",
"."
] | [
{
"id": 1,
"type": "column",
"value": "driver_name"
},
{
"id": 0,
"type": "column",
"value": "driver_id"
},
{
"id": 2,
"type": "table",
"value": "driver"
},
{
"id": 3,
"type": "table",
"value": "race"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
12,902 | professional_basketball | bird:train.json:2871 | What is the full name of the team that the 'NBA Coach of the Year' 1992 winner coached? | SELECT name FROM teams AS T1 INNER JOIN coaches AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year INNER JOIN awards_coaches AS T3 ON T2.coachID = T3.coachID AND T2.year = T3.year WHERE T3.year = 1992 AND award = 'NBA Coach of the Year' | [
"What",
"is",
"the",
"full",
"name",
"of",
"the",
"team",
"that",
"the",
"'",
"NBA",
"Coach",
"of",
"the",
"Year",
"'",
"1992",
"winner",
"coached",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "NBA Coach of the Year"
},
{
"id": 1,
"type": "table",
"value": "awards_coaches"
},
{
"id": 3,
"type": "table",
"value": "coaches"
},
{
"id": 8,
"type": "column",
"value": "coachid"
},
{
"id": 2,
"type": "ta... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
12,903 | gas_company | spider:train_spider.json:2034 | What are the locations of all the gas stations ordered by opening year? | SELECT LOCATION FROM gas_station ORDER BY open_year | [
"What",
"are",
"the",
"locations",
"of",
"all",
"the",
"gas",
"stations",
"ordered",
"by",
"opening",
"year",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "gas_station"
},
{
"id": 2,
"type": "column",
"value": "open_year"
},
{
"id": 1,
"type": "column",
"value": "location"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7,
8
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
11,
12
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
12,904 | restaurant | bird:train.json:1772 | How many deli in Belmont have a review rating of 2 or more? | SELECT COUNT(id_restaurant) FROM generalinfo WHERE city = 'belmont' AND review > 2 AND food_type = 'deli' | [
"How",
"many",
"deli",
"in",
"Belmont",
"have",
"a",
"review",
"rating",
"of",
"2",
"or",
"more",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "id_restaurant"
},
{
"id": 0,
"type": "table",
"value": "generalinfo"
},
{
"id": 6,
"type": "column",
"value": "food_type"
},
{
"id": 3,
"type": "value",
"value": "belmont"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
12,905 | thrombosis_prediction | bird:dev.json:1165 | What is the age gap between the youngest and oldest patient with a normal triglyceride recorded? | SELECT STRFTIME('%Y', MAX(T1.Birthday)) - STRFTIME('%Y', MIN(T1.Birthday)) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.TG >= 200 | [
"What",
"is",
"the",
"age",
"gap",
"between",
"the",
"youngest",
"and",
"oldest",
"patient",
"with",
"a",
"normal",
"triglyceride",
"recorded",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "laboratory"
},
{
"id": 6,
"type": "column",
"value": "birthday"
},
{
"id": 0,
"type": "table",
"value": "patient"
},
{
"id": 3,
"type": "value",
"value": "200"
},
{
"id": 2,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"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",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,906 | soccer_2016 | bird:train.json:1913 | How many players are older than Gurkeerat Singh player? | SELECT SUM(CASE WHEN DOB < '1990-06-29' THEN 1 ELSE 0 END) FROM Player WHERE Player_Name != 'Gurkeerat Singh' | [
"How",
"many",
"players",
"are",
"older",
"than",
"Gurkeerat",
"Singh",
"player",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Gurkeerat Singh"
},
{
"id": 1,
"type": "column",
"value": "player_name"
},
{
"id": 6,
"type": "value",
"value": "1990-06-29"
},
{
"id": 0,
"type": "table",
"value": "player"
},
{
"id": 5,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
6,
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"en... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"O"
] |
12,907 | college_1 | spider:train_spider.json:3250 | What is the total number of professors with a Ph.D. ? | SELECT count(*) FROM professor WHERE prof_high_degree = 'Ph.D.' | [
"What",
"is",
"the",
"total",
"number",
"of",
"professors",
"with",
"a",
"Ph.D.",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "prof_high_degree"
},
{
"id": 0,
"type": "table",
"value": "professor"
},
{
"id": 2,
"type": "value",
"value": "Ph.D."
