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13,796
|
book_publishing_company
|
bird:train.json:204
|
Please list the stores that ordered the book "Life Without Fear".
|
SELECT T2.stor_name FROM sales AS T1 INNER JOIN stores AS T2 ON T1.stor_id = T2.stor_id INNER JOIN titles AS T3 ON T1.title_id = T3.title_id WHERE T3.title = 'Life Without Fear'
|
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"."
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{
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"value": "stor_id"
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{
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"value": "titles"
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{
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{
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"type": "column",
"value": "title"
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{
"id": 4,
"type": "table",
"value": "sales"
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"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
13,797
|
world
|
bird:train.json:7896
|
What is the average surface area of all countries?
|
SELECT AVG(SurfaceArea) FROM Country
|
[
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"area",
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"?"
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{
"id": 1,
"type": "column",
"value": "surfacearea"
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{
"id": 0,
"type": "table",
"value": "country"
}
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[
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
13,798
|
card_games
|
bird:dev.json:405
|
How many Brazilian Portuguese translated sets are inside the Commander block?
|
SELECT COUNT(T1.id) FROM sets AS T1 INNER JOIN set_translations AS T2 ON T1.code = T2.setCode WHERE T2.language = 'Portuguese (Brazil)' AND T1.block = 'Commander'
|
[
"How",
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"Brazilian",
"Portuguese",
"translated",
"sets",
"are",
"inside",
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"Commander",
"block",
"?"
] |
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{
"id": 6,
"type": "value",
"value": "Portuguese (Brazil)"
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{
"id": 1,
"type": "table",
"value": "set_translations"
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{
"id": 8,
"type": "value",
"value": "Commander"
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{
"id": 5,
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"value": "language"
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{
"id": 4,
"type": "column",
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{
"id": 7,
"type": "column",
"value": "block"
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{
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},
{
"id": 3,
"type": "column",
"value": "code"
},
{
"id": 2,
"type": "column",
"value": "id"
}
] |
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[
"O",
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"O",
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"B-TABLE",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
13,799
|
soccer_2016
|
bird:train.json:2014
|
What is the role of SC Ganguly?
|
SELECT T3.Role_Desc FROM Player AS T1 INNER JOIN Player_Match AS T2 ON T1.Player_Id = T2.Player_Id INNER JOIN Rolee AS T3 ON T2.Role_Id = T3.Role_Id WHERE T1.Player_Name = 'SC Ganguly' GROUP BY T3.Role_Desc
|
[
"What",
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"SC",
"Ganguly",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "player_match"
},
{
"id": 2,
"type": "column",
"value": "player_name"
},
{
"id": 3,
"type": "value",
"value": "SC Ganguly"
},
{
"id": 0,
"type": "column",
"value": "role_desc"
},
{
"id": 7,
"type": "column",
"value": "player_id"
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{
"id": 6,
"type": "column",
"value": "role_id"
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{
"id": 4,
"type": "table",
"value": "player"
},
{
"id": 1,
"type": "table",
"value": "rolee"
}
] |
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{
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}
] |
[
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
13,800
|
e_government
|
spider:train_spider.json:6327
|
Count the number of services.
|
SELECT count(*) FROM services
|
[
"Count",
"the",
"number",
"of",
"services",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "services"
}
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{
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},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,801
|
game_1
|
spider:train_spider.json:6053
|
Find the last name and gender of the students who are playing both Call of Destiny and Works of Widenius games.
|
SELECT lname , sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Call of Destiny" INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID = T2.GameID WHERE T2.Gname = "Works of Widenius")
|
[
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"Call",
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"Destiny",
"and",
"Works",
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"Widenius",
"games",
"."
] |
[
{
"id": 8,
"type": "column",
"value": "Works of Widenius"
},
{
"id": 7,
"type": "column",
"value": "Call of Destiny"
},
{
"id": 4,
"type": "table",
"value": "plays_games"
},
{
"id": 5,
"type": "table",
"value": "video_games"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 9,
"type": "column",
"value": "gameid"
},
{
"id": 1,
"type": "column",
"value": "lname"
},
{
"id": 3,
"type": "column",
"value": "stuid"
},
{
"id": 6,
"type": "column",
"value": "gname"
},
{
"id": 2,
"type": "column",
"value": "sex"
}
] |
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[
"O",
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"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"B-COLUMN",
"O"
] |
13,802
|
mondial_geo
|
bird:train.json:8461
|
Name all the organisations that were established from 1970 to 1980.
|
SELECT Name FROM organization WHERE STRFTIME('%Y', Established) BETWEEN '1970' AND '1980'
|
[
"Name",
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"the",
"organisations",
"that",
"were",
"established",
"from",
"1970",
"to",
"1980",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "organization"
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{
"id": 5,
"type": "column",
"value": "established"
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{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "value",
"value": "1970"
},
{
"id": 3,
"type": "value",
"value": "1980"
},
{
"id": 4,
"type": "value",
"value": "%Y"
}
] |
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[
"B-COLUMN",
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"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
13,803
|
soccer_2016
|
bird:train.json:1974
|
Among the" Mumbai Indians" team that played in 2009, how many percent of the matches did they win?
|
SELECT CAST(SUM(CASE WHEN T1.Match_Winner = T2.Team_Id THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.Match_Id) FROM `Match` AS T1 INNER JOIN Team AS T2 ON T1.Team_1 = T2.Team_Id OR T1.Team_2 = T2.Team_Id WHERE T2.Team_Name = 'Mumbai Indians' AND T1.Match_Date LIKE '2009%'
|
[
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",",
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"of",
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"matches",
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"they",
"win",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "Mumbai Indians"
},
{
"id": 13,
"type": "column",
"value": "match_winner"
},
{
"id": 4,
"type": "column",
"value": "match_date"
},
{
"id": 2,
"type": "column",
"value": "team_name"
},
{
"id": 7,
"type": "column",
"value": "match_id"
},
{
"id": 9,
"type": "column",
"value": "team_id"
},
{
"id": 8,
"type": "column",
"value": "team_1"
},
{
"id": 10,
"type": "column",
"value": "team_2"
},
{
"id": 0,
"type": "table",
"value": "Match"
},
{
"id": 5,
"type": "value",
"value": "2009%"
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{
"id": 1,
"type": "table",
"value": "team"
},
{
"id": 6,
"type": "value",
"value": "100"
},
{
"id": 11,
"type": "value",
"value": "0"
},
{
"id": 12,
"type": "value",
"value": "1"
}
] |
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"O",
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"O",
"O",
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"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
13,804
|
movie_platform
|
bird:train.json:162
|
Between 1/1/2017 to 12/31/2017, how many users who were eligible for trial when they rated the movie "Patti Smith: Dream of Life"and what is the image URL to the movie on Mubi?
|
SELECT COUNT(T1.user_id), T2.movie_image_url FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE datetime(T1.rating_timestamp_utc) BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 00:00:00'
|
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"what",
"is",
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"image",
"URL",
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"the",
"movie",
"on",
"Mubi",
"?"
] |
[
{
"id": 7,
"type": "column",
"value": "rating_timestamp_utc"
},
{
"id": 3,
"type": "value",
"value": "2017-01-01 00:00:00"
},
{
"id": 4,
"type": "value",
"value": "2017-12-31 00:00:00"
},
{
"id": 0,
"type": "column",
"value": "movie_image_url"
},
{
"id": 6,
"type": "column",
"value": "movie_id"
},
{
"id": 1,
"type": "table",
"value": "ratings"
},
{
"id": 5,
"type": "column",
"value": "user_id"
},
{
"id": 2,
"type": "table",
"value": "movies"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
28,
29
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},
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{
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32
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},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
13,805
|
olympics
|
bird:train.json:4998
|
What is the average age of the athletes from the United States of America who competed in the 2016 Summer Olympics?
|
SELECT AVG(T2.age) FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person_region AS T3 ON T2.person_id = T3.person_id INNER JOIN noc_region AS T4 ON T3.region_id = T4.id WHERE T1.games_name = '2016 Summer' AND T4.region_name = 'USA'
|
[
"What",
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"age",
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"athletes",
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"United",
"States",
"of",
"America",
"who",
"competed",
"in",
"the",
"2016",
"Summer",
"Olympics",
"?"
] |
[
{
"id": 10,
"type": "table",
"value": "games_competitor"
},
{
"id": 2,
"type": "table",
"value": "person_region"
},
{
"id": 6,
"type": "value",
"value": "2016 Summer"
},
{
"id": 7,
"type": "column",
"value": "region_name"
},
{
"id": 0,
"type": "table",
"value": "noc_region"
},
{
"id": 5,
"type": "column",
"value": "games_name"
},
{
"id": 3,
"type": "column",
"value": "region_id"
},
{
"id": 11,
"type": "column",
"value": "person_id"
},
{
"id": 12,
"type": "column",
"value": "games_id"
},
{
"id": 9,
"type": "table",
"value": "games"
},
{
"id": 1,
"type": "column",
"value": "age"
},
{
"id": 8,
"type": "value",
"value": "USA"
},
{
"id": 4,
"type": "column",
"value": "id"
}
] |
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{
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"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
13,806
|
boat_1
|
bird:test.json:851
|
What is the name of sailors whose names contain letter e?
|
SELECT name FROM Sailors WHERE name LIKE '%e%'
|
[
"What",
"is",
"the",
"name",
"of",
"sailors",
"whose",
"names",
"contain",
"letter",
"e",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "sailors"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "value",
"value": "%e%"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
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"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
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},
{
"entity_id": 6,
"token_idxs": []
},
{
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{
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},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,807
|
financial
|
bird:dev.json:129
|
Which are the top ten withdrawals (non-credit card) by district names for the month of January 1996?
|
SELECT DISTINCT T1.A2 FROM district AS T1 INNER JOIN account AS T2 ON T1.district_id = T2.district_id INNER JOIN trans AS T3 ON T2.account_id = T3.account_id WHERE T3.type = 'VYDAJ' AND T3.date LIKE '1996-01%' ORDER BY A2 ASC LIMIT 10
|
[
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"(",
"non",
"-",
"credit",
"card",
")",
"by",
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"names",
"for",
"the",
"month",
"of",
"January",
"1996",
"?"
] |
[
{
"id": 9,
"type": "column",
"value": "district_id"
},
{
"id": 4,
"type": "column",
"value": "account_id"
},
{
"id": 2,
"type": "table",
"value": "district"
},
{
"id": 8,
"type": "value",
"value": "1996-01%"
},
{
"id": 3,
"type": "table",
"value": "account"
},
{
"id": 1,
"type": "table",
"value": "trans"
},
{
"id": 6,
"type": "value",
"value": "VYDAJ"
},
{
"id": 5,
"type": "column",
"value": "type"
},
{
"id": 7,
"type": "column",
"value": "date"
},
{
"id": 0,
"type": "column",
"value": "a2"
}
] |
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},
{
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},
{
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13
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},
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"entity_id": 16,
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},
{
"entity_id": 18,
"token_idxs": []
},
{
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"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,808
|
disney
|
bird:train.json:4713
|
List all the main characters of the movie that are comedy genre.
|
SELECT T2.hero FROM movies_total_gross AS T1 INNER JOIN characters AS T2 ON T1.movie_title = T2.movie_title WHERE T1.genre = 'Comedy'
|
[
"List",
"all",
"the",
"main",
"characters",
"of",
"the",
"movie",
"that",
"are",
"comedy",
"genre",
"."
