question_id
int64 0
16.1k
| db_id
stringclasses 259
values | dber_id
stringlengths 15
29
| question
stringlengths 16
325
| SQL
stringlengths 18
1.25k
| tokens
listlengths 4
62
| entities
listlengths 0
21
| entity_to_token
listlengths 20
20
| dber_tags
listlengths 4
62
|
|---|---|---|---|---|---|---|---|---|
14,450
|
card_games
|
bird:dev.json:419
|
How many color cards with no borders have been ranked higher than 12000 on EDHRec?
|
SELECT COUNT(id) FROM cards WHERE edhrecRank > 12000 AND borderColor = 'borderless'
|
[
"How",
"many",
"color",
"cards",
"with",
"no",
"borders",
"have",
"been",
"ranked",
"higher",
"than",
"12000",
"on",
"EDHRec",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "bordercolor"
},
{
"id": 2,
"type": "column",
"value": "edhrecrank"
},
{
"id": 5,
"type": "value",
"value": "borderless"
},
{
"id": 0,
"type": "table",
"value": "cards"
},
{
"id": 3,
"type": "value",
"value": "12000"
},
{
"id": 1,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
14,451
|
authors
|
bird:train.json:3568
|
List the names of all authors affiliated with Birkbeck University of London.
|
SELECT Name FROM Author WHERE Affiliation = 'Birkbeck University of London'
|
[
"List",
"the",
"names",
"of",
"all",
"authors",
"affiliated",
"with",
"Birkbeck",
"University",
"of",
"London",
"."
] |
[
{
"id": 3,
"type": "value",
"value": "Birkbeck University of London"
},
{
"id": 2,
"type": "column",
"value": "affiliation"
},
{
"id": 0,
"type": "table",
"value": "author"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
8,
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",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
14,452
|
entertainment_awards
|
spider:train_spider.json:4601
|
What are the names and locations of festivals?
|
SELECT Festival_Name , LOCATION FROM festival_detail
|
[
"What",
"are",
"the",
"names",
"and",
"locations",
"of",
"festivals",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "festival_detail"
},
{
"id": 1,
"type": "column",
"value": "festival_name"
},
{
"id": 2,
"type": "column",
"value": "location"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"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-COLUMN",
"O",
"B-COLUMN",
"O"
] |
14,453
|
food_inspection_2
|
bird:train.json:6116
|
Among the facilities that have undergone at least one inspection in 2010, how many of them are in ward no.42?
|
SELECT COUNT(DISTINCT T1.license_no) FROM inspection AS T1 INNER JOIN establishment AS T2 ON T1.license_no = T2.license_no WHERE strftime('%Y', T1.inspection_date) = '2010' AND T2.ward = 42
|
[
"Among",
"the",
"facilities",
"that",
"have",
"undergone",
"at",
"least",
"one",
"inspection",
"in",
"2010",
",",
"how",
"many",
"of",
"them",
"are",
"in",
"ward",
"no.42",
"?"
] |
[
{
"id": 7,
"type": "column",
"value": "inspection_date"
},
{
"id": 1,
"type": "table",
"value": "establishment"
},
{
"id": 0,
"type": "table",
"value": "inspection"
},
{
"id": 2,
"type": "column",
"value": "license_no"
},
{
"id": 3,
"type": "value",
"value": "2010"
},
{
"id": 4,
"type": "column",
"value": "ward"
},
{
"id": 5,
"type": "value",
"value": "42"
},
{
"id": 6,
"type": "value",
"value": "%Y"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"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",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
14,454
|
donor
|
bird:train.json:3178
|
What is the essay title of the project that have the highest total price excluding optional support and who is the biggest donor? Identify the donor and calculate how many percent did he/she donated in the project.
|
SELECT T1.title, T3.donor_acctid, CAST(T3.donation_to_project AS REAL) / T2.total_price_excluding_optional_support FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid INNER JOIN donations AS T3 ON T2.projectid = T3.projectid ORDER BY T3.donation_to_project DESC LIMIT 1
|
[
"What",
"is",
"the",
"essay",
"title",
"of",
"the",
"project",
"that",
"have",
"the",
"highest",
"total",
"price",
"excluding",
"optional",
"support",
"and",
"who",
"is",
"the",
"biggest",
"donor",
"?",
"Identify",
"the",
"donor",
"and",
"calculate",
"how",
"many",
"percent",
"did",
"he",
"/",
"she",
"donated",
"in",
"the",
"project",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "total_price_excluding_optional_support"
},
{
"id": 3,
"type": "column",
"value": "donation_to_project"
},
{
"id": 1,
"type": "column",
"value": "donor_acctid"
},
{
"id": 2,
"type": "table",
"value": "donations"
},
{
"id": 7,
"type": "column",
"value": "projectid"
},
{
"id": 6,
"type": "table",
"value": "projects"
},
{
"id": 5,
"type": "table",
"value": "essays"
},
{
"id": 0,
"type": "column",
"value": "title"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
26,
27
]
},
{
"entity_id": 2,
"token_idxs": [
36,
37
]
},
{
"entity_id": 3,
"token_idxs": [
38
]
},
{
"entity_id": 4,
"token_idxs": [
12,
13,
14,
15,
16
]
},
{
"entity_id": 5,
"token_idxs": [
3
]
},
{
"entity_id": 6,
"token_idxs": [
7
]
},
{
"entity_id": 7,
"token_idxs": [
39
]
},
{
"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-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"B-COLUMN",
"O"
] |
14,455
|
california_schools
|
bird:dev.json:71
|
What is the district code for the School that does not offer a magnet program in the city of Fresno?