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
12,908 | movie_3 | bird:train.json:9256 | What is the average number of actors acted in comedy movies? | SELECT AVG(T1.actor_id) FROM film_actor AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T2.category_id = T3.category_id INNER JOIN actor AS T4 ON T4.actor_id = T1.actor_id WHERE T3.name = 'comedy' | [
"What",
"is",
"the",
"average",
"number",
"of",
"actors",
"acted",
"in",
"comedy",
"movies",
"?"
] | [
{
"id": 6,
"type": "table",
"value": "film_category"
},
{
"id": 7,
"type": "column",
"value": "category_id"
},
{
"id": 5,
"type": "table",
"value": "film_actor"
},
{
"id": 3,
"type": "column",
"value": "actor_id"
},
{
"id": 4,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
12,909 | wedding | spider:train_spider.json:1640 | Show the pair of male and female names in all weddings after year 2014 | SELECT T2.name , T3.name FROM wedding AS T1 JOIN people AS T2 ON T1.male_id = T2.people_id JOIN people AS T3 ON T1.female_id = T3.people_id WHERE T1.year > 2014 | [
"Show",
"the",
"pair",
"of",
"male",
"and",
"female",
"names",
"in",
"all",
"weddings",
"after",
"year",
"2014"
] | [
{
"id": 5,
"type": "column",
"value": "female_id"
},
{
"id": 6,
"type": "column",
"value": "people_id"
},
{
"id": 4,
"type": "table",
"value": "wedding"
},
{
"id": 7,
"type": "column",
"value": "male_id"
},
{
"id": 1,
"type": "table",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-VALUE"
] |
12,910 | college_1 | spider:train_spider.json:3226 | How many schools are there in the department? | SELECT count(DISTINCT school_code) FROM department | [
"How",
"many",
"schools",
"are",
"there",
"in",
"the",
"department",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "school_code"
},
{
"id": 0,
"type": "table",
"value": "department"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"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",
"B-TABLE",
"O"
] |
12,911 | network_2 | spider:train_spider.json:4455 | What are the names of all of Bob's friends? | SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name = T2.name WHERE T2.friend = 'Bob' | [
"What",
"are",
"the",
"names",
"of",
"all",
"of",
"Bob",
"'s",
"friends",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "personfriend"
},
{
"id": 1,
"type": "table",
"value": "person"
},
{
"id": 3,
"type": "column",
"value": "friend"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "value",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O"
] |
12,912 | talkingdata | bird:train.json:1105 | How many models does the VIVO phone brand released? | SELECT COUNT(device_id) FROM phone_brand_device_model2 WHERE phone_brand = 'vivo' | [
"How",
"many",
"models",
"does",
"the",
"VIVO",
"phone",
"brand",
"released",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "phone_brand_device_model2"
},
{
"id": 1,
"type": "column",
"value": "phone_brand"
},
{
"id": 3,
"type": "column",
"value": "device_id"
},
{
"id": 2,
"type": "value",
"value": "vivo"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"O"
] |
12,913 | tracking_orders | spider:train_spider.json:6892 | Find all the order items whose product id is 11. What are the order item ids? | SELECT order_item_id FROM order_items WHERE product_id = 11 | [
"Find",
"all",
"the",
"order",
"items",
"whose",
"product",
"i",
"d",
"is",
"11",
".",
"What",
"are",
"the",
"order",
"item",
"ids",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "order_item_id"
},
{
"id": 0,
"type": "table",
"value": "order_items"
},
{
"id": 2,
"type": "column",
"value": "product_id"
},
{
"id": 3,
"type": "value",
"value": "11"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
15,
16,
17
]
},
{
"entity_id": 2,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_i... | [
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
12,914 | olympics | bird:train.json:4956 | In which cities beginning with the letter M have the Olympic Games been held? | SELECT city_name FROM city WHERE city_name LIKE 'M%' | [
"In",
"which",
"cities",
"beginning",
"with",
"the",
"letter",
"M",
"have",
"the",
"Olympic",
"Games",
"been",
"held",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "city_name"
},
{
"id": 0,
"type": "table",
"value": "city"
},
{
"id": 2,
"type": "value",