] |
[
{
"id": 1,
"type": "table",
"value": "movies_total_gross"
},
{
"id": 5,
"type": "column",
"value": "movie_title"
},
{
"id": 2,
"type": "table",
"value": "characters"
},
{
"id": 4,
"type": "value",
"value": "Comedy"
},
{
"id": 3,
"type": "column",
"value": "genre"
},
{
"id": 0,
"type": "column",
"value": "hero"
}
] |
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{
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"token_idxs": []
},
{
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{
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10
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},
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8
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},
{
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"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
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{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
13,810
|
synthea
|
bird:train.json:1431
|
Among the immunizations in 2017, calculate the percentage of patients who received the Influenza seasonal injectable preservative free. Among them, how many patients are English?
|
SELECT CAST(SUM(CASE WHEN T2.DESCRIPTION = 'Influenza seasonal injectable preservative free' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.patient), SUM(CASE WHEN T1.ethnicity = 'english' THEN 1 ELSE 0 END) FROM patients AS T1 INNER JOIN immunizations AS T2 ON T1.patient = T2.PATIENT WHERE strftime('%Y', T2.DATE) = '2017'
|
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".",
"Among",
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",",
"how",
"many",
"patients",
"are",
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"?"
] |
[
{
"id": 12,
"type": "value",
"value": "Influenza seasonal injectable preservative free"
},
{
"id": 1,
"type": "table",
"value": "immunizations"
},
{
"id": 11,
"type": "column",
"value": "description"
},
{
"id": 9,
"type": "column",
"value": "ethnicity"
},
{
"id": 0,
"type": "table",
"value": "patients"
},
{
"id": 3,
"type": "column",
"value": "patient"
},
{
"id": 10,
"type": "value",
"value": "english"
},
{
"id": 2,
"type": "value",
"value": "2017"
},
{
"id": 5,
"type": "column",
"value": "date"
},
{
"id": 6,
"type": "value",
"value": "100"
},
{
"id": 4,
"type": "value",
"value": "%Y"
},
{
"id": 7,
"type": "value",
"value": "0"
},
{
"id": 8,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
25
]
},
{
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"token_idxs": [
2
]
},
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4
]
},
{
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"token_idxs": [
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{
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{
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{
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
13,811
|
club_1
|
spider:train_spider.json:4299
|
Count the number of different positions in the club "Bootup Baltimore".
|
SELECT count(DISTINCT t2.position) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid = t2.clubid WHERE t1.clubname = "Bootup Baltimore"
|
[
"Count",
"the",
"number",
"of",
"different",
"positions",
"in",
"the",
"club",
"\"",
"Bootup",
"Baltimore",
"\"",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "Bootup Baltimore"
},
{
"id": 1,
"type": "table",
"value": "member_of_club"
},
{
"id": 2,
"type": "column",
"value": "clubname"
},
{
"id": 4,
"type": "column",
"value": "position"
},
{
"id": 5,
"type": "column",
"value": "clubid"
},
{
"id": 0,
"type": "table",
"value": "club"
}
] |
[
{
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"token_idxs": [
8
]
},
{
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},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
5
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},
{
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},
{
"entity_id": 6,
"token_idxs": []
},
{
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{
"entity_id": 8,
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{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
13,812
|
wrestler
|
spider:train_spider.json:1873
|
Which reign is the most common among wrestlers?
|
SELECT Reign FROM wrestler GROUP BY Reign ORDER BY COUNT(*) DESC LIMIT 1
|
[
"Which",
"reign",
"is",
"the",
"most",
"common",
"among",
"wrestlers",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "wrestler"
},
{
"id": 1,
"type": "column",
"value": "reign"
}
] |
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{
"entity_id": 0,
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7
]
},
{
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1
]
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{
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},
{
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},
{
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},
{
"entity_id": 14,
"token_idxs": []
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"entity_id": 15,
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},
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},
{
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,813
|
loan_1
|
spider:train_spider.json:3013
|
What is the average number of customers across banks in the state of Utah?
|
SELECT avg(no_of_customers) FROM bank WHERE state = 'Utah'
|
[
"What",
"is",
"the",
"average",
"number",
"of",
"customers",
"across",
"banks",
"in",
"the",
"state",
"of",
"Utah",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "no_of_customers"
},
{
"id": 1,
"type": "column",
"value": "state"
},
{
"id": 0,
"type": "table",
"value": "bank"
},
{
"id": 2,
"type": "value",
"value": "Utah"
}
] |
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{
"entity_id": 0,
"token_idxs": [
8
]
},
{
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11
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},
{
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13
]
},
{
"entity_id": 3,
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5,
6
]
},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"entity_id": 9,
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},
{
"entity_id": 10,
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},
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},
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},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
13,814
|
art_1
|
bird:test.json:1301
|
What is the id of every painting created before the oldest painting in gallery 240?
|
SELECT paintingID FROM paintings WHERE YEAR < (SELECT min(YEAR) FROM paintings WHERE LOCATION = 'Gallery 240')
|
[
"What",
"is",
"the",
"i",
"d",
"of",
"every",
"painting",
"created",
"before",
"the",
"oldest",
"painting",
"in",
"gallery",
"240",
"?"
] |
[
{
"id": 4,
"type": "value",
"value": "Gallery 240"
},
{
"id": 1,
"type": "column",
"value": "paintingid"
},
{
"id": 0,
"type": "table",
"value": "paintings"
},
{
"id": 3,
"type": "column",
"value": "location"
},
{
"id": 2,
"type": "column",
"value": "year"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
14,
15
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},
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"entity_id": 5,
"token_idxs": []
},
{
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"token_idxs": []
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},
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"entity_id": 8,
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"entity_id": 9,
"token_idxs": []
},
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"entity_id": 10,
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},
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"token_idxs": []
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"token_idxs": []
},
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"entity_id": 13,
"token_idxs": []
},
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"entity_id": 14,
"token_idxs": []
},
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"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
13,815
|
hockey
|
bird:train.json:7710
|
Which is the catching hand for the goaltender who had the most shutouts in 1996?
|
SELECT T1.shootCatch FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID WHERE T2.year = 1996 ORDER BY T2.SHO DESC LIMIT 1
|
[
"Which",
"is",
"the",
"catching",
"hand",
"for",
"the",
"goaltender",
"who",
"had",
"the",
"most",
"shutouts",
"in",
"1996",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "shootcatch"
},
{
"id": 6,
"type": "column",
"value": "playerid"
},
{
"id": 2,
"type": "table",
"value": "goalies"
},
{
"id": 1,
"type": "table",
"value": "master"
},
{
"id": 3,
"type": "column",
"value": "year"
},
{
"id": 4,
"type": "value",
"value": "1996"
},
{
"id": 5,
"type": "column",
"value": "sho"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
"entity_id": 5,
"token_idxs": [
8
]
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
13,816
|
hockey
|
bird:train.json:7736
|
What is the power play percentage of the team with the least number of penalty kill chances and to which team were they playing against? Indicate whether the team lost or victorious.
|
SELECT SUM(T1.A), T2.firstName, T2.lastName FROM Scoring AS T1 INNER JOIN Master AS T2 ON T1.playerID = T2.playerID WHERE T1.lgID = 'NHL' GROUP BY T2.firstName, T2.lastName ORDER BY SUM(T1.A) DESC LIMIT 1
|
[
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"the",
"power",
"play",
"percentage",
"of",
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"team",
"with",
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"of",
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"kill",
"chances",
"and",
"to",
"which",
"team",
"were",
"they",
"playing",
"against",
"?",
"Indicate",
"whether",
"the",
"team",
"lost",
"or",
"victorious",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 7,
"type": "column",
"value": "playerid"
},
{
"id": 2,
"type": "table",
"value": "scoring"
},
{
"id": 3,
"type": "table",
"value": "master"
},
{
"id": 4,
"type": "column",
"value": "lgid"
},
{
"id": 5,
"type": "value",
"value": "NHL"
},
{
"id": 6,
"type": "column",
"value": "a"
}
] |
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{
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"token_idxs": []
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{
"entity_id": 1,
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11,
12
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{
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{
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{
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{
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{
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{
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4
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{
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{
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{
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{
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{
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"token_idxs": []
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{
"entity_id": 14,
"token_idxs": []
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{
"entity_id": 15,
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},
{
"entity_id": 16,
"token_idxs": []
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{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,817
|
simpson_episodes
|
bird:train.json:4283
|
What are the keywords of the least popular episode?
|
SELECT T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id ORDER BY T1.votes LIMIT 1;
|
[
"What",
"are",
"the",
"keywords",
"of",
"the",
"least",
"popular",
"episode",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "episode_id"
},
{
"id": 0,
"type": "column",
"value": "keyword"
},
{
"id": 1,
"type": "table",
"value": "episode"
},
{
"id": 2,
"type": "table",
"value": "keyword"
},
{
"id": 3,
"type": "column",
"value": "votes"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
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},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
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"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,818
|
image_and_language
|
bird:train.json:7531
|
Name the object element that is described as being scattered on image no. 10.
|
SELECT T2.OBJ_CLASS FROM IMG_OBJ AS T1 INNER JOIN OBJ_CLASSES AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID INNER JOIN IMG_OBJ_ATT AS T3 ON T1.IMG_ID = T3.IMG_ID INNER JOIN ATT_CLASSES AS T4 ON T3.ATT_CLASS_ID = T4.ATT_CLASS_ID WHERE T4.ATT_CLASS = 'scattered' AND T1.IMG_ID = 10 GROUP BY T2.OBJ_CLASS
|
[
"Name",
"the",
"object",
"element",
"that",
"is",
"described",
"as",
"being",
"scattered",
"on",
"image",
"no",
".",
"10",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "att_class_id"
},
{
"id": 10,
"type": "column",
"value": "obj_class_id"
},
{
"id": 1,
"type": "table",
"value": "att_classes"
},
{
"id": 2,
"type": "table",
"value": "img_obj_att"
},
{
"id": 9,
"type": "table",
"value": "obj_classes"
},
{
"id": 0,
"type": "column",
"value": "obj_class"
},
{
"id": 4,
"type": "column",
"value": "att_class"
},
{
"id": 5,
"type": "value",
"value": "scattered"
},
{
"id": 8,
"type": "table",
"value": "img_obj"
},
{
"id": 6,
"type": "column",
"value": "img_id"
},
{
"id": 7,
"type": "value",
"value": "10"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
9
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},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
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14
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{
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,819
|
college_1
|
spider:train_spider.json:3177
|
What is the first and last name of the professor in biology department?
|
SELECT T3.EMP_FNAME , T3.EMP_LNAME FROM professor AS T1 JOIN department AS T2 ON T1.dept_code = T2.dept_code JOIN employee AS T3 ON T1.EMP_NUM = T3.EMP_NUM WHERE DEPT_NAME = "Biology"
|
[
"What",
"is",
"the",
"first",
"and",
"last",
"name",
"of",
"the",
"professor",
"in",
"biology",
"department",
"?"