|
SELECT T1.`District Code` FROM frpm AS T1 INNER JOIN schools AS T2 ON T1.CDSCode = T2.CDSCode WHERE T2.City = 'Fresno' AND T2.Magnet = 0
|
[
"What",
"is",
"the",
"district",
"code",
"for",
"the",
"School",
"that",
"does",
"not",
"offer",
"a",
"magnet",
"program",
"in",
"the",
"city",
"of",
"Fresno",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "District Code"
},
{
"id": 2,
"type": "table",
"value": "schools"
},
{
"id": 3,
"type": "column",
"value": "cdscode"
},
{
"id": 5,
"type": "value",
"value": "Fresno"
},
{
"id": 6,
"type": "column",
"value": "magnet"
},
{
"id": 1,
"type": "table",
"value": "frpm"
},
{
"id": 4,
"type": "column",
"value": "city"
},
{
"id": 7,
"type": "value",
"value": "0"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entity_id": 5,
"token_idxs": [
19
]
},
{
"entity_id": 6,
"token_idxs": [
13
]
},
{
"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",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
14,457
|
game_1
|
spider:train_spider.json:5997
|
Show all advisors who have at least two students.
|
SELECT advisor FROM Student GROUP BY advisor HAVING count(*) >= 2
|
[
"Show",
"all",
"advisors",
"who",
"have",
"at",
"least",
"two",
"students",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "advisor"
},
{
"id": 2,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"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": []
},
{
"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-TABLE",
"O"
] |
14,458
|
european_football_2
|
bird:dev.json:1124
|
Who are the players that tend to be attacking when their mates were doing attack moves? List down their name.
|
SELECT DISTINCT t1.player_name FROM Player AS t1 INNER JOIN Player_Attributes AS t2 ON t1.player_api_id = t2.player_api_id WHERE t2.attacking_work_rate = 'high'
|
[
"Who",
"are",
"the",
"players",
"that",
"tend",
"to",
"be",
"attacking",
"when",
"their",
"mates",
"were",
"doing",
"attack",
"moves",
"?",
"List",
"down",
"their",
"name",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "attacking_work_rate"
},
{
"id": 2,
"type": "table",
"value": "player_attributes"
},
{
"id": 5,
"type": "column",
"value": "player_api_id"
},
{
"id": 0,
"type": "column",
"value": "player_name"
},
{
"id": 1,
"type": "table",
"value": "player"
},
{
"id": 4,
"type": "value",
"value": "high"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"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-TABLE",
"I-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,459
|
loan_1
|
spider:train_spider.json:3032
|
Find the name and account balance of the customer whose name includes the letter ‘a’.
|
SELECT cust_name , acc_bal FROM customer WHERE cust_name LIKE '%a%'
|
[
"Find",
"the",
"name",
"and",
"account",
"balance",
"of",
"the",
"customer",
"whose",
"name",
"includes",
"the",
"letter",
"‘",
"a",
"’",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "cust_name"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 2,
"type": "column",
"value": "acc_bal"
},
{
"id": 3,
"type": "value",
"value": "%a%"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,460
|
aan_1
|
bird:test.json:966
|
Count the number of papers.
|
SELECT count(*) FROM Paper
|
[
"Count",
"the",
"number",
"of",
"papers",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "paper"
}
] |
[
{
"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"
] |
14,461
|
college_3
|
spider:train_spider.json:4673
|
Find the name of the department that has no students minored in?
|
SELECT DName FROM DEPARTMENT EXCEPT SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MINOR_IN AS T2 ON T1.DNO = T2.DNO
|
[
"Find",
"the",
"name",
"of",
"the",
"department",
"that",
"has",
"no",
"students",
"minored",
"in",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "department"
},
{
"id": 2,
"type": "table",
"value": "minor_in"
},
{
"id": 1,
"type": "column",
"value": "dname"
},
{
"id": 3,
"type": "column",
"value": "dno"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
10,
11
]
},
{
"entity_id": 3,
"token_idxs": [
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",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
14,462
|
coinmarketcap
|
bird:train.json:6276
|
What was the percentage of the Bitcoins verifiably burned until 2018/4/28?
|
SELECT CAST((SUM(T2.max_supply) - SUM(T2.total_supply)) AS REAL) / SUM(T2.total_supply) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T2.date < '2018-04-28' AND T1.name = 'Bitcoin'
|
[
"What",
"was",
"the",
"percentage",
"of",
"the",
"Bitcoins",
"verifiably",
"burned",
"until",
"2018/4/28",
"?"