"value": "M%"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,915 | mondial_geo | bird:train.json:8307 | What is the greatest length of the border between 2 independent countries? | SELECT MAX(T3.Length) FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country INNER JOIN borders AS T3 ON T3.Country1 = T2.Country WHERE T2.Independence IS NOT NULL | [
"What",
"is",
"the",
"greatest",
"length",
"of",
"the",
"border",
"between",
"2",
"independent",
"countries",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "independence"
},
{
"id": 4,
"type": "table",
"value": "politics"
},
{
"id": 5,
"type": "column",
"value": "country1"
},
{
"id": 0,
"type": "table",
"value": "borders"
},
{
"id": 3,
"type": "table",
"va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"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-COLUMN",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
12,916 | bakery_1 | bird:test.json:1589 | On which day did the bakery sell more than 100 dollars in total. | SELECT T3.date FROM goods AS T1 JOIN items AS T2 ON T1.id = T2.item JOIN receipts AS T3 ON T2.receipt = T3.ReceiptNumber GROUP BY T3.date HAVING sum(T1.price) > 100 | [
"On",
"which",
"day",
"did",
"the",
"bakery",
"sell",
"more",
"than",
"100",
"dollars",
"in",
"total",
"."
] | [
{
"id": 6,
"type": "column",
"value": "receiptnumber"
},
{
"id": 1,
"type": "table",
"value": "receipts"
},
{
"id": 5,
"type": "column",
"value": "receipt"
},
{
"id": 3,
"type": "table",
"value": "goods"
},
{
"id": 4,
"type": "table",
"valu... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"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",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
12,917 | college_completion | bird:train.json:3696 | From which institute is harder to graduate for a bachelor, Amridge University or Auburn University? | SELECT chronname FROM institution_details WHERE chronname IN ('Amridge University', 'Auburn University') ORDER BY grad_100_value LIMIT 1 | [
"From",
"which",
"institute",
"is",
"harder",
"to",
"graduate",
"for",
"a",
"bachelor",
",",
"Amridge",
"University",
"or",
"Auburn",
"University",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "institution_details"
},
{
"id": 2,
"type": "value",
"value": "Amridge University"
},
{
"id": 3,
"type": "value",
"value": "Auburn University"
},
{
"id": 4,
"type": "column",
"value": "grad_100_value"
},
{
"id":... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11,
12
]
},
{
"entity_id": 3,
"token_idxs": [
14,
15
]
},
{
"entity_id": 4,
"token_idxs": []
}... | [
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
12,918 | hockey | bird:train.json:7778 | In which year did the Montreal Canadiens have 49 penalty minutes in the Stanley Cup finals? Was it 1924, 1923 or 1918? | SELECT T1.year FROM Teams AS T1 INNER JOIN TeamsSC AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.name = 'Montreal Canadiens' AND T2.PIM = 49 | [
"In",
"which",
"year",
"did",
"the",
"Montreal",
"Canadiens",
"have",
"49",
"penalty",
"minutes",
"in",
"the",
"Stanley",
"Cup",
"finals",
"?",
"Was",
"it",
"1924",
",",
"1923",
"or",
"1918",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Montreal Canadiens"
},
{
"id": 2,
"type": "table",
"value": "teamssc"
},
{
"id": 1,
"type": "table",
"value": "teams"
},
{
"id": 0,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5,
6
]
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
12,919 | music_4 | spider:train_spider.json:6171 | What are the songs in volumes associated with the artist aged 32 or older? | SELECT T2.Song FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID = T2.Artist_ID WHERE T1.age >= 32 | [
"What",
"are",
"the",
"songs",
"in",
"volumes",
"associated",
"with",
"the",
"artist",
"aged",
"32",
"or",
"older",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "artist_id"
},
{
"id": 1,
"type": "table",
"value": "artist"
},
{
"id": 2,
"type": "table",
"value": "volume"
},
{
"id": 0,
"type": "column",
"value": "song"
},
{
"id": 3,
"type": "column",
"value": "ag... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O"
] |
12,920 | codebase_comments | bird:train.json:628 | List all the methods with a solution with a "636449700980488000" processed time. | SELECT DISTINCT T2.Name FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T1.ProcessedTime = 636449700980488000 | [
"List",
"all",
"the",
"methods",
"with",
"a",
"solution",
"with",
"a",
"\"",
"636449700980488000",
"\"",
"processed",
"time",
"."