] |
[
{
"id": 6,
"type": "table",
"value": "department"
},
{
"id": 0,
"type": "column",
"value": "emp_fname"
},
{
"id": 1,
"type": "column",
"value": "emp_lname"
},
{
"id": 3,
"type": "column",
"value": "dept_name"
},
{
"id": 5,
"type": "table",
"value": "professor"
},
{
"id": 8,
"type": "column",
"value": "dept_code"
},
{
"id": 2,
"type": "table",
"value": "employee"
},
{
"id": 4,
"type": "column",
"value": "Biology"
},
{
"id": 7,
"type": "column",
"value": "emp_num"
}
] |
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"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
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},
{
"entity_id": 4,
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11
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},
{
"entity_id": 5,
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9
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},
{
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},
{
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},
{
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{
"entity_id": 9,
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},
{
"entity_id": 10,
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},
{
"entity_id": 11,
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},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
13,820
|
customers_and_addresses
|
spider:train_spider.json:6107
|
What is the name of the customer that made the order with the largest quantity?
|
SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id = t2.customer_id JOIN order_items AS t3 ON t2.order_id = t3.order_id WHERE t3.order_quantity = ( SELECT max(order_quantity) FROM order_items)
|
[
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"name",
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"the",
"order",
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"largest",
"quantity",
"?"
] |
[
{
"id": 4,
"type": "table",
"value": "customer_orders"
},
{
"id": 2,
"type": "column",
"value": "order_quantity"
},
{
"id": 0,
"type": "column",
"value": "customer_name"
},
{
"id": 1,
"type": "table",
"value": "order_items"
},
{
"id": 6,
"type": "column",
"value": "customer_id"
},
{
"id": 3,
"type": "table",
"value": "customers"
},
{
"id": 5,
"type": "column",
"value": "order_id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
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14
]
},
{
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6
]
},
{
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},
{
"entity_id": 5,
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10
]
},
{
"entity_id": 6,
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},
{
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},
{
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},
{
"entity_id": 9,
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{
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},
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},
{
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},
{
"entity_id": 13,
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},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
13,821
|
movie_1
|
spider:train_spider.json:2531
|
What are the names of all movies that received 3 or 4 stars?
|
SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID = T2.mID WHERE T1.stars = 3 INTERSECT SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID = T2.mID WHERE T1.stars = 4
|
[
"What",
"are",
"the",
"names",
"of",
"all",
"movies",
"that",
"received",
"3",
"or",
"4",
"stars",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "rating"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 2,
"type": "table",
"value": "movie"
},
{
"id": 3,
"type": "column",
"value": "stars"
},
{
"id": 6,
"type": "column",
"value": "mid"
},
{
"id": 4,
"type": "value",
"value": "3"
},
{
"id": 5,
"type": "value",
"value": "4"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
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},
{
"entity_id": 2,
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6
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
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9
]
},
{
"entity_id": 5,
"token_idxs": [
11
]
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
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},
{
"entity_id": 8,
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},
{
"entity_id": 9,
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},
{
"entity_id": 10,
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},
{
"entity_id": 11,
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},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
13,823
|
student_club
|
bird:dev.json:1423
|
How many income are received with an amount of 50?
|
SELECT COUNT(income_id) FROM income WHERE amount = 50
|
[
"How",
"many",
"income",
"are",
"received",
"with",
"an",
"amount",
"of",
"50",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "income_id"
},
{
"id": 0,
"type": "table",
"value": "income"
},
{
"id": 1,
"type": "column",
"value": "amount"
},
{
"id": 2,
"type": "value",
"value": "50"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
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9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
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},
{
"entity_id": 8,
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},
{
"entity_id": 9,
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},
{
"entity_id": 10,
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},
{
"entity_id": 11,
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},
{
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"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
13,824
|
video_games
|
bird:train.json:3384
|
How many games were published by Acclaim Entertainment?
|
SELECT COUNT(DISTINCT T1.game_id) FROM game_publisher AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T2.publisher_name = 'Acclaim Entertainment'
|
[
"How",
"many",
"games",
"were",
"published",
"by",
"Acclaim",
"Entertainment",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "Acclaim Entertainment"
},
{
"id": 0,
"type": "table",
"value": "game_publisher"
},
{
"id": 2,
"type": "column",
"value": "publisher_name"
},
{
"id": 5,
"type": "column",
"value": "publisher_id"
},
{
"id": 1,
"type": "table",
"value": "publisher"
},
{
"id": 4,
"type": "column",
"value": "game_id"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
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3
]
},
{
"entity_id": 1,
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4
]
},
{
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},
{
"entity_id": 3,
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6,
7
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
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"token_idxs": []
},
{
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},
{
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},
{
"entity_id": 8,
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
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"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
13,825
|
professional_basketball
|
bird:train.json:2839
|
Which player from "AFS" team has the tallest height?
|
SELECT T1.firstName, T1.middleName, T1.lastName FROM players AS T1 INNER JOIN players_teams AS T2 ON T1.playerID = T2.playerID WHERE T2.tmID = 'AFS' ORDER BY T1.height DESC LIMIT 1
|
[
"Which",
"player",
"from",
"\"",
"AFS",
"\"",
"team",
"has",
"the",
"tallest",
"height",
"?"
] |
[
{
"id": 4,
"type": "table",
"value": "players_teams"
},
{
"id": 1,
"type": "column",
"value": "middlename"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 2,
"type": "column",
"value": "lastname"
},
{
"id": 8,
"type": "column",
"value": "playerid"
},
{
"id": 3,
"type": "table",
"value": "players"
},
{
"id": 7,
"type": "column",
"value": "height"
},
{
"id": 5,
"type": "column",
"value": "tmid"
},
{
"id": 6,
"type": "value",
"value": "AFS"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
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},
{
"entity_id": 2,
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5,
6
]
},
{
"entity_id": 3,
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1
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{
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"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
13,826
|
vehicle_rent
|
bird:test.json:437
|
Find the average city and highway fuel rates for cars with different powertrain types.
|
SELECT avg(City_fuel_economy_rate) , avg(Highway_fuel_economy_rate) , Type_of_powertrain FROM vehicles GROUP BY Type_of_powertrain
|
[
"Find",
"the",
"average",
"city",
"and",
"highway",
"fuel",
"rates",
"for",
"cars",
"with",
"different",
"powertrain",
"types",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "highway_fuel_economy_rate"
},
{
"id": 2,
"type": "column",
"value": "city_fuel_economy_rate"
},
{
"id": 1,
"type": "column",
"value": "type_of_powertrain"
},
{
"id": 0,
"type": "table",
"value": "vehicles"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
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},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
13,827
|
film_rank
|
spider:train_spider.json:4116
|
What is the average ticket sales gross in dollars of films?
|
SELECT avg(Gross_in_dollar) FROM film
|
[
"What",
"is",
"the",
"average",
"ticket",
"sales",
"gross",
"in",
"dollars",
"of",
"films",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "gross_in_dollar"
},
{
"id": 0,
"type": "table",
"value": "film"
}
] |
[
{
"entity_id": 0,
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10
]
},
{
"entity_id": 1,
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6,
7,
8
]
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
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},
{
"entity_id": 4,
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},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O"
] |
13,828
|
soccer_2
|
spider:train_spider.json:4979
|
What is the name of every college in alphabetical order that has more than 18000 students enrolled?
|
SELECT cName FROM College WHERE enr > 18000 ORDER BY cName
|
[
"What",
"is",
"the",
"name",
"of",
"every",
"college",
"in",
"alphabetical",
"order",
"that",
"has",
"more",
"than",
"18000",
"students",
"enrolled",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "college"
},
{
"id": 1,
"type": "column",
"value": "cname"
},
{
"id": 3,
"type": "value",
"value": "18000"
},
{
"id": 2,
"type": "column",
"value": "enr"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
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},
{
"entity_id": 3,
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14
]
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
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},
{
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},
{
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},
{
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
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},
{
"entity_id": 11,
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},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
13,829
|
thrombosis_prediction
|
bird:dev.json:1167
|
For the year that concluded on December 31, 1998, how many male patients on average were tested in the lab each month?
|
SELECT CAST(COUNT(T1.ID) AS REAL) / 12 FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE STRFTIME('%Y', T2.Date) = '1998' AND T1.SEX = 'M'
|
[
"For",
"the",
"year",
"that",
"concluded",
"on",
"December",
"31",
",",
"1998",
",",
"how",
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"male",
"patients",
"on",
"average",
"were",
"tested",
"in",
"the",
"lab",
"each",
"month",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "laboratory"
},
{
"id": 0,
"type": "table",
"value": "patient"
},
{
"id": 4,
"type": "value",
"value": "1998"
},
{
"id": 8,
"type": "column",
"value": "date"
},
{
"id": 5,
"type": "column",
"value": "sex"
},
{
"id": 2,
"type": "value",
"value": "12"
},
{
"id": 3,
"type": "column",
"value": "id"
},
{
"id": 7,
"type": "value",
"value": "%Y"
},
{
"id": 6,
"type": "value",
"value": "M"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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9
]
},
{
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{
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{
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{
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{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,830
|
network_2
|
spider:train_spider.json:4471
|
What are the ages of all of Zach's friends who are in the longest relationship?
|
SELECT T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name = T2.friend WHERE T2.name = 'Zach' AND T2.year = (SELECT max(YEAR) FROM PersonFriend WHERE name = 'Zach')
|
[
"What",
"are",
"the",
"ages",
"of",
"all",
"of",
"Zach",
"'s",
"friends",
"who",
"are",
"in",
"the",
"longest",
"relationship",
"?"
] |
[
{
"id": 2,
"type": "table",
"value": "personfriend"
},
{
"id": 1,
"type": "table",
"value": "person"
},
{
"id": 4,
"type": "column",
"value": "friend"
},
{
"id": 3,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "value",
"value": "Zach"
},
{
"id": 6,
"type": "column",
"value": "year"
},
{
"id": 0,
"type": "column",
"value": "age"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"token_idxs": [
7
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,831
|
world_development_indicators
|
bird:train.json:2096
|
How many low-income countries under the lending category of the International Development Associations have a note on the series code SM.POP.TOTL?
|
SELECT COUNT(T1.Countrycode) FROM Country AS T1 INNER JOIN CountryNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T1.LendingCategory = 'IDA' AND T2.Seriescode = 'SM.POP.TOTL' AND IncomeGroup = 'Low income'
|
[
"How",
"many",
"low",
"-",
"income",
"countries",
"under",
"the",
"lending",
"category",
"of",
"the",
"International",
"Development",
"Associations",
"have",
"a",
"note",
"on",
"the",
"series",
"code",
"SM.POP.TOTL",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "lendingcategory"
},
{
"id": 1,
"type": "table",
"value": "countrynotes"
},
{
"id": 2,
"type": "column",
"value": "countrycode"
},
{
"id": 6,
"type": "value",
"value": "SM.POP.TOTL"
},
{
"id": 7,
"type": "column",
"value": "incomegroup"
},
{
"id": 5,
"type": "column",
"value": "seriescode"
},
{
"id": 8,
"type": "value",
"value": "Low income"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 4,
"type": "value",
"value": "IDA"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
20,
21
]
},
{
"entity_id": 6,
"token_idxs": [
22
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
13,832
|
superhero
|
bird:dev.json:767
|
What is the average of superheroes with no skin colour?
|
SELECT CAST(COUNT(*) AS REAL) / SUM(CASE WHEN T2.id = 1 THEN 1 ELSE 0 END) FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.skin_colour_id = T2.id
|
[
"What",
"is",
"the",
"average",
"of",
"superheroes",
"with",
"no",
"skin",
"colour",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "skin_colour_id"
},
{
"id": 0,
"type": "table",
"value": "superhero"
},
{
"id": 1,
"type": "table",
"value": "colour"
},
{
"id": 3,
"type": "column",
"value": "id"
},
{
"id": 4,
"type": "value",
"value": "0"
},
{
"id": 5,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
13,833
|
university
|
bird:train.json:7999
|
How many universities scored 0 in Awards between 2005 to 2015?