] |
[
{
"id": 8,
"type": "column",
"value": "total_supply"
},
{
"id": 1,
"type": "table",
"value": "historical"
},
{
"id": 5,
"type": "value",
"value": "2018-04-28"
},
{
"id": 9,
"type": "column",
"value": "max_supply"
},
{
"id": 3,
"type": "column",
"value": "coin_id"
},
{
"id": 7,
"type": "value",
"value": "Bitcoin"
},
{
"id": 0,
"type": "table",
"value": "coins"
},
{
"id": 4,
"type": "column",
"value": "date"
},
{
"id": 6,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"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-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,463
|
california_schools
|
bird:dev.json:44
|
What is the average writing score of the school who has the highest number of test takers whose total SAT sscores are greater or equal to 1500? Indicate the city to where the school is situated.
|
SELECT T1.AvgScrWrite, T2.City FROM satscores AS T1 INNER JOIN schools AS T2 ON T1.cds = T2.CDSCode ORDER BY T1.NumGE1500 DESC LIMIT 1
|
[
"What",
"is",
"the",
"average",
"writing",
"score",
"of",
"the",
"school",
"who",
"has",
"the",
"highest",
"number",
"of",
"test",
"takers",
"whose",
"total",
"SAT",
"sscores",
"are",
"greater",
"or",
"equal",
"to",
"1500",
"?",
"Indicate",
"the",
"city",
"to",
"where",
"the",
"school",
"is",
"situated",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "avgscrwrite"
},
{
"id": 2,
"type": "table",
"value": "satscores"
},
{
"id": 4,
"type": "column",
"value": "numge1500"
},
{
"id": 3,
"type": "table",
"value": "schools"
},
{
"id": 6,
"type": "column",
"value": "cdscode"
},
{
"id": 1,
"type": "column",
"value": "city"
},
{
"id": 5,
"type": "column",
"value": "cds"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
30
]
},
{
"entity_id": 2,
"token_idxs": [
19,
20
]
},
{
"entity_id": 3,
"token_idxs": [
34
]
},
{
"entity_id": 4,
"token_idxs": [
26
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
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",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
14,464
|
formula_1
|
spider:train_spider.json:2187
|
What are the numbers of constructors for different nationalities?
|
SELECT count(*) , nationality FROM constructors GROUP BY nationality
|
[
"What",
"are",
"the",
"numbers",
"of",
"constructors",
"for",
"different",
"nationalities",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "constructors"
},
{
"id": 1,
"type": "column",
"value": "nationality"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"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",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
14,465
|
bike_1
|
spider:train_spider.json:121
|
What is the total and maximum duration of trips with bike id 636?
|
SELECT sum(duration) , max(duration) FROM trip WHERE bike_id = 636
|
[
"What",
"is",
"the",
"total",
"and",
"maximum",
"duration",
"of",
"trips",
"with",
"bike",
"i",
"d",
"636",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "duration"
},
{
"id": 1,
"type": "column",
"value": "bike_id"
},
{
"id": 0,
"type": "table",
"value": "trip"
},
{
"id": 2,
"type": "value",
"value": "636"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
10,
11,
12
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"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",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
14,466
|
language_corpus
|
bird:train.json:5724
|
What is the pair of words that is repeated the most times? Identify them by their ID.
|
SELECT w1st, w2nd FROM biwords WHERE occurrences = ( SELECT MAX(occurrences) FROM biwords )
|
[
"What",
"is",
"the",
"pair",
"of",
"words",
"that",
"is",
"repeated",
"the",
"most",
"times",
"?",
"Identify",
"them",
"by",
"their",
"ID",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "occurrences"
},
{
"id": 0,
"type": "table",
"value": "biwords"
},
{
"id": 1,
"type": "column",
"value": "w1st"
},
{
"id": 2,
"type": "column",
"value": "w2nd"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,467
|
student_club
|
bird:dev.json:1403
|
Indicate the name of the closed event whose cost has exceeded the budget the most.
|
SELECT T2.event_name FROM budget AS T1 INNER JOIN event AS T2 ON T2.event_id = T1.link_to_event WHERE T1.event_status = 'Closed' AND T1.remaining < 0 ORDER BY T1.remaining LIMIT 1
|
[
"Indicate",
"the",
"name",
"of",
"the",
"closed",
"event",
"whose",
"cost",
"has",
"exceeded",
"the",
"budget",
"the",
"most",
"."