] | [
{
"id": 4,
"type": "value",
"value": "636449700980488000"
},
{
"id": 3,
"type": "column",
"value": "processedtime"
},
{
"id": 6,
"type": "column",
"value": "solutionid"
},
{
"id": 1,
"type": "table",
"value": "solution"
},
{
"id": 2,
"type": "t... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
12,921 | superstore | bird:train.json:2451 | Please give the name of customers from the West region that bought exactly 8 items in their purchase. | SELECT DISTINCT T2.`Customer Name` FROM west_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` WHERE T1.Quantity = 8 AND T1.Region = 'West' | [
"Please",
"give",
"the",
"name",
"of",
"customers",
"from",
"the",
"West",
"region",
"that",
"bought",
"exactly",
"8",
"items",
"in",
"their",
"purchase",
"."
] | [
{
"id": 1,
"type": "table",
"value": "west_superstore"
},
{
"id": 0,
"type": "column",
"value": "Customer Name"
},
{
"id": 3,
"type": "column",
"value": "Customer ID"
},
{
"id": 4,
"type": "column",
"value": "quantity"
},
{
"id": 2,
"type": "ta... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"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",
"B-COLUMN",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O"
] |
12,922 | train_station | spider:train_spider.json:6605 | Show all locations and the total number of platforms and passengers for all train stations in each location. | SELECT LOCATION , sum(number_of_platforms) , sum(total_passengers) FROM station GROUP BY LOCATION | [
"Show",
"all",
"locations",
"and",
"the",
"total",
"number",
"of",
"platforms",
"and",
"passengers",
"for",
"all",
"train",
"stations",
"in",
"each",
"location",
"."
] | [
{
"id": 2,
"type": "column",
"value": "number_of_platforms"
},
{
"id": 3,
"type": "column",
"value": "total_passengers"
},
{
"id": 1,
"type": "column",
"value": "location"
},
{
"id": 0,
"type": "table",
"value": "station"
}
] | [
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
12,923 | advertising_agencies | bird:test.json:2109 | List all payment ids and its corresponding invoice ids and details. | SELECT payment_id , invoice_id , payment_details FROM Payments | [
"List",
"all",
"payment",
"ids",
"and",
"its",
"corresponding",
"invoice",
"ids",
"and",
"details",
"."
] | [
{
"id": 3,
"type": "column",
"value": "payment_details"
},
{
"id": 1,
"type": "column",
"value": "payment_id"
},
{
"id": 2,
"type": "column",
"value": "invoice_id"
},
{
"id": 0,
"type": "table",
"value": "payments"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O"
] |
12,924 | world_development_indicators | bird:train.json:2176 | How many footnotes have been made in year 1980 and what are the table names of these countries that made the footnotes? | SELECT COUNT(T2.Description) FROM Country AS T1 INNER JOIN FootNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T2.Year = 'YR1980' UNION ALL SELECT DISTINCT T1.TableName FROM Country AS T1 INNER JOIN FootNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T2.Year = 'YR1980' | [
"How",
"many",
"footnotes",
"have",
"been",
"made",
"in",
"year",
"1980",
"and",
"what",
"are",
"the",
"table",
"names",
"of",
"these",
"countries",
"that",
"made",
"the",
"footnotes",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "description"
},
{
"id": 6,
"type": "column",
"value": "countrycode"
},
{
"id": 1,
"type": "table",
"value": "footnotes"
},
{
"id": 4,
"type": "column",
"value": "tablename"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
17
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
13,
14
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
12,925 | roller_coaster | spider:train_spider.json:6211 | Please show the most common status of roller coasters. | SELECT Status FROM roller_coaster GROUP BY Status ORDER BY COUNT(*) DESC LIMIT 1 | [
"Please",
"show",
"the",
"most",
"common",
"status",
"of",
"roller",
"coasters",
"."