|
SELECT COUNT(*) FROM ranking_criteria AS T1 INNER JOIN university_ranking_year AS T2 ON T1.id = T2.ranking_criteria_id WHERE T1.criteria_name = 'Award' AND T2.year BETWEEN 2005 AND 2015 AND T2.score = 0
|
[
"How",
"many",
"universities",
"scored",
"0",
"in",
"Awards",
"between",
"2005",
"to",
"2015",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "university_ranking_year"
},
{
"id": 3,
"type": "column",
"value": "ranking_criteria_id"
},
{
"id": 0,
"type": "table",
"value": "ranking_criteria"
},
{
"id": 4,
"type": "column",
"value": "criteria_name"
},
{
"id": 5,
"type": "value",
"value": "Award"
},
{
"id": 9,
"type": "column",
"value": "score"
},
{
"id": 6,
"type": "column",
"value": "year"
},
{
"id": 7,
"type": "value",
"value": "2005"
},
{
"id": 8,
"type": "value",
"value": "2015"
},
{
"id": 2,
"type": "column",
"value": "id"
},
{
"id": 10,
"type": "value",
"value": "0"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
8
]
},
{
"entity_id": 8,
"token_idxs": [
10
]
},
{
"entity_id": 9,
"token_idxs": [
3
]
},
{
"entity_id": 10,
"token_idxs": [
4
]
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
13,834
|
address_1
|
bird:test.json:792
|
What are the first names and majors of students living in Baltimore?
|
SELECT T2.Fname , T2.Major FROM City AS T1 JOIN Student AS T2 ON T1.city_code = T2.city_code WHERE T1.city_name = "Baltimore"
|
[
"What",
"are",
"the",
"first",
"names",
"and",
"majors",
"of",
"students",
"living",
"in",
"Baltimore",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "city_name"
},
{
"id": 5,
"type": "column",
"value": "Baltimore"
},
{
"id": 6,
"type": "column",
"value": "city_code"
},
{
"id": 3,
"type": "table",
"value": "student"
},
{
"id": 0,
"type": "column",
"value": "fname"
},
{
"id": 1,
"type": "column",
"value": "major"
},
{
"id": 2,
"type": "table",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"token_idxs": [
11
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
13,835
|
olympics
|
bird:train.json:5017
|
What is the percentage of athletes from Vanuatu who are taller than 175?
|
SELECT CAST(COUNT(CASE WHEN T3.height > 175 THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T2.person_id) FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.region_name = 'Vanuatu'
|
[
"What",
"is",
"the",
"percentage",
"of",
"athletes",
"from",
"Vanuatu",
"who",
"are",
"taller",
"than",
"175",
"?"
] |
[
{
"id": 4,
"type": "table",
"value": "person_region"
},
{
"id": 1,
"type": "column",
"value": "region_name"
},
{
"id": 3,
"type": "table",
"value": "noc_region"
},
{
"id": 5,
"type": "column",
"value": "person_id"
},
{
"id": 8,
"type": "column",
"value": "region_id"
},
{
"id": 2,
"type": "value",
"value": "Vanuatu"
},
{
"id": 0,
"type": "table",
"value": "person"
},
{
"id": 10,
"type": "column",
"value": "height"
},
{
"id": 7,
"type": "value",
"value": "100"
},
{
"id": 11,
"type": "value",
"value": "175"
},
{
"id": 6,
"type": "column",
"value": "id"
},
{
"id": 9,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"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": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": [
12
]
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,836
|
flight_4
|
spider:train_spider.json:6871
|
Return the number of routes with destination airport in Italy operated by the airline with name 'American Airlines'.
|
SELECT count(*) FROM routes AS T1 JOIN airports AS T2 ON T1.dst_apid = T2.apid JOIN airlines AS T3 ON T1.alid = T3.alid WHERE T2.country = 'Italy' AND T3.name = 'American Airlines'
|
[
"Return",
"the",
"number",
"of",
"routes",
"with",
"destination",
"airport",
"in",
"Italy",
"operated",
"by",
"the",
"airline",
"with",
"name",
"'",
"American",
"Airlines",
"'",
"."
] |
[
{
"id": 7,
"type": "value",
"value": "American Airlines"
},
{
"id": 0,
"type": "table",
"value": "airlines"
},
{
"id": 2,
"type": "table",
"value": "airports"
},
{
"id": 8,
"type": "column",
"value": "dst_apid"
},
{
"id": 4,
"type": "column",
"value": "country"
},
{
"id": 1,
"type": "table",
"value": "routes"
},
{
"id": 5,
"type": "value",
"value": "Italy"
},
{
"id": 3,
"type": "column",
"value": "alid"
},
{
"id": 6,
"type": "column",
"value": "name"
},
{
"id": 9,
"type": "column",
"value": "apid"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
18
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
9
]
},
{
"entity_id": 6,
"token_idxs": [
15
]
},
{
"entity_id": 7,
"token_idxs": [
17
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O"
] |
13,837
|
european_football_1
|
bird:train.json:2757
|
What is the name of the division in which Club Brugge and Genk competed on September 13, 2009?
|
SELECT T2.name FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T1.Date = '2009-09-13' and T1.HomeTeam = 'Club Brugge' AND T1.AwayTeam = 'Genk'
|
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{
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{
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"value": "division"
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{
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},
{
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"value": "awayteam"
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{
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{
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"type": "column",
"value": "name"
},
{
"id": 5,
"type": "column",
"value": "date"
},
{
"id": 10,
"type": "value",
"value": "Genk"
},
{
"id": 3,
"type": "column",
"value": "div"
}
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[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,838
|
retail_world
|
bird:train.json:6397
|
Please list the full name and region of each employee in alphabetical order.
|
SELECT DISTINCT T1.FirstName, T1.LastName, T4.RegionDescription FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID INNER JOIN Region AS T4 ON T3.RegionID = T4.RegionID ORDER BY T1.FirstName
|
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"name",
"and",
"region",
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"order",
"."
] |
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{
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"type": "table",
"value": "employeeterritories"
},
{
"id": 2,
"type": "column",
"value": "regiondescription"
},
{
"id": 4,
"type": "table",
"value": "territories"
},
{
"id": 8,
"type": "column",
"value": "territoryid"
},
{
"id": 9,
"type": "column",
"value": "employeeid"
},
{
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"type": "column",
"value": "firstname"
},
{
"id": 6,
"type": "table",
"value": "employees"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 5,
"type": "column",
"value": "regionid"
},
{
"id": 3,
"type": "table",
"value": "region"
}
] |
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"O",
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"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"O"
] |
13,839
|
codebase_community
|
bird:dev.json:649
|
Describe the post history counts and last edit date of the post title "What is the best introductory Bayesian statistics textbook?"
|
SELECT T1.Id, T2.LastEditDate FROM postHistory AS T1 INNER JOIN posts AS T2 ON T1.PostId = T2.Id WHERE T2.Title = 'What is the best introductory Bayesian statistics textbook?'
|
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"\""
] |
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{
"id": 5,
"type": "value",
"value": "What is the best introductory Bayesian statistics textbook?"
},
{
"id": 1,
"type": "column",
"value": "lasteditdate"
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{
"id": 2,
"type": "table",
"value": "posthistory"
},
{
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"type": "column",
"value": "postid"
},
{
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"type": "table",
"value": "posts"
},
{
"id": 4,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "column",
"value": "id"
}
] |
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"I-COLUMN",
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"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
13,840
|
sakila_1
|
spider:train_spider.json:2968
|
What is the film title and inventory id of the item in the inventory which was rented most frequently?
|
SELECT T1.title , T2.inventory_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id = T2.film_id JOIN rental AS T3 ON T2.inventory_id = T3.inventory_id GROUP BY T2.inventory_id ORDER BY count(*) DESC LIMIT 1
|
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{
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"value": "film_id"
},
{
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"value": "rental"
},
{
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"type": "column",
"value": "title"
},
{
"id": 3,
"type": "table",
"value": "film"
}
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"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
13,841
|
behavior_monitoring
|
spider:train_spider.json:3120
|
What are the line 1 and average monthly rentals of all student addresses?
|
SELECT T1.line_1 , avg(T2.monthly_rental) FROM Addresses AS T1 JOIN Student_Addresses AS T2 ON T1.address_id = T2.address_id GROUP BY T2.address_id
|
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"all",
"student",
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"?"
] |
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{
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"value": "student_addresses"
},
{
"id": 4,
"type": "column",
"value": "monthly_rental"
},
{
"id": 0,
"type": "column",
"value": "address_id"
},
{
"id": 2,
"type": "table",
"value": "addresses"
},
{
"id": 1,
"type": "column",
"value": "line_1"
}
] |
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{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O"
] |
13,842
|
world_development_indicators
|
bird:train.json:2097
|
Among the countries in the High income: OECD group whose currency unit is Euro, how many of them have a note on the series code SP.DYN.AMRT.FE?
|
SELECT COUNT(T1.Countrycode) FROM Country AS T1 INNER JOIN CountryNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T1.IncomeGroup = 'High income: OECD' AND T1.CurrencyUnit = 'Euro' AND T2.Seriescode = 'SP.DYN.AMRT.FE'
|
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"on",
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"?"
] |
[
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"type": "value",
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},
{
"id": 8,
"type": "value",
"value": "SP.DYN.AMRT.FE"
},
{
"id": 1,
"type": "table",
"value": "countrynotes"
},
{
"id": 5,
"type": "column",
"value": "currencyunit"
},
{
"id": 2,
"type": "column",
"value": "countrycode"
},
{
"id": 3,
"type": "column",
"value": "incomegroup"
},
{
"id": 7,
"type": "column",
"value": "seriescode"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 6,
"type": "value",
"value": "Euro"
}
] |
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"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
13,843
|
mondial_geo
|
bird:train.json:8329
|
Which are the rivers that flows to Black Sea?
|
SELECT Name FROM river WHERE Sea = 'Black Sea'
|
[
"Which",
"are",
"the",
"rivers",
"that",
"flows",
"to",
"Black",
"Sea",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "Black Sea"
},
{
"id": 0,
"type": "table",
"value": "river"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "sea"
}
] |
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},
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},
{
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{
"entity_id": 16,
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},
{
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
13,844
|
bike_share_1
|
bird:train.json:9028
|
How many docks were available at the starting station of trip ID 912900?
|
SELECT SUM(T2.docks_available) FROM trip AS T1 INNER JOIN status AS T2 ON T2.station_id = T1.start_station_id WHERE T1.zip_code = 912900
|
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"station",
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"ID",
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"?"