] |
[
{
"id": 5,
"type": "column",
"value": "link_to_event"
},
{
"id": 6,
"type": "column",
"value": "event_status"
},
{
"id": 0,
"type": "column",
"value": "event_name"
},
{
"id": 3,
"type": "column",
"value": "remaining"
},
{
"id": 4,
"type": "column",
"value": "event_id"
},
{
"id": 1,
"type": "table",
"value": "budget"
},
{
"id": 7,
"type": "value",
"value": "Closed"
},
{
"id": 2,
"type": "table",
"value": "event"
},
{
"id": 8,
"type": "value",
"value": "0"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"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": [
5
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
14,469
|
codebase_comments
|
bird:train.json:629
|
How many solutions are in "https://github.com/derickbailey/presentations-and-training.git"?
|
SELECT COUNT(T2.RepoId) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T1.Url = 'https://github.com/derickbailey/presentations-and-training.git'
|
[
"How",
"many",
"solutions",
"are",
"in",
"\"",
"https://github.com/derickbailey/presentations-and-training.git",
"\"",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "https://github.com/derickbailey/presentations-and-training.git"
},
{
"id": 1,
"type": "table",
"value": "solution"
},
{
"id": 4,
"type": "column",
"value": "repoid"
},
{
"id": 0,
"type": "table",
"value": "repo"
},
{
"id": 2,
"type": "column",
"value": "url"
},
{
"id": 5,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"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",
"B-VALUE",
"O",
"O"
] |
14,470
|
university_basketball
|
spider:train_spider.json:1008
|
Count the number of different affiliation types.
|
SELECT count(DISTINCT affiliation) FROM university
|
[
"Count",
"the",
"number",
"of",
"different",
"affiliation",
"types",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "affiliation"
},
{
"id": 0,
"type": "table",
"value": "university"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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-COLUMN",
"O",
"O"
] |
14,471
|
retail_world
|
bird:train.json:6430
|
What is the description of the category that tofu belongs to?
|
SELECT T1.Description FROM Categories AS T1 INNER JOIN Products AS T2 ON T1.CategoryID = T2.CategoryID WHERE T2.ProductName = 'tofu'
|
[
"What",
"is",
"the",
"description",
"of",
"the",
"category",
"that",
"tofu",
"belongs",
"to",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 3,
"type": "column",
"value": "productname"
},
{
"id": 1,
"type": "table",
"value": "categories"
},
{
"id": 5,
"type": "column",
"value": "categoryid"
},
{
"id": 2,
"type": "table",
"value": "products"
},
{
"id": 4,
"type": "value",
"value": "tofu"
}
] |
[
{
"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": [
8
]
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"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",
"B-VALUE",
"O",
"O",
"O"
] |
14,472
|
video_games
|
bird:train.json:3374
|
Among the games released in 2004, what is the percentage of games on PSP?
|
SELECT CAST(COUNT(CASE WHEN T1.platform_name = 'PSP' THEN T3.game_id ELSE NULL END) AS REAL) * 100 / COUNT(T3.game_id) FROM platform AS T1 INNER JOIN game_platform AS T2 ON T1.id = T2.platform_id INNER JOIN game_publisher AS T3 ON T2.game_publisher_id = T3.id WHERE T2.release_year = 2004
|
[
"Among",
"the",
"games",
"released",
"in",
"2004",
",",
"what",
"is",
"the",
"percentage",
"of",
"games",
"on",
"PSP",
"?"
] |
[
{
"id": 5,
"type": "column",
"value": "game_publisher_id"
},
{
"id": 0,
"type": "table",
"value": "game_publisher"
},
{
"id": 4,
"type": "table",
"value": "game_platform"
},
{
"id": 10,
"type": "column",
"value": "platform_name"
},
{
"id": 1,
"type": "column",
"value": "release_year"
},
{
"id": 9,
"type": "column",
"value": "platform_id"
},
{
"id": 3,
"type": "table",
"value": "platform"
},
{
"id": 8,
"type": "column",
"value": "game_id"
},
{
"id": 2,
"type": "value",
"value": "2004"
},
{
"id": 7,
"type": "value",
"value": "100"
},
{
"id": 11,
"type": "value",
"value": "PSP"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
2
]
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": [
14
]
},
{
"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-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,473
|
customers_and_invoices
|
spider:train_spider.json:1626
|
Give the order ids for all orders, as well as the total product quantity in each.