] | [
{
"id": 0,
"type": "table",
"value": "roller_coaster"
},
{
"id": 1,
"type": "column",
"value": "status"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7,
8
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
12,926 | european_football_1 | bird:train.json:2787 | How many times did the team Werder Bremen win as the away team in matches of the Bundesliga division? | SELECT COUNT(T1.Div) FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.name = 'Bundesliga' AND T1.AwayTeam = 'Werder Bremen' AND T1.FTR = 'A' | [
"How",
"many",
"times",
"did",
"the",
"team",
"Werder",
"Bremen",
"win",
"as",
"the",
"away",
"team",
"in",
"matches",
"of",
"the",
"Bundesliga",
"division",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "Werder Bremen"
},
{
"id": 5,
"type": "value",
"value": "Bundesliga"
},
{
"id": 1,
"type": "table",
"value": "divisions"
},
{
"id": 3,
"type": "column",
"value": "division"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
18
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
12,927 | college_2 | spider:train_spider.json:1335 | Count the number of classrooms in Lamberton. | SELECT count(*) FROM classroom WHERE building = 'Lamberton' | [
"Count",
"the",
"number",
"of",
"classrooms",
"in",
"Lamberton",
"."
] | [
{
"id": 0,
"type": "table",
"value": "classroom"
},
{
"id": 2,
"type": "value",
"value": "Lamberton"
},
{
"id": 1,
"type": "column",
"value": "building"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"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",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
12,928 | talkingdata | bird:train.json:1196 | Among the users who are above 20, how many users use device model of ELIFE E7 Mini? | 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 T2.device_model = 'ELIFE E7 Mini' AND T1.age > 20 | [
"Among",
"the",
"users",
"who",
"are",
"above",
"20",
",",
"how",
"many",
"users",
"use",
"device",
"model",
"of",
"ELIFE",
"E7",
"Mini",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "phone_brand_device_model2"
},
{
"id": 4,
"type": "value",
"value": "ELIFE E7 Mini"
},
{
"id": 3,
"type": "column",
"value": "device_model"
},
{
"id": 0,
"type": "table",
"value": "gender_age"
},
{
"id": 2,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
15,
16,
17
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
12,929 | hockey | bird:train.json:7618 | What is the average weight of players who have height greater than 72 inches. | SELECT AVG(weight) FROM Master WHERE height > 72 | [
"What",
"is",
"the",
"average",
"weight",
"of",
"players",
"who",
"have",
"height",
"greater",
"than",
"72",
"inches",
"."
] | [
{
"id": 0,
"type": "table",
"value": "master"
},
{
"id": 1,
"type": "column",
"value": "height"
},
{
"id": 3,
"type": "column",
"value": "weight"
},
{
"id": 2,
"type": "value",
"value": "72"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O"
] |
12,930 | student_loan | bird:train.json:4444 | How many students have been absents for more than 6 months? | SELECT COUNT(name) FROM longest_absense_from_school WHERE month > 6 | [
"How",
"many",
"students",
"have",
"been",
"absents",
"for",
"more",
"than",
"6",
"months",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "longest_absense_from_school"
},
{
"id": 1,
"type": "column",
"value": "month"
},
{
"id": 3,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "value",
"value": "6"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
12,931 | bike_1 | spider:train_spider.json:166 | How many trips did not end in San Francisco? | SELECT count(*) FROM trip AS T1 JOIN station AS T2 ON T1.end_station_id = T2.id WHERE T2.city != "San Francisco" | [
"How",
"many",
"trips",
"did",
"not",
"end",
"in",
"San",
"Francisco",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "end_station_id"
},
{
"id": 3,
"type": "column",
"value": "San Francisco"
},
{
"id": 1,
"type": "table",
"value": "station"
},
{
"id": 0,
"type": "table",
"value": "trip"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
12,932 | company_office | spider:train_spider.json:4552 | Compute the average profits companies make. | SELECT avg(Profits_billion) FROM Companies | [
"Compute",
"the",
"average",
"profits",
"companies",
"make",
"."