] |
[
{
"id": 6,
"type": "column",
"value": "start_station_id"
},
{
"id": 4,
"type": "column",
"value": "docks_available"
},
{
"id": 5,
"type": "column",
"value": "station_id"
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{
"id": 2,
"type": "column",
"value": "zip_code"
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{
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{
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"type": "value",
"value": "912900"
},
{
"id": 0,
"type": "table",
"value": "trip"
}
] |
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"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
7
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
13,845
|
video_games
|
bird:train.json:3471
|
State the name of the platforms for games released in 2000.
|
SELECT DISTINCT T2.platform_name FROM game_platform AS T1 INNER JOIN platform AS T2 ON T1.platform_id = T2.id WHERE T1.release_year = 2000
|
[
"State",
"the",
"name",
"of",
"the",
"platforms",
"for",
"games",
"released",
"in",
"2000",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "platform_name"
},
{
"id": 1,
"type": "table",
"value": "game_platform"
},
{
"id": 3,
"type": "column",
"value": "release_year"
},
{
"id": 5,
"type": "column",
"value": "platform_id"
},
{
"id": 2,
"type": "table",
"value": "platform"
},
{
"id": 4,
"type": "value",
"value": "2000"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
13,846
|
protein_institute
|
spider:train_spider.json:1927
|
Show the protein name and the institution name.
|
SELECT T2.protein_name , T1.institution FROM institution AS T1 JOIN protein AS T2 ON T1.institution_id = T2.institution_id
|
[
"Show",
"the",
"protein",
"name",
"and",
"the",
"institution",
"name",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "institution_id"
},
{
"id": 0,
"type": "column",
"value": "protein_name"
},
{
"id": 1,
"type": "column",
"value": "institution"
},
{
"id": 2,
"type": "table",
"value": "institution"
},
{
"id": 3,
"type": "table",
"value": "protein"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
13,847
|
hr_1
|
spider:train_spider.json:3510
|
What are the employee ids, full names, and job ids for employees who make more than the highest earning employee with title PU_MAN?
|
SELECT employee_id , first_name , last_name , job_id FROM employees WHERE salary > ( SELECT max(salary) FROM employees WHERE job_id = 'PU_MAN' )
|
[
"What",
"are",
"the",
"employee",
"ids",
",",
"full",
"names",
",",
"and",
"job",
"ids",
"for",
"employees",
"who",
"make",
"more",
"than",
"the",
"highest",
"earning",
"employee",
"with",
"title",
"PU_MAN",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "employee_id"
},
{
"id": 2,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 3,
"type": "column",
"value": "last_name"
},
{
"id": 4,
"type": "column",
"value": "job_id"
},
{
"id": 5,
"type": "column",
"value": "salary"
},
{
"id": 6,
"type": "value",
"value": "PU_MAN"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10,
11
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
24
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,848
|
music_2
|
spider:train_spider.json:5268
|
What are all the songs in albums under label "Universal Music Group"?
|
SELECT T3.title FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid = T2.albumid JOIN songs AS T3 ON T2.songid = T3.songid WHERE t1.label = "Universal Music Group"
|
[
"What",
"are",
"all",
"the",
"songs",
"in",
"albums",
"under",
"label",
"\"",
"Universal",
"Music",
"Group",
"\"",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "Universal Music Group"
},
{
"id": 5,
"type": "table",
"value": "tracklists"
},
{
"id": 8,
"type": "column",
"value": "albumid"
},
{
"id": 4,
"type": "table",
"value": "albums"
},
{
"id": 6,
"type": "column",
"value": "songid"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 1,
"type": "table",
"value": "songs"
},
{
"id": 2,
"type": "column",
"value": "label"
},
{
"id": 7,
"type": "column",
"value": "aid"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11,
12
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O"
] |
13,849
|
restaurant
|
bird:train.json:1733
|
Which street in San Francisco has the most burger restaurants?
|
SELECT T2.street_name FROM generalinfo AS T1 INNER JOIN location AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T1.city = 'san francisco' AND T1.food_type = 'burgers' GROUP BY T2.street_name ORDER BY COUNT(T2.id_restaurant) DESC LIMIT 1
|
[
"Which",
"street",
"in",
"San",
"Francisco",
"has",
"the",
"most",
"burger",
"restaurants",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "id_restaurant"
},
{
"id": 5,
"type": "value",
"value": "san francisco"
},
{
"id": 0,
"type": "column",
"value": "street_name"
},
{
"id": 1,
"type": "table",
"value": "generalinfo"
},
{
"id": 6,
"type": "column",
"value": "food_type"
},
{
"id": 2,
"type": "table",
"value": "location"
},
{
"id": 7,
"type": "value",
"value": "burgers"
},
{
"id": 4,
"type": "column",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
3,
4
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
8
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
13,850
|
video_game
|
bird:test.json:1958
|
What are the titles of games that have platforms in the market districts of Asia or the USA?
|
SELECT T1.Title FROM game AS T1 JOIN platform AS T2 ON T1.Platform_ID = T2.Platform_ID WHERE T2.Market_district = "Asia" OR T2.Market_district = "USA"
|
[
"What",
"are",
"the",
"titles",
"of",
"games",
"that",
"have",
"platforms",
"in",
"the",
"market",
"districts",
"of",
"Asia",
"or",
"the",
"USA",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "market_district"
},
{
"id": 3,
"type": "column",
"value": "platform_id"
},
{
"id": 2,
"type": "table",
"value": "platform"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 1,
"type": "table",
"value": "game"
},
{
"id": 5,
"type": "column",
"value": "Asia"
},
{
"id": 6,
"type": "column",
"value": "USA"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
11,
12
]
},
{
"entity_id": 5,
"token_idxs": [
14
]
},
{
"entity_id": 6,
"token_idxs": [
17
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
13,851
|
law_episode
|
bird:train.json:1283
|
How many episodes are there in the 9th season of Law and Order? Calculate the average number of casts per season of the said series.
|
SELECT SUM(CASE WHEN T2.season = 9 THEN 1 ELSE 0 END) AS num , CAST(SUM(CASE WHEN T2.season = 9 THEN 1 ELSE 0 END) AS REAL) / COUNT(T1.episode_id) FROM Credit AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T1.category = 'Cast' AND T2.series = 'Law and Order'
|
[
"How",
"many",
"episodes",
"are",
"there",
"in",
"the",
"9th",
"season",
"of",
"Law",
"and",
"Order",
"?",
"Calculate",
"the",
"average",
"number",
"of",
"casts",
"per",
"season",
"of",
"the",
"said",
"series",
"."
] |
[
{
"id": 6,
"type": "value",
"value": "Law and Order"
},
{
"id": 2,
"type": "column",
"value": "episode_id"
},
{
"id": 3,
"type": "column",
"value": "category"
},
{
"id": 1,
"type": "table",
"value": "episode"
},
{
"id": 0,
"type": "table",
"value": "credit"
},
{
"id": 5,
"type": "column",
"value": "series"
},
{
"id": 9,
"type": "column",
"value": "season"
},
{
"id": 4,
"type": "value",
"value": "Cast"
},
{
"id": 7,
"type": "value",
"value": "0"
},
{
"id": 8,
"type": "value",
"value": "1"
},
{
"id": 10,
"type": "value",
"value": "9"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
20
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"entity_id": 5,
"token_idxs": [
25
]
},
{
"entity_id": 6,
"token_idxs": [
10,
11,
12
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": [
8
]
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
13,852
|
insurance_and_eClaims
|
spider:train_spider.json:1523
|
Find the names of customers who have no policies associated.
|
SELECT customer_details FROM customers EXCEPT SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id = t2.customer_id
|
[
"Find",
"the",
"names",
"of",
"customers",
"who",
"have",
"no",
"policies",
"associated",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "customer_details"
},
{
"id": 3,
"type": "column",
"value": "customer_id"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 2,
"type": "table",
"value": "policies"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
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"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
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},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
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"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
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},
{
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"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
13,853
|
college_2
|
spider:train_spider.json:1486
|
Find the names and average salaries of all departments whose average salary is greater than 42000.
|
SELECT dept_name , AVG (salary) FROM instructor GROUP BY dept_name HAVING AVG (salary) > 42000
|
[
"Find",
"the",
"names",
"and",
"average",
"salaries",
"of",
"all",
"departments",
"whose",
"average",
"salary",
"is",
"greater",
"than",
"42000",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "instructor"
},
{
"id": 1,
"type": "column",
"value": "dept_name"
},
{
"id": 3,
"type": "column",
"value": "salary"
},
{
"id": 2,
"type": "value",
"value": "42000"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,854
|
codebase_community
|
bird:dev.json:600
|
List out all post that are related to post ID 61217 and what is the popularity of this post?
|
SELECT T1.ViewCount FROM posts AS T1 INNER JOIN postLinks AS T2 ON T1.Id = T2.PostId WHERE T2.PostId = 61217
|
[
"List",
"out",
"all",
"post",
"that",
"are",
"related",
"to",
"post",
"ID",
"61217",
"and",
"what",
"is",
"the",
"popularity",
"of",
"this",
"post",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "viewcount"
},
{
"id": 2,
"type": "table",
"value": "postlinks"
},
{
"id": 3,
"type": "column",
"value": "postid"
},
{
"id": 1,
"type": "table",
"value": "posts"
},
{
"id": 4,
"type": "value",
"value": "61217"
},
{
"id": 5,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": [
9
]
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,855
|
computer_student
|
bird:train.json:1019
|
Among the students being advised by Advisor 5, how many students are in the 5th year?
|
SELECT COUNT(*) FROM advisedBy AS T1 INNER JOIN person AS T2 ON T1.p_id = T2.p_id WHERE T1.p_id_dummy = 5 AND T2.student = 1 AND T2.yearsInProgram = 'Year_5'
|
[
"Among",
"the",
"students",
"being",
"advised",
"by",
"Advisor",
"5",
",",
"how",
"many",
"students",
"are",
"in",
"the",
"5th",
"year",
"?"
] |
[
{
"id": 7,
"type": "column",
"value": "yearsinprogram"
},
{
"id": 3,
"type": "column",
"value": "p_id_dummy"
},
{
"id": 0,
"type": "table",
"value": "advisedby"
},
{
"id": 5,
"type": "column",
"value": "student"
},
{
"id": 1,
"type": "table",
"value": "person"
},
{
"id": 8,
"type": "value",
"value": "Year_5"
},
{
"id": 2,
"type": "column",
"value": "p_id"
},
{
"id": 4,
"type": "value",
"value": "5"
},
{
"id": 6,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4,
5
]
},
{
"entity_id": 1,
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},
{
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"token_idxs": []
},
{
"entity_id": 3,
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},
{
"entity_id": 4,
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7
]
},
{
"entity_id": 5,
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11
]
},
{
"entity_id": 6,
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},
{
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},
{
"entity_id": 8,
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16
]
},
{
"entity_id": 9,
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},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,856
|
music_2
|
spider:train_spider.json:5222
|
Which vocal type has the band mate with last name "Heilo" played the most?
|
SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate = T2.id WHERE lastname = "Heilo" GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1
|
[
"Which",
"vocal",
"type",
"has",
"the",
"band",
"mate",
"with",
"last",
"name",
"\"",
"Heilo",
"\"",
"played",
"the",
"most",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "lastname"
},
{
"id": 5,
"type": "column",
"value": "bandmate"
},
{
"id": 1,
"type": "table",
"value": "vocals"
},
{
"id": 4,
"type": "column",
"value": "Heilo"
},
{
"id": 0,
"type": "column",
"value": "type"
},
{
"id": 2,
"type": "table",
"value": "band"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
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11
]
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
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},
{
"entity_id": 10,
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},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
13,857
|
restaurant_bills
|
bird:test.json:614
|
How many customers are there?
|
SELECT count(*) FROM customer
|
[
"How",
"many",
"customers",
"are",
"there",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "customer"
}
] |
[
{
"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": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
13,858
|
financial
|
bird:dev.json:90
|
How many accounts who have region in Prague are eligible for loans?
|
SELECT COUNT(T1.account_id) FROM account AS T1 INNER JOIN loan AS T2 ON T1.account_id = T2.account_id INNER JOIN district AS T3 ON T1.district_id = T3.district_id WHERE T3.A3 = 'Prague'
|
[
"How",
"many",
"accounts",
"who",
"have",
"region",
"in",
"Prague",
"are",
"eligible",
"for",
"loans",
"?"