|
SELECT order_id , sum(product_quantity) FROM Order_items GROUP BY order_id
|
[
"Give",
"the",
"order",
"ids",
"for",
"all",
"orders",
",",
"as",
"well",
"as",
"the",
"total",
"product",
"quantity",
"in",
"each",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "product_quantity"
},
{
"id": 0,
"type": "table",
"value": "order_items"
},
{
"id": 1,
"type": "column",
"value": "order_id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
13,
14
]
},
{
"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-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O"
] |
14,474
|
election
|
spider:train_spider.json:2798
|
What are the names of parties that have both delegates on "Appropriations" committee and
|
SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party = T2.Party_ID WHERE T1.Committee = "Appropriations" INTERSECT SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party = T2.Party_ID WHERE T1.Committee = "Economic Matters"
|
[
"What",
"are",
"the",
"names",
"of",
"parties",
"that",
"have",
"both",
"delegates",
"on",
"\"",
"Appropriations",
"\"",
"committee",
"and"
] |
[
{
"id": 5,
"type": "column",
"value": "Economic Matters"
},
{
"id": 4,
"type": "column",
"value": "Appropriations"
},
{
"id": 3,
"type": "column",
"value": "committee"
},
{
"id": 1,
"type": "table",
"value": "election"
},
{
"id": 6,
"type": "column",
"value": "party_id"
},
{
"id": 0,
"type": "column",
"value": "party"
},
{
"id": 2,
"type": "table",
"value": "party"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9,
10
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"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",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O"
] |
14,475
|
superhero
|
bird:dev.json:810
|
What is the race of the superhero with maximum attribute value?
|
SELECT T3.race FROM superhero AS T1 INNER JOIN hero_attribute AS T2 ON T1.id = T2.hero_id INNER JOIN race AS T3 ON T1.race_id = T3.id ORDER BY T2.attribute_value DESC LIMIT 1
|
[
"What",
"is",
"the",
"race",
"of",
"the",
"superhero",
"with",
"maximum",
"attribute",
"value",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "attribute_value"
},
{
"id": 4,
"type": "table",
"value": "hero_attribute"
},
{
"id": 3,
"type": "table",
"value": "superhero"
},
{
"id": 5,
"type": "column",
"value": "race_id"
},
{
"id": 7,
"type": "column",
"value": "hero_id"
},
{
"id": 0,
"type": "column",
"value": "race"
},
{
"id": 1,
"type": "table",
"value": "race"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"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-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,476
|
world_development_indicators
|
bird:train.json:2113
|
What is the series note description of the series "SP.DYN.TO65.MA.ZS" which covers the topic "Health: Mortality" in 1967?
|
SELECT T2.Description FROM Series AS T1 INNER JOIN SeriesNotes AS T2 ON T1.SeriesCode = T2.Seriescode WHERE T1.SeriesCode = 'SP.DYN.TO65.MA.ZS' AND T1.Topic = 'Health: Mortality' AND T2.Year = 'YR1967'
|
[
"What",
"is",
"the",
"series",
"note",
"description",
"of",
"the",
"series",
"\"",
"SP.DYN.TO65.MA.ZS",
"\"",
"which",
"covers",
"the",
"topic",
"\"",
"Health",
":",
"Mortality",
"\"",
"in",
"1967",
"?"
] |
[
{
"id": 4,
"type": "value",
"value": "SP.DYN.TO65.MA.ZS"
},
{
"id": 6,
"type": "value",
"value": "Health: Mortality"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 2,
"type": "table",
"value": "seriesnotes"
},
{
"id": 3,
"type": "column",
"value": "seriescode"
},
{
"id": 1,
"type": "table",
"value": "series"
},
{
"id": 8,
"type": "value",
"value": "YR1967"
},
{
"id": 5,
"type": "column",
"value": "topic"
},
{
"id": 7,
"type": "column",
"value": "year"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": [
15
]
},
{
"entity_id": 6,
"token_idxs": [
17,
18,
19
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
22
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"O"
] |
14,477
|
donor
|
bird:train.json:3216
|
Among public magnet schools,what percentage of schools that receive the donated resources as books?
|
SELECT CAST(SUM(CASE WHEN T1.project_resource_type = 'Books' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.projectid) FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.school_magnet = 't'
|
[
"Among",
"public",
"magnet",
"schools",
",",
"what",
"percentage",
"of",
"schools",
"that",
"receive",
"the",
"donated",
"resources",
"as",
"books",
"?"