] | [
{
"id": 1,
"type": "column",
"value": "profits_billion"
},
{
"id": 0,
"type": "table",
"value": "companies"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"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",
"B-TABLE",
"O",
"O"
] |
12,933 | language_corpus | bird:train.json:5722 | Indicate the page id of Wikipedia about Catalan language of all the pages that have a numeric value in their title. | SELECT pid, title FROM pages WHERE title LIKE '%0%' OR '%1%' OR '%2%' OR '%3%' OR '%4%' OR '%5%' OR '%6%' OR '%7%' OR '%8%' OR '%9%' | [
"Indicate",
"the",
"page",
"i",
"d",
"of",
"Wikipedia",
"about",
"Catalan",
"language",
"of",
"all",
"the",
"pages",
"that",
"have",
"a",
"numeric",
"value",
"in",
"their",
"title",
"."
] | [
{
"id": 0,
"type": "table",
"value": "pages"
},
{
"id": 2,
"type": "column",
"value": "title"
},
{
"id": 1,
"type": "column",
"value": "pid"
},
{
"id": 3,
"type": "value",
"value": "%1%"
},
{
"id": 4,
"type": "value",
"value": "%2%"
},
... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": [
21
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
12,934 | flight_4 | spider:train_spider.json:6838 | What is the name and city of the airport from most of the routes start? | SELECT T1.name , T1.city , T2.src_apid FROM airports AS T1 JOIN routes AS T2 ON T1.apid = T2.src_apid GROUP BY T2.src_apid ORDER BY count(*) DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
"and",
"city",
"of",
"the",
"airport",
"from",
"most",
"of",
"the",
"routes",
"start",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "src_apid"
},
{
"id": 3,
"type": "table",
"value": "airports"
},
{
"id": 4,
"type": "table",
"value": "routes"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "c... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
12,935 | thrombosis_prediction | bird:dev.json:1267 | Among the patients with normal anti-SM, how many of them does not have thrombosis? | SELECT COUNT(T1.ID) FROM Examination AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.SM IN ('negative','0') AND T1.Thrombosis = 0 | [
"Among",
"the",
"patients",
"with",
"normal",
"anti",
"-",
"SM",
",",
"how",
"many",
"of",
"them",
"does",
"not",
"have",
"thrombosis",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "examination"
},
{
"id": 1,
"type": "table",
"value": "laboratory"
},
{
"id": 6,
"type": "column",
"value": "thrombosis"
},
{
"id": 4,
"type": "value",
"value": "negative"
},
{
"id": 2,
"type": "column",
... | [
{
"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-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
12,936 | olympics | bird:train.json:4967 | How many persons in total have participated in 12 meter Mixed Sailing competitions? | SELECT COUNT(T1.competitor_id) FROM competitor_event AS T1 INNER JOIN event AS T2 ON T1.event_id = T2.id INNER JOIN sport AS T3 ON T2.sport_id = T3.id WHERE T2.event_name = 'Sailing Mixed 12 metres' | [
"How",
"many",
"persons",
"in",
"total",
"have",
"participated",
"in",
"12",
"meter",
"Mixed",
"Sailing",
"competitions",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Sailing Mixed 12 metres"
},
{
"id": 4,
"type": "table",
"value": "competitor_event"
},
{
"id": 3,
"type": "column",
"value": "competitor_id"
},
{
"id": 1,
"type": "column",
"value": "event_name"
},
{
"id": 6,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7,
8,
9
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-COLUMN",
"O"
] |
12,937 | formula_1 | bird:dev.json:982 | How many American drivers have puncture status. | SELECT COUNT(T1.driverId) FROM drivers AS T1 INNER JOIN results AS T2 on T1.driverId = T2.driverId INNER JOIN status AS T3 on T2.statusId = T3.statusId WHERE T3.status = 'Puncture' AND T1.nationality = 'American' | [
"How",
"many",
"American",
"drivers",
"have",
"puncture",
"status",
"."