] |
[
{
"id": 6,
"type": "column",
"value": "district_id"
},
{
"id": 3,
"type": "column",
"value": "account_id"
},
{
"id": 0,
"type": "table",
"value": "district"
},
{
"id": 4,
"type": "table",
"value": "account"
},
{
"id": 2,
"type": "value",
"value": "Prague"
},
{
"id": 5,
"type": "table",
"value": "loan"
},
{
"id": 1,
"type": "column",
"value": "a3"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
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},
{
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"token_idxs": [
2
]
},
{
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11
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{
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{
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,859
|
wine_1
|
spider:train_spider.json:6558
|
What are the distinct names of wines with prices higher than any wine from John Anthony winery.
|
SELECT DISTINCT Name FROM WINE WHERE Price > (SELECT min(Price) FROM wine WHERE Winery = "John Anthony")
|
[
"What",
"are",
"the",
"distinct",
"names",
"of",
"wines",
"with",
"prices",
"higher",
"than",
"any",
"wine",
"from",
"John",
"Anthony",
"winery",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "John Anthony"
},
{
"id": 3,
"type": "column",
"value": "winery"
},
{
"id": 2,
"type": "column",
"value": "price"
},
{
"id": 0,
"type": "table",
"value": "wine"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
14,
15
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
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{
"entity_id": 7,
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},
{
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"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O"
] |
13,860
|
car_retails
|
bird:train.json:1651
|
How much did customer 103 pay in total?
|
SELECT SUM(t.amount) FROM payments t WHERE t.customerNumber = '103'
|
[
"How",
"much",
"did",
"customer",
"103",
"pay",
"in",
"total",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "customernumber"
},
{
"id": 0,
"type": "table",
"value": "payments"
},
{
"id": 3,
"type": "column",
"value": "amount"
},
{
"id": 2,
"type": "value",
"value": "103"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
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4
]
},
{
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},
{
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},
{
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},
{
"entity_id": 6,
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},
{
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{
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},
{
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},
{
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},
{
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"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
13,861
|
social_media
|
bird:train.json:830
|
Tweets that were posted from Brazil are in what languague?
|
SELECT DISTINCT T1.Lang FROM twitter AS T1 INNER JOIN location AS T2 ON T1.LocationID = T2.LocationID WHERE T2.Country = 'Brazil'
|
[
"Tweets",
"that",
"were",
"posted",
"from",
"Brazil",
"are",
"in",
"what",
"languague",
"?"
] |
[
{
"id": 5,
"type": "column",
"value": "locationid"
},
{
"id": 2,
"type": "table",
"value": "location"
},
{
"id": 1,
"type": "table",
"value": "twitter"
},
{
"id": 3,
"type": "column",
"value": "country"
},
{
"id": 4,
"type": "value",
"value": "Brazil"
},
{
"id": 0,
"type": "column",
"value": "lang"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"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": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
13,863
|
thrombosis_prediction
|
bird:dev.json:1292
|
What is the anti Cardiolipin antibody concentration of the female patient with the highest uric acid level in the normal range?
|
SELECT T3.`aCL IgG`, T3.`aCL IgM`, T3.`aCL IgA` FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID INNER JOIN Examination AS T3 ON T3.ID = T2.ID WHERE T1.SEX = 'F' AND T2.UA > 6.5 ORDER BY T2.UA DESC LIMIT 1
|
[
"What",
"is",
"the",
"anti",
"Cardiolipin",
"antibody",
"concentration",
"of",
"the",
"female",
"patient",
"with",
"the",
"highest",
"uric",
"acid",
"level",
"in",
"the",
"normal",
"range",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "examination"
},
{
"id": 6,
"type": "table",
"value": "laboratory"
},
{
"id": 0,
"type": "column",
"value": "aCL IgG"
},
{
"id": 1,
"type": "column",
"value": "aCL IgM"
},
{
"id": 2,
"type": "column",
"value": "aCL IgA"
},
{
"id": 5,
"type": "table",
"value": "patient"
},
{
"id": 8,
"type": "column",
"value": "sex"
},
{
"id": 10,
"type": "value",
"value": "6.5"
},
{
"id": 4,
"type": "column",
"value": "ua"
},
{
"id": 7,
"type": "column",
"value": "id"
},
{
"id": 9,
"type": "value",
"value": "F"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
15
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": [
7
]
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,864
|
coinmarketcap
|
bird:train.json:6256
|
List all the inactive coins and state the last date of its transaction?
|
SELECT T1.NAME, MAX(T2.DATE) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.ID = T2.coin_id WHERE T1.status = 'inactive' ORDER BY T2.DATE DESC LIMIT 1
|
[
"List",
"all",
"the",
"inactive",
"coins",
"and",
"state",
"the",
"last",
"date",
"of",
"its",
"transaction",
"?"
] |
[
{
"id": 2,
"type": "table",
"value": "historical"
},
{
"id": 4,
"type": "value",
"value": "inactive"
},
{
"id": 7,
"type": "column",
"value": "coin_id"
},
{
"id": 3,
"type": "column",
"value": "status"
},
{
"id": 1,
"type": "table",
"value": "coins"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "column",
"value": "date"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"token_idxs": [
9
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
13,865
|
sales
|
bird:train.json:5469
|
Find and list the products that sold below the average quantity.
|
SELECT DISTINCT T2.Name FROM Sales AS T1 INNER JOIN Products AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Quantity < ( SELECT AVG(Quantity) FROM Sales )
|
[
"Find",
"and",
"list",
"the",
"products",
"that",
"sold",
"below",
"the",
"average",
"quantity",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "productid"
},
{
"id": 2,
"type": "table",
"value": "products"
},
{
"id": 3,
"type": "column",
"value": "quantity"
},
{
"id": 1,
"type": "table",
"value": "sales"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
13,866
|
device
|
spider:train_spider.json:5082
|
List the names of shops that have no devices in stock.
|
SELECT Shop_Name FROM shop WHERE Shop_ID NOT IN (SELECT Shop_ID FROM stock)
|
[
"List",
"the",
"names",
"of",
"shops",
"that",
"have",
"no",
"devices",
"in",
"stock",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "shop_name"
},
{
"id": 2,
"type": "column",
"value": "shop_id"
},
{
"id": 3,
"type": "table",
"value": "stock"
},
{
"id": 0,
"type": "table",
"value": "shop"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,867
|
superhero
|
bird:dev.json:830
|
Identify the weakest attribute of the Black Panther.
|
SELECT T3.attribute_name FROM superhero AS T1 INNER JOIN hero_attribute AS T2 ON T1.id = T2.hero_id INNER JOIN attribute AS T3 ON T2.attribute_id = T3.id WHERE T1.superhero_name = 'Black Panther' ORDER BY T2.attribute_value ASC LIMIT 1
|
[
"Identify",
"the",
"weakest",
"attribute",
"of",
"the",
"Black",
"Panther",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "attribute_value"
},
{
"id": 0,
"type": "column",
"value": "attribute_name"
},
{
"id": 2,
"type": "column",
"value": "superhero_name"
},
{
"id": 6,
"type": "table",
"value": "hero_attribute"
},
{
"id": 3,
"type": "value",
"value": "Black Panther"
},
{
"id": 7,
"type": "column",
"value": "attribute_id"
},
{
"id": 1,
"type": "table",
"value": "attribute"
},
{
"id": 5,
"type": "table",
"value": "superhero"
},
{
"id": 9,
"type": "column",
"value": "hero_id"
},
{
"id": 8,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6,
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
13,868
|
cre_Drama_Workshop_Groups
|
spider:train_spider.json:5101
|
What are the store names of drama workshop groups?
|
SELECT Store_Name FROM Drama_Workshop_Groups
|
[
"What",
"are",
"the",
"store",
"names",
"of",
"drama",
"workshop",
"groups",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "drama_workshop_groups"
},
{
"id": 1,
"type": "column",
"value": "store_name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O"
] |
13,869
|
city_record
|
spider:train_spider.json:6289
|
Which cities' temperature in March is lower than that in July or higher than that in Oct?
|
SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id = T2.city_id WHERE T2.Mar < T2.Jul OR T2.Mar > T2.Oct
|
[
"Which",
"cities",
"'",
"temperature",
"in",
"March",
"is",
"lower",
"than",
"that",
"in",
"July",
"or",
"higher",
"than",
"that",
"in",
"Oct",
"?"
] |
[
{
"id": 2,
"type": "table",
"value": "temperature"
},
{
"id": 3,
"type": "column",
"value": "city_id"
},
{
"id": 0,
"type": "column",
"value": "city"
},
{
"id": 1,
"type": "table",
"value": "city"
},
{
"id": 4,
"type": "column",
"value": "mar"
},
{
"id": 5,
"type": "column",
"value": "jul"
},
{
"id": 6,
"type": "column",
"value": "oct"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"token_idxs": [
11
]
},
{
"entity_id": 6,
"token_idxs": [
17
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
13,870
|
bike_1
|
spider:train_spider.json:204
|
What are the different ids and names of the stations that have had more than 12 bikes available?
|
SELECT DISTINCT T1.id , T1.name FROM station AS T1 JOIN status AS T2 ON T1.id = T2.station_id WHERE T2.bikes_available > 12
|
[
"What",
"are",
"the",
"different",
"ids",
"and",
"names",
"of",
"the",
"stations",
"that",
"have",
"had",
"more",
"than",
"12",
"bikes",
"available",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "bikes_available"
},
{
"id": 6,
"type": "column",
"value": "station_id"
},
{
"id": 2,
"type": "table",
"value": "station"
},
{
"id": 3,
"type": "table",
"value": "status"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 0,
"type": "column",
"value": "id"
},
{
"id": 5,
"type": "value",
"value": "12"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
16,
17
]
},
{
"entity_id": 5,
"token_idxs": [
15
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
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"entity_id": 11,
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},
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"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"O"
] |
13,871
|
car_racing
|
bird:test.json:1593
|
Find the number of drivers whose points are greater than 150 for each make.
|
SELECT make , count(*) FROM driver WHERE points > 150 GROUP BY make
|
[
"Find",
"the",
"number",
"of",
"drivers",
"whose",
"points",
"are",
"greater",
"than",
"150",
"for",
"each",
"make",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "driver"
},
{
"id": 2,
"type": "column",
"value": "points"
},
{
"id": 1,
"type": "column",
"value": "make"
},
{
"id": 3,
"type": "value",
"value": "150"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O"
] |
13,872
|
address
|
bird:train.json:5093
|
Please list the bad alias of all the residential areas with a median female age of over 32.
|
SELECT DISTINCT T2.bad_alias FROM zip_data AS T1 INNER JOIN avoid AS T2 ON T1.zip_code = T2.zip_code WHERE T1.female_median_age > 32
|
[
"Please",
"list",
"the",
"bad",
"alias",
"of",
"all",
"the",
"residential",
"areas",
"with",
"a",
"median",
"female",
"age",
"of",
"over",
"32",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "female_median_age"
},
{
"id": 0,
"type": "column",
"value": "bad_alias"
},
{
"id": 1,
"type": "table",
"value": "zip_data"
},
{
"id": 5,
"type": "column",
"value": "zip_code"
},
{
"id": 2,
"type": "table",
"value": "avoid"
},
{
"id": 4,
"type": "value",
"value": "32"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13,
14
]
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
13,873
|
movies_4
|
bird:train.json:541
|
Provide the release date and language of the most popular movie.
|
SELECT T1.release_date, T3.language_name FROM movie AS T1 INNER JOIN movie_languages AS T2 ON T1.movie_id = T2.movie_id INNER JOIN language AS T3 ON T2.language_id = T3.language_id ORDER BY T1.popularity DESC LIMIT 1
|
[
"Provide",
"the",
"release",
"date",
"and",
"language",
"of",
"the",
"most",
"popular",
"movie",
"."