] |
[
{
"id": 8,
"type": "column",
"value": "project_resource_type"
},
{
"id": 2,
"type": "column",
"value": "school_magnet"
},
{
"id": 0,
"type": "table",
"value": "resources"
},
{
"id": 4,
"type": "column",
"value": "projectid"
},
{
"id": 1,
"type": "table",
"value": "projects"
},
{
"id": 9,
"type": "value",
"value": "Books"
},
{
"id": 5,
"type": "value",
"value": "100"
},
{
"id": 3,
"type": "value",
"value": "t"
},
{
"id": 6,
"type": "value",
"value": "0"
},
{
"id": 7,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3,
4,
5
]
},
{
"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": [
15
]
},
{
"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",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
14,478
|
formula_1
|
spider:train_spider.json:2163
|
Find all the distinct id and nationality of drivers who have had laptime more than 100000 milliseconds?
|
SELECT DISTINCT T1.driverid , T1.nationality FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE T2.milliseconds > 100000
|
[
"Find",
"all",
"the",
"distinct",
"i",
"d",
"and",
"nationality",
"of",
"drivers",
"who",
"have",
"had",
"laptime",
"more",
"than",
"100000",
"milliseconds",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "milliseconds"
},
{
"id": 1,
"type": "column",
"value": "nationality"
},
{
"id": 0,
"type": "column",
"value": "driverid"
},
{
"id": 3,
"type": "table",
"value": "laptimes"
},
{
"id": 2,
"type": "table",
"value": "drivers"
},
{
"id": 5,
"type": "value",
"value": "100000"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entity_id": 5,
"token_idxs": [
16
]
},
{
"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",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
14,479
|
public_review_platform
|
bird:train.json:3814
|
What is the average rating of inactive businesses?
|
SELECT CAST(SUM(stars) AS REAL) / COUNT(business_id) AS "average" FROM Business WHERE active LIKE 'FALSE'
|
[
"What",
"is",
"the",
"average",
"rating",
"of",
"inactive",
"businesses",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "business_id"
},
{
"id": 0,
"type": "table",
"value": "business"
},
{
"id": 1,
"type": "column",
"value": "active"
},
{
"id": 2,
"type": "value",
"value": "FALSE"
},
{
"id": 4,
"type": "column",
"value": "stars"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"B-TABLE",
"O"
] |
14,480
|
mondial_geo
|
bird:train.json:8321
|
On which island does South Yorkshire situated? State it's longtitude and latitude.
|
SELECT DISTINCT T3.Longitude, T3.Latitude FROM city AS T1 INNER JOIN locatedOn AS T2 ON T1.Name = T2.City INNER JOIN island AS T3 ON T3.Name = T2.Island WHERE T1.Province = 'South Yorkshire'
|
[
"On",
"which",
"island",
"does",
"South",
"Yorkshire",
"situated",
"?",
"State",
"it",
"'s",
"longtitude",
"and",
"latitude",
"."
] |
[
{
"id": 4,
"type": "value",
"value": "South Yorkshire"
},
{
"id": 0,
"type": "column",
"value": "longitude"
},
{
"id": 6,
"type": "table",
"value": "locatedon"
},
{
"id": 1,
"type": "column",
"value": "latitude"
},
{
"id": 3,
"type": "column",
"value": "province"
},
{
"id": 2,
"type": "table",
"value": "island"
},
{
"id": 8,
"type": "column",
"value": "island"
},
{
"id": 5,
"type": "table",
"value": "city"
},
{
"id": 7,
"type": "column",
"value": "name"
},
{
"id": 9,
"type": "column",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4,
5
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
2
]
},
{
"entity_id": 9,
"token_idxs": [
9
]
},
{
"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-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O"
] |
14,481
|
donor
|
bird:train.json:3155
|
What is the project in which 320 students will be impacted if the project is funded? Name the project and state the project cost.
|
SELECT T1.title, T2.total_price_excluding_optional_support FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.students_reached = 320
|
[
"What",
"is",
"the",
"project",
"in",
"which",
"320",
"students",
"will",
"be",
"impacted",
"if",
"the",
"project",
"is",
"funded",
"?",
"Name",
"the",
"project",
"and",
"state",
"the",
"project",
"cost",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "total_price_excluding_optional_support"
},
{
"id": 4,
"type": "column",
"value": "students_reached"
},
{
"id": 6,
"type": "column",
"value": "projectid"
},
{
"id": 3,
"type": "table",
"value": "projects"
},
{
"id": 2,
"type": "table",
"value": "essays"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 5,
"type": "value",
"value": "320"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
23
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"entity_id": 6,
"token_idxs": [
3
]
},
{
"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-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
14,482
|
college_1
|
spider:train_spider.json:3334
|
What is the name of the department that offers a course that has a description including the word "Statistics"?
|
SELECT T2.dept_name FROM course AS T1 JOIN department AS T2 ON T1.dept_code = T2.dept_code WHERE T1.crs_description LIKE '%Statistics%'
|
[
"What",
"is",
"the",
"name",
"of",
"the",
"department",
"that",
"offers",
"a",
"course",
"that",
"has",
"a",
"description",
"including",
"the",
"word",
"\"",
"Statistics",
"\"",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "crs_description"
},
{
"id": 4,
"type": "value",
"value": "%Statistics%"
},
{
"id": 2,
"type": "table",
"value": "department"
},
{
"id": 0,
"type": "column",
"value": "dept_name"
},
{
"id": 5,
"type": "column",
"value": "dept_code"
},
{
"id": 1,
"type": "table",
"value": "course"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"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",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
14,483
|
movielens
|
bird:train.json:2278
|
For the male users no older than 18, how many times have they given the highest rating?