] | [
{
"id": 7,
"type": "column",
"value": "nationality"
},
{
"id": 1,
"type": "column",
"value": "driverid"
},
{
"id": 4,
"type": "column",
"value": "statusid"
},
{
"id": 6,
"type": "value",
"value": "Puncture"
},
{
"id": 8,
"type": "value",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6
... | [
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
12,938 | public_review_platform | bird:train.json:4009 | Among all closed businesses in Avondale, AZ what percent have obtained a 'wonderful experience' rating of the business. | SELECT CAST(SUM(CASE WHEN stars > 3 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(stars) FROM Business WHERE city = 'Avondale' AND active = 'false' | [
"Among",
"all",
"closed",
"businesses",
"in",
"Avondale",
",",
"AZ",
"what",
"percent",
"have",
"obtained",
"a",
"'",
"wonderful",
"experience",
"'",
"rating",
"of",
"the",
"business",
"."
] | [
{
"id": 0,
"type": "table",
"value": "business"
},
{
"id": 2,
"type": "value",
"value": "Avondale"
},
{
"id": 3,
"type": "column",
"value": "active"
},
{
"id": 4,
"type": "value",
"value": "false"
},
{
"id": 6,
"type": "column",
"value": "s... | [
{
"entity_id": 0,
"token_idxs": [
20
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
12,939 | company_office | spider:train_spider.json:4553 | What are the maximum and minimum sales of the companies whose industries are not "Banking". | SELECT max(Sales_billion) , min(Sales_billion) FROM Companies WHERE Industry != "Banking" | [
"What",
"are",
"the",
"maximum",
"and",
"minimum",
"sales",
"of",
"the",
"companies",
"whose",
"industries",
"are",
"not",
"\"",
"Banking",
"\"",
"."
] | [
{
"id": 3,
"type": "column",
"value": "sales_billion"
},
{
"id": 0,
"type": "table",
"value": "companies"
},
{
"id": 1,
"type": "column",
"value": "industry"
},
{
"id": 2,
"type": "column",
"value": "Banking"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
12,940 | authors | bird:train.json:3583 | What percentage of journals whose short name begins with ANN were published in the paper database in 1989? | SELECT CAST((SUM(CASE WHEN T1.ShortName LIKE 'ANN%' THEN 1 ELSE 0 END)) AS REAL) * 100 / COUNT(T1.ShortName) FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T2.Year = 1989 | [
"What",
"percentage",
"of",
"journals",
"whose",
"short",
"name",
"begins",
"with",
"ANN",
"were",
"published",
"in",
"the",
"paper",
"database",
"in",
"1989",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "journalid"
},
{
"id": 7,
"type": "column",
"value": "shortname"
},
{
"id": 0,
"type": "table",
"value": "journal"
},
{
"id": 1,
"type": "table",
"value": "paper"
},
{
"id": 2,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
17
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
12,941 | video_game | bird:test.json:1940 | Return the names and ids of all platforms with the download rank of 1. | SELECT Platform_name , Platform_ID FROM platform WHERE Download_rank = 1 | [
"Return",
"the",
"names",
"and",
"ids",
"of",
"all",
"platforms",
"with",
"the",
"download",
"rank",
"of",
"1",
"."
] | [
{
"id": 1,
"type": "column",
"value": "platform_name"
},
{
"id": 3,
"type": "column",
"value": "download_rank"
},
{
"id": 2,
"type": "column",
"value": "platform_id"
},
{
"id": 0,
"type": "table",
"value": "platform"
},
{
"id": 4,
"type": "valu... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_i... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O"
] |
12,942 | college_2 | spider:train_spider.json:1462 | Find the building, room number, semester and year of all courses offered by Psychology department sorted by course titles. | SELECT T2.building , T2.room_number , T2.semester , T2.year FROM course AS T1 JOIN SECTION AS T2 ON T1.course_id = T2.course_id WHERE T1.dept_name = 'Psychology' ORDER BY T1.title | [
"Find",
"the",
"building",
",",
"room",
"number",
",",
"semester",
"and",
"year",
"of",
"all",
"courses",
"offered",
"by",
"Psychology",
"department",
"sorted",
"by",
"course",
"titles",
"."