] |
[
{
"id": 5,
"type": "table",
"value": "movie_languages"
},
{
"id": 1,
"type": "column",
"value": "language_name"
},
{
"id": 0,
"type": "column",
"value": "release_date"
},
{
"id": 6,
"type": "column",
"value": "language_id"
},
{
"id": 3,
"type": "column",
"value": "popularity"
},
{
"id": 2,
"type": "table",
"value": "language"
},
{
"id": 7,
"type": "column",
"value": "movie_id"
},
{
"id": 4,
"type": "table",
"value": "movie"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
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},
{
"entity_id": 9,
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},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
13,874
|
movie_3
|
bird:train.json:9324
|
Which category does BABY HALL film belong to?
|
SELECT T3.`name` FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T3.category_id = T2.category_id WHERE T1.title = 'BABY HALL'
|
[
"Which",
"category",
"does",
"BABY",
"HALL",
"film",
"belong",
"to",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "film_category"
},
{
"id": 6,
"type": "column",
"value": "category_id"
},
{
"id": 3,
"type": "value",
"value": "BABY HALL"
},
{
"id": 1,
"type": "table",
"value": "category"
},
{
"id": 7,
"type": "column",
"value": "film_id"
},
{
"id": 2,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "table",
"value": "film"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3,
4
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"O",
"O"
] |
13,875
|
customers_and_addresses
|
spider:train_spider.json:6085
|
Which city has the most customers living in?
|
SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id = t2.customer_id JOIN addresses AS t3 ON t2.address_id = t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1
|
[
"Which",
"city",
"has",
"the",
"most",
"customers",
"living",
"in",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "customer_addresses"
},
{
"id": 5,
"type": "column",
"value": "customer_id"
},
{
"id": 4,
"type": "column",
"value": "address_id"
},
{
"id": 1,
"type": "table",
"value": "addresses"
},
{
"id": 2,
"type": "table",
"value": "customers"
},
{
"id": 0,
"type": "column",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
13,876
|
riding_club
|
spider:train_spider.json:1731
|
Show the names of players and names of their coaches in descending order of the votes of players.
|
SELECT T3.Player_name , T2.coach_name FROM player_coach AS T1 JOIN coach AS T2 ON T1.Coach_ID = T2.Coach_ID JOIN player AS T3 ON T1.Player_ID = T3.Player_ID ORDER BY T3.Votes DESC
|
[
"Show",
"the",
"names",
"of",
"players",
"and",
"names",
"of",
"their",
"coaches",
"in",
"descending",
"order",
"of",
"the",
"votes",
"of",
"players",
"."
] |
[
{
"id": 4,
"type": "table",
"value": "player_coach"
},
{
"id": 0,
"type": "column",
"value": "player_name"
},
{
"id": 1,
"type": "column",
"value": "coach_name"
},
{
"id": 6,
"type": "column",
"value": "player_id"
},
{
"id": 7,
"type": "column",
"value": "coach_id"
},
{
"id": 2,
"type": "table",
"value": "player"
},
{
"id": 3,
"type": "column",
"value": "votes"
},
{
"id": 5,
"type": "table",
"value": "coach"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5,
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
9
]
},
{
"entity_id": 6,
"token_idxs": [
4
]
},
{
"entity_id": 7,
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10
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
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},
{
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"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O"
] |
13,877
|
bakery_1
|
bird:test.json:1514
|
What are the receipt numbers for a customer with the last name Logan who purchased a croissant?
|
SELECT T1.ReceiptNumber FROM receipts AS T1 JOIN items AS T2 ON T1.ReceiptNumber = T2.receipt JOIN goods AS T3 ON T2.item = T3.id JOIN customers AS T4 ON T4.Id = T1.CustomerId WHERE T3.food = "Croissant" AND T4.LastName = 'LOGAN'
|
[
"What",
"are",
"the",
"receipt",
"numbers",
"for",
"a",
"customer",
"with",
"the",
"last",
"name",
"Logan",
"who",
"purchased",
"a",
"croissant",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "receiptnumber"
},
{
"id": 4,
"type": "column",
"value": "customerid"
},
{
"id": 1,
"type": "table",
"value": "customers"
},
{
"id": 6,
"type": "column",
"value": "Croissant"
},
{
"id": 7,
"type": "column",
"value": "lastname"
},
{
"id": 9,
"type": "table",
"value": "receipts"
},
{
"id": 12,
"type": "column",
"value": "receipt"
},
{
"id": 2,
"type": "table",
"value": "goods"
},
{
"id": 8,
"type": "value",
"value": "LOGAN"
},
{
"id": 10,
"type": "table",
"value": "items"
},
{
"id": 5,
"type": "column",
"value": "food"
},
{
"id": 11,
"type": "column",
"value": "item"
},
{
"id": 3,
"type": "column",
"value": "id"
}
] |
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{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
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},
{
"entity_id": 6,
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16
]
},
{
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10,
11
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},
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12
]
},
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"entity_id": 9,
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},
{
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},
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},
{
"entity_id": 12,
"token_idxs": [
3
]
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
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},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
13,878
|
video_games
|
bird:train.json:3390
|
Provide any five games and release year under the sports genre.
|
SELECT T3.game_name, T1.release_year FROM game_platform AS T1 INNER JOIN game_publisher AS T2 ON T1.game_publisher_id = T2.id INNER JOIN game AS T3 ON T2.game_id = T3.id INNER JOIN genre AS T4 ON T3.genre_id = T4.id WHERE T4.genre_name = 'Sports' LIMIT 5
|
[
"Provide",
"any",
"five",
"games",
"and",
"release",
"year",
"under",
"the",
"sports",
"genre",
"."
] |
[
{
"id": 11,
"type": "column",
"value": "game_publisher_id"
},
{
"id": 9,
"type": "table",
"value": "game_publisher"
},
{
"id": 8,
"type": "table",
"value": "game_platform"
},
{
"id": 1,
"type": "column",
"value": "release_year"
},
{
"id": 3,
"type": "column",
"value": "genre_name"
},
{
"id": 0,
"type": "column",
"value": "game_name"
},
{
"id": 6,
"type": "column",
"value": "genre_id"
},
{
"id": 10,
"type": "column",
"value": "game_id"
},
{
"id": 4,
"type": "value",
"value": "Sports"
},
{
"id": 2,
"type": "table",
"value": "genre"
},
{
"id": 5,
"type": "table",
"value": "game"
},
{
"id": 7,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"token_idxs": [
3
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
13,879
|
climbing
|
spider:train_spider.json:1118
|
How many distinct countries are the climbers from?
|
SELECT COUNT(DISTINCT Country) FROM climber
|
[
"How",
"many",
"distinct",
"countries",
"are",
"the",
"climbers",
"from",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "climber"
},
{
"id": 1,
"type": "column",
"value": "country"
}
] |
[
{
"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": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
13,880
|
bike_1
|
spider:train_spider.json:192
|
For each end station id, what is its name, latitude, and minimum duration for trips ended there?
|
SELECT T1.name , T1.lat , min(T2.duration) FROM station AS T1 JOIN trip AS T2 ON T1.id = T2.end_station_id GROUP BY T2.end_station_id
|
[
"For",
"each",
"end",
"station",
"i",
"d",
",",
"what",
"is",
"its",
"name",
",",
"latitude",
",",
"and",
"minimum",
"duration",
"for",
"trips",
"ended",
"there",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "end_station_id"
},
{
"id": 5,
"type": "column",
"value": "duration"
},
{
"id": 3,
"type": "table",
"value": "station"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "table",
"value": "trip"
},
{
"id": 2,
"type": "column",
"value": "lat"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
18
]
},
{
"entity_id": 5,
"token_idxs": [
16
]
},
{
"entity_id": 6,
"token_idxs": [
4,
5
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
13,881
|
college_1
|
spider:train_spider.json:3264
|
What are the first and last names of the employee with the earliest date of birth?
|
SELECT emp_fname , emp_lname FROM employee ORDER BY emp_dob LIMIT 1
|
[
"What",
"are",
"the",
"first",
"and",
"last",
"names",
"of",
"the",
"employee",
"with",
"the",
"earliest",
"date",
"of",
"birth",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "emp_fname"
},
{
"id": 2,
"type": "column",
"value": "emp_lname"
},
{
"id": 0,
"type": "table",
"value": "employee"
},
{
"id": 3,
"type": "column",
"value": "emp_dob"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"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": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,882
|
game_1
|
spider:train_spider.json:6015
|
Show all student IDs with more than total 10 hours per week on all sports played.
|
SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek) > 10
|
[
"Show",
"all",
"student",
"IDs",
"with",
"more",
"than",
"total",
"10",
"hours",
"per",
"week",
"on",
"all",
"sports",
"played",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "hoursperweek"
},
{
"id": 0,
"type": "table",
"value": "sportsinfo"
},
{
"id": 1,
"type": "column",
"value": "stuid"
},
{
"id": 2,
"type": "value",
"value": "10"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10,
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
13,883
|
device
|
spider:train_spider.json:5067
|
What is the shop name corresponding to the shop that opened in the most recent year?
|
SELECT Shop_Name FROM shop ORDER BY Open_Year DESC LIMIT 1
|
[
"What",
"is",
"the",
"shop",
"name",
"corresponding",
"to",
"the",
"shop",
"that",
"opened",
"in",
"the",
"most",
"recent",
"year",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "shop_name"
},
{
"id": 2,
"type": "column",
"value": "open_year"
},
{
"id": 0,
"type": "table",
"value": "shop"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,884
|
advertising_agencies
|
bird:test.json:2084
|
Count the number of invoices.
|
SELECT count(*) FROM Invoices
|
[
"Count",
"the",
"number",
"of",
"invoices",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "invoices"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"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": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
13,886
|
farm
|
spider:train_spider.json:43
|
What are the statuses and average populations of each city?
|
SELECT Status , avg(Population) FROM city GROUP BY Status
|
[
"What",
"are",
"the",
"statuses",
"and",
"average",
"populations",
"of",
"each",
"city",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "population"
},
{
"id": 1,
"type": "column",
"value": "status"
},
{
"id": 0,
"type": "table",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
13,887
|
public_review_platform
|
bird:train.json:3959
|
Is the Yelp business No. 14033 good for supper?
|
SELECT T1.attribute_value FROM Business_Attributes AS T1 INNER JOIN Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T2.attribute_name = 'good_for_dinner' AND T1.business_id = 14033
|
[
"Is",
"the",
"Yelp",
"business",
"No",
".",
"14033",
"good",
"for",
"supper",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "business_attributes"
},
{
"id": 0,
"type": "column",
"value": "attribute_value"
},
{
"id": 5,
"type": "value",
"value": "good_for_dinner"
},
{
"id": 4,
"type": "column",
"value": "attribute_name"
},
{
"id": 3,
"type": "column",
"value": "attribute_id"
},
{
"id": 6,
"type": "column",
"value": "business_id"
},
{
"id": 2,
"type": "table",
"value": "attributes"
},
{
"id": 7,
"type": "value",
"value": "14033"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
7,
8
]
},
{
"entity_id": 6,
"token_idxs": [
3
]
},
{
"entity_id": 7,
"token_idxs": [
6
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
13,888
|
retail_world
|
bird:train.json:6643
|
State the name of all territories in Northern region.