|
SELECT COUNT(T1.movieid) FROM u2base AS T1 INNER JOIN users AS T2 ON T1.userid = T2.userid WHERE T1.rating = 5 AND T2.age < 18 AND T2.u_gender = 'M'
|
[
"For",
"the",
"male",
"users",
"no",
"older",
"than",
"18",
",",
"how",
"many",
"times",
"have",
"they",
"given",
"the",
"highest",
"rating",
"?"
] |
[
{
"id": 8,
"type": "column",
"value": "u_gender"
},
{
"id": 2,
"type": "column",
"value": "movieid"
},
{
"id": 0,
"type": "table",
"value": "u2base"
},
{
"id": 3,
"type": "column",
"value": "userid"
},
{
"id": 4,
"type": "column",
"value": "rating"
},
{
"id": 1,
"type": "table",
"value": "users"
},
{
"id": 6,
"type": "column",
"value": "age"
},
{
"id": 7,
"type": "value",
"value": "18"
},
{
"id": 5,
"type": "value",
"value": "5"
},
{
"id": 9,
"type": "value",
"value": "M"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
7
]
},
{
"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",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,484
|
mental_health_survey
|
bird:train.json:4599
|
State the number of questions that were asked in the "mental health survey for 2018".
|
SELECT COUNT(T1.QuestionID) FROM Answer AS T1 INNER JOIN Survey AS T2 ON T1.SurveyID = T2.SurveyID WHERE T2.Description = 'mental health survey for 2018'
|
[
"State",
"the",
"number",
"of",
"questions",
"that",
"were",
"asked",
"in",
"the",
"\"",
"mental",
"health",
"survey",
"for",
"2018",
"\"",
"."
] |
[
{
"id": 3,
"type": "value",
"value": "mental health survey for 2018"
},
{
"id": 2,
"type": "column",
"value": "description"
},
{
"id": 4,
"type": "column",
"value": "questionid"
},
{
"id": 5,
"type": "column",
"value": "surveyid"
},
{
"id": 0,
"type": "table",
"value": "answer"
},
{
"id": 1,
"type": "table",
"value": "survey"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11,
12,
14,
15
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"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-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
14,486
|
car_retails
|
bird:train.json:1646
|
Name the Sales Manager of Europe, Middle East, and Africa region. In which office does he/she report to?
|
SELECT t2.firstName, t2.lastName FROM offices AS t1 INNER JOIN employees AS t2 ON t1.officeCode = t2.officeCode WHERE t2.jobTitle = 'Sale Manager (EMEA)'
|
[
"Name",
"the",
"Sales",
"Manager",
"of",
"Europe",
",",
"Middle",
"East",
",",
"and",
"Africa",
"region",
".",
"In",
"which",
"office",
"does",
"he",
"/",
"she",
"report",
"to",
"?"
] |
[
{
"id": 5,
"type": "value",
"value": "Sale Manager (EMEA)"
},
{
"id": 6,
"type": "column",
"value": "officecode"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 3,
"type": "table",
"value": "employees"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 4,
"type": "column",
"value": "jobtitle"
},
{
"id": 2,
"type": "table",
"value": "offices"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
2,
3
]
},
{
"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",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,487
|
architecture
|
spider:train_spider.json:6947
|
What is the maximum length in meters for the bridges and what are the architects' names?
|
SELECT max(T1.length_meters) , T2.name FROM bridge AS T1 JOIN architect AS T2 ON T1.architect_id = T2.id
|
[
"What",
"is",
"the",
"maximum",
"length",
"in",
"meters",
"for",
"the",
"bridges",
"and",
"what",
"are",
"the",
"architects",
"'",
"names",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "length_meters"
},
{
"id": 4,
"type": "column",
"value": "architect_id"
},
{
"id": 2,
"type": "table",
"value": "architect"
},
{
"id": 1,
"type": "table",
"value": "bridge"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
4,
5,
6
]
},
{
"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-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O"
] |
14,488
|
superstore
|
bird:train.json:2365
|
What are the names of the products that were ordered by Alejandro Grove?