] | [
{
"id": 1,
"type": "column",
"value": "room_number"
},
{
"id": 7,
"type": "value",
"value": "Psychology"
},
{
"id": 6,
"type": "column",
"value": "dept_name"
},
{
"id": 9,
"type": "column",
"value": "course_id"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
19
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
12,943 | mondial_geo | bird:train.json:8277 | What is the quantity of the mountains does Japan have? | SELECT COUNT(DISTINCT T2.Mountain) FROM country AS T1 INNER JOIN geo_mountain AS T2 ON T1.Code = T2.Country WHERE T1.Name = 'Japan' | [
"What",
"is",
"the",
"quantity",
"of",
"the",
"mountains",
"does",
"Japan",
"have",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "geo_mountain"
},
{
"id": 4,
"type": "column",
"value": "mountain"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 6,
"type": "column",
"value": "country"
},
{
"id": 3,
"type": "value",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
12,944 | authors | bird:train.json:3667 | Provide the name of the author who is affiliated with the organization named 'ABB Electrical Machines'. | SELECT Name FROM Author WHERE Affiliation = 'ABB Electrical Machines' | [
"Provide",
"the",
"name",
"of",
"the",
"author",
"who",
"is",
"affiliated",
"with",
"the",
"organization",
"named",
"'",
"ABB",
"Electrical",
"Machines",
"'",
"."
] | [
{
"id": 3,
"type": "value",
"value": "ABB Electrical Machines"
},
{
"id": 2,
"type": "column",
"value": "affiliation"
},
{
"id": 0,
"type": "table",
"value": "author"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
14,
15,
16
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
12,945 | insurance_and_eClaims | spider:train_spider.json:1512 | Find the type code of the most frequently used policy. | SELECT policy_type_code FROM policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"type",
"code",
"of",
"the",
"most",
"frequently",
"used",
"policy",
"."
] | [
{
"id": 1,
"type": "column",
"value": "policy_type_code"
},
{
"id": 0,
"type": "table",
"value": "policies"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"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",
"O",
"O",
"B-TABLE",
"O"
] |
12,946 | small_bank_1 | spider:train_spider.json:1820 | Find the name of accounts whose checking balance is higher than corresponding saving balance. | SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid = T2.custid JOIN savings AS T3 ON T1.custid = T3.custid WHERE T2.balance > T3.balance | [
"Find",
"the",
"name",
"of",
"accounts",
"whose",
"checking",
"balance",
"is",
"higher",
"than",
"corresponding",
"saving",
"balance",
"."
] | [
{
"id": 3,
"type": "table",
"value": "accounts"
},
{
"id": 4,
"type": "table",
"value": "checking"
},
{
"id": 1,
"type": "table",
"value": "savings"
},
{
"id": 2,
"type": "column",
"value": "balance"
},
{
"id": 5,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
12,947 | superhero | bird:dev.json:783 | Provide the eye colours of the heroes whose skin colours are gold. | SELECT T2.colour FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.eye_colour_id = T2.id INNER JOIN colour AS T3 ON T1.skin_colour_id = T3.id WHERE T3.colour = 'Gold' | [
"Provide",
"the",
"eye",
"colours",
"of",
"the",
"heroes",
"whose",
"skin",
"colours",
"are",
"gold",
"."
] | [
{
"id": 4,
"type": "column",
"value": "skin_colour_id"
},
{
"id": 6,
"type": "column",
"value": "eye_colour_id"
},
{
"id": 3,
"type": "table",
"value": "superhero"
},
{
"id": 0,
"type": "column",
"value": "colour"
},
{
"id": 1,
"type": "table",... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
12,948 | video_games | bird:train.json:3423 | What are the names of the games published by American Softworks? | SELECT T3.game_name FROM publisher AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.publisher_id INNER JOIN game AS T3 ON T2.game_id = T3.id WHERE T1.publisher_name = 'American Softworks' | [
"What",
"are",
"the",
"names",
"of",
"the",
"games",
"published",
"by",
"American",
"Softworks",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "American Softworks"
},
{
"id": 2,
"type": "column",
"value": "publisher_name"
},
{
"id": 5,
"type": "table",
"value": "game_publisher"
},
{
"id": 8,
"type": "column",
"value": "publisher_id"
},
{
"id": 0,
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.