|
SELECT DISTINCT T1.TerritoryDescription FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID WHERE T2.RegionDescription = 'Northern'
|
[
"State",
"the",
"name",
"of",
"all",
"territories",
"in",
"Northern",
"region",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "territorydescription"
},
{
"id": 3,
"type": "column",
"value": "regiondescription"
},
{
"id": 1,
"type": "table",
"value": "territories"
},
{
"id": 4,
"type": "value",
"value": "Northern"
},
{
"id": 5,
"type": "column",
"value": "regionid"
},
{
"id": 2,
"type": "table",
"value": "region"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"B-TABLE",
"O"
] |
13,889
|
cre_Drama_Workshop_Groups
|
spider:train_spider.json:5138
|
What are the product names with average product price smaller than 1000000?
|
SELECT Product_Name FROM PRODUCTS GROUP BY Product_Name HAVING avg(Product_Price) < 1000000
|
[
"What",
"are",
"the",
"product",
"names",
"with",
"average",
"product",
"price",
"smaller",
"than",
"1000000",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "product_price"
},
{
"id": 1,
"type": "column",
"value": "product_name"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 2,
"type": "value",
"value": "1000000"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
13,890
|
cre_Drama_Workshop_Groups
|
spider:train_spider.json:5125
|
Return the name of the marketing region the store Rob Dinning is located in.
|
SELECT T1.Marketing_Region_Name FROM Marketing_Regions AS T1 JOIN Stores AS T2 ON T1.Marketing_Region_Code = T2.Marketing_Region_Code WHERE T2.Store_Name = "Rob Dinning"
|
[
"Return",
"the",
"name",
"of",
"the",
"marketing",
"region",
"the",
"store",
"Rob",
"Dinning",
"is",
"located",
"in",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "marketing_region_name"
},
{
"id": 5,
"type": "column",
"value": "marketing_region_code"
},
{
"id": 1,
"type": "table",
"value": "marketing_regions"
},
{
"id": 4,
"type": "column",
"value": "Rob Dinning"
},
{
"id": 3,
"type": "column",
"value": "store_name"
},
{
"id": 2,
"type": "table",
"value": "stores"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
1,
2
]
},
{
"entity_id": 4,
"token_idxs": [
9,
10
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
13,891
|
authors
|
bird:train.json:3586
|
What percentage of papers were preprinted after the year 2000?
|
SELECT CAST(SUM(CASE WHEN Year > 2000 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(Id) FROM Paper
|
[
"What",
"percentage",
"of",
"papers",
"were",
"preprinted",
"after",
"the",
"year",
"2000",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "paper"
},
{
"id": 5,
"type": "column",
"value": "year"
},
{
"id": 6,
"type": "value",
"value": "2000"
},
{
"id": 1,
"type": "value",
"value": "100"
},
{
"id": 2,
"type": "column",
"value": "id"
},
{
"id": 3,
"type": "value",
"value": "0"
},
{
"id": 4,
"type": "value",
"value": "1"
}
] |
[
{
"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": []
},
{
"entity_id": 5,
"token_idxs": [
8
]
},
{
"entity_id": 6,
"token_idxs": [
9
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
13,892
|
tv_shows
|
bird:test.json:148
|
Show the transmitters of radios and the station names of the channels they are associated with in descending order of the ERP of the radios.
|
SELECT T3.Transmitter , T2.Station_name FROM city_channel_radio AS T1 JOIN city_channel AS T2 ON T1.City_channel_ID = T2.ID JOIN radio AS T3 ON T1.Radio_ID = T3.Radio_ID ORDER BY T3.ERP_kW DESC
|
[
"Show",
"the",
"transmitters",
"of",
"radios",
"and",
"the",
"station",
"names",
"of",
"the",
"channels",
"they",
"are",
"associated",
"with",
"in",
"descending",
"order",
"of",
"the",
"ERP",
"of",
"the",
"radios",
"."
] |
[
{
"id": 4,
"type": "table",
"value": "city_channel_radio"
},
{
"id": 7,
"type": "column",
"value": "city_channel_id"
},
{
"id": 1,
"type": "column",
"value": "station_name"
},
{
"id": 5,
"type": "table",
"value": "city_channel"
},
{
"id": 0,
"type": "column",
"value": "transmitter"
},
{
"id": 6,
"type": "column",
"value": "radio_id"
},
{
"id": 3,
"type": "column",
"value": "erp_kw"
},
{
"id": 2,
"type": "table",
"value": "radio"
},
{
"id": 8,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7,
8
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
21
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
11
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
13,894
|
student_club
|
bird:dev.json:1394
|
How many members of the Student_Club have major in 'Physics Teaching'?
|
SELECT COUNT(T2.member_id) FROM major AS T1 INNER JOIN member AS T2 ON T1.major_id = T2.link_to_major WHERE T1.major_name = 'Physics Teaching'
|
[
"How",
"many",
"members",
"of",
"the",
"Student_Club",
"have",
"major",
"in",
"'",
"Physics",
"Teaching",
"'",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "Physics Teaching"
},
{
"id": 6,
"type": "column",
"value": "link_to_major"
},
{
"id": 2,
"type": "column",
"value": "major_name"
},
{
"id": 4,
"type": "column",
"value": "member_id"
},
{
"id": 5,
"type": "column",
"value": "major_id"
},
{
"id": 1,
"type": "table",
"value": "member"
},
{
"id": 0,
"type": "table",
"value": "major"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
13,895
|
culture_company
|
spider:train_spider.json:6978
|
Show publishers with a book published in 1989 and a book in 1990.
|
SELECT publisher FROM book_club WHERE YEAR = 1989 INTERSECT SELECT publisher FROM book_club WHERE YEAR = 1990
|
[
"Show",
"publishers",
"with",
"a",
"book",
"published",
"in",
"1989",
"and",
"a",
"book",
"in",
"1990",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "book_club"
},
{
"id": 1,
"type": "column",
"value": "publisher"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "1989"
},
{
"id": 4,
"type": "value",
"value": "1990"
}
] |
[
{
"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_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
13,896
|
olympics
|
bird:train.json:5036
|
Mention the height of people who belong to region id 7.
|
SELECT T2.height FROM person_region AS T1 INNER JOIN person AS T2 ON T1.person_id = T2.id WHERE T1.region_id = 7
|
[
"Mention",
"the",
"height",
"of",
"people",
"who",
"belong",
"to",
"region",
"i",
"d",
"7",
"."
] |
[
{
"id": 1,
"type": "table",
"value": "person_region"
},
{
"id": 3,
"type": "column",
"value": "region_id"
},
{
"id": 5,
"type": "column",
"value": "person_id"
},
{
"id": 0,
"type": "column",
"value": "height"
},
{
"id": 2,
"type": "table",
"value": "person"
},
{
"id": 6,
"type": "column",
"value": "id"
},
{
"id": 4,
"type": "value",
"value": "7"
}
] |
[
{
"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": [
11
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
9,
10
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
13,897
|
talkingdata
|
bird:train.json:1080
|
Among the female users of the devices, how many of them are under 30?
|
SELECT COUNT(device_id) FROM gender_age WHERE age < 30 AND gender = 'F'
|
[
"Among",
"the",
"female",
"users",
"of",
"the",
"devices",
",",
"how",
"many",
"of",
"them",
"are",
"under",
"30",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "gender_age"
},
{
"id": 1,
"type": "column",
"value": "device_id"
},
{
"id": 4,
"type": "column",
"value": "gender"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "30"
},
{
"id": 5,
"type": "value",
"value": "F"
}
] |
[
{
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"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
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13
]
},
{
"entity_id": 5,
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4
]
},
{
"entity_id": 6,
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"entity_id": 13,
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},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
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},
{
"entity_id": 18,
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},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O"
] |
13,898
|
airline
|
bird:train.json:5891
|
Provide the date and tail number of flight with air carrier "Ross Aviation Inc.: GWE".
|
SELECT T1.FL_DATE, T1.TAIL_NUM FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T2.Description = 'Ross Aviation Inc.: GWE'
|
[
"Provide",
"the",
"date",
"and",
"tail",
"number",
"of",
"flight",
"with",
"air",
"carrier",
"\"",
"Ross",
"Aviation",
"Inc.",
":",
"GWE",
"\"",
"."
] |
[
{
"id": 5,
"type": "value",
"value": "Ross Aviation Inc.: GWE"
},
{
"id": 6,
"type": "column",
"value": "op_carrier_airline_id"
},
{
"id": 3,
"type": "table",
"value": "Air Carriers"
},
{
"id": 4,
"type": "column",
"value": "description"
},
{
"id": 1,
"type": "column",
"value": "tail_num"
},
{
"id": 2,
"type": "table",
"value": "airlines"
},
{
"id": 0,
"type": "column",
"value": "fl_date"
},
{
"id": 7,
"type": "column",
"value": "code"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
"token_idxs": [
12,
13,
14,
15,
16
]
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
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},
{
"entity_id": 10,
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},
{
"entity_id": 11,
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},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
13,900
|
soccer_2016
|
bird:train.json:2023
|
Who is the youngest player and which city did he/she come from?
|
SELECT T3.City_Name FROM Player AS T1 INNER JOIN Country AS T2 ON T1.Country_Name = T2.Country_Id INNER JOIN City AS T3 ON T2.Country_Id = T3.Country_Id ORDER BY T1.DOB LIMIT 1
|
[
"Who",
"is",
"the",
"youngest",
"player",
"and",
"which",
"city",
"did",
"he",
"/",
"she",
"come",
"from",
"?"
] |
[
{
"id": 6,
"type": "column",
"value": "country_name"
},
{
"id": 5,
"type": "column",
"value": "country_id"
},
{
"id": 0,
"type": "column",
"value": "city_name"
},
{
"id": 4,
"type": "table",
"value": "country"
},
{
"id": 3,
"type": "table",
"value": "player"
},
{
"id": 1,
"type": "table",
"value": "city"
},
{
"id": 2,
"type": "column",
"value": "dob"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
13,901
|
local_govt_in_alabama
|
spider:train_spider.json:2147
|
Which service id and type has the least number of participants?
|
SELECT T3.service_id , T4.Service_Type_Code FROM participants AS T1 JOIN Participants_in_Events AS T2 ON T1.Participant_ID = T2.Participant_ID JOIN EVENTS AS T3 ON T2.Event_ID = T3.Event_ID JOIN services AS T4 ON T3.service_id = T4.service_id GROUP BY T3.service_id ORDER BY count(*) ASC LIMIT 1
|
[
"Which",
"service",
"i",
"d",
"and",
"type",
"has",
"the",
"least",
"number",
"of",
"participants",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "participants_in_events"
},
{
"id": 1,
"type": "column",
"value": "service_type_code"
},
{
"id": 7,
"type": "column",
"value": "participant_id"
},
{
"id": 4,
"type": "table",
"value": "participants"
},
{
"id": 0,
"type": "column",
"value": "service_id"
},
{
"id": 2,
"type": "table",
"value": "services"
},
{
"id": 6,
"type": "column",
"value": "event_id"
},
{
"id": 3,
"type": "table",
"value": "events"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
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