|
SELECT DISTINCT T3.`Product Name` FROM west_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T1.`Product ID` WHERE T2.`Customer Name` = 'Alejandro Grove'
|
[
"What",
"are",
"the",
"names",
"of",
"the",
"products",
"that",
"were",
"ordered",
"by",
"Alejandro",
"Grove",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "Alejandro Grove"
},
{
"id": 4,
"type": "table",
"value": "west_superstore"
},
{
"id": 2,
"type": "column",
"value": "Customer Name"
},
{
"id": 0,
"type": "column",
"value": "Product Name"
},
{
"id": 7,
"type": "column",
"value": "Customer ID"
},
{
"id": 6,
"type": "column",
"value": "Product ID"
},
{
"id": 1,
"type": "table",
"value": "product"
},
{
"id": 5,
"type": "table",
"value": "people"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
2,
3
]
},
{
"entity_id": 3,
"token_idxs": [
11,
12
]
},
{
"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",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
14,489
|
disney
|
bird:train.json:4648
|
The character Donald Duck has appeared in two Disney movies, which one has more grossing?
|
SELECT T1.movie_title FROM movies_total_gross AS T1 INNER JOIN characters AS T2 ON T1.movie_title = T2.movie_title WHERE T2.hero = 'Donald Duck' ORDER BY CAST(REPLACE(SUBSTR(total_gross, 2), ',', '') AS REAL) DESC LIMIT 1
|
[
"The",
"character",
"Donald",
"Duck",
"has",
"appeared",
"in",
"two",
"Disney",
"movies",
",",
"which",
"one",
"has",
"more",
"grossing",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "movies_total_gross"
},
{
"id": 0,
"type": "column",
"value": "movie_title"
},
{
"id": 4,
"type": "value",
"value": "Donald Duck"
},
{
"id": 6,
"type": "column",
"value": "total_gross"
},
{
"id": 2,
"type": "table",
"value": "characters"
},
{
"id": 3,
"type": "column",
"value": "hero"
},
{
"id": 5,
"type": "value",
"value": ","
},
{
"id": 7,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2,
3
]
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"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-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,490
|
apartment_rentals
|
spider:train_spider.json:1235
|
What are the start date and end date of the apartment bookings made by female guests (gender code "Female")?
|
SELECT T1.booking_start_date , T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id = T2.guest_id WHERE T2.gender_code = "Female"
|
[
"What",
"are",
"the",
"start",
"date",
"and",
"end",
"date",
"of",
"the",
"apartment",
"bookings",
"made",
"by",
"female",
"guests",
"(",
"gender",
"code",
"\"",
"Female",
"\"",
")",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "booking_start_date"
},
{
"id": 1,
"type": "table",
"value": "apartment_bookings"
},
{
"id": 3,
"type": "column",
"value": "gender_code"
},
{
"id": 5,
"type": "column",
"value": "guest_id"
},
{
"id": 2,
"type": "table",
"value": "guests"
},
{
"id": 4,
"type": "column",
"value": "Female"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
10,
11
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
17,
18
]
},
{
"entity_id": 4,
"token_idxs": [
20
]
},
{
"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",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
14,491
|
retail_complains
|
bird:train.json:374
|
What is the social number of the client who has the longest delay in his/her complaint? Calculate the days of delay and state the company's response to the consumer.
|
SELECT T1.social , 365 * (strftime('%Y', T2.`Date sent to company`) - strftime('%Y', T2.`Date received`)) + 30 * (strftime('%M', T2.`Date sent to company`) - strftime('%M', T2.`Date received`)) + (strftime('%d', T2.`Date sent to company`) - strftime('%d', T2.`Date received`)), T2.`Company response to consumer` FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID ORDER BY 365 * (strftime('%Y', T2.`Date sent to company`) - strftime('%Y', T2.`Date received`)) + 30 * (strftime('%M', T2.`Date sent to company`) - strftime('%M', T2.`Date received`)) + (strftime('%d', T2.`Date sent to company`) - strftime('%d', T2.`Date received`)) DESC LIMIT 1
|
[
"What",
"is",
"the",
"social",
"number",
"of",
"the",
"client",
"who",
"has",
"the",
"longest",
"delay",
"in",
"his",
"/",
"her",
"complaint",
"?",
"Calculate",
"the",
"days",
"of",
"delay",
"and",
"state",
"the",
"company",
"'s",
"response",
"to",
"the",
"consumer",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "Company response to consumer"
},
{
"id": 8,
"type": "column",
"value": "Date sent to company"
},
{
"id": 9,
"type": "column",
"value": "Date received"
},
{
"id": 4,
"type": "column",
"value": "client_id"
},
{
"id": 0,
"type": "column",
"value": "social"
},
{
"id": 2,
"type": "table",
"value": "client"
},
{
"id": 3,
"type": "table",
"value": "events"
},
{
"id": 5,
"type": "value",
"value": "365"
},
{
"id": 6,
"type": "value",
"value": "30"
},
{
"id": 7,
"type": "value",
"value": "%d"
},
{
"id": 10,
"type": "value",
"value": "%Y"
},
{
"id": 11,
"type": "value",
"value": "%M"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
28,
29,
30,
31,
32
]
},
{
"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": [
25,
26,
27
]
},
{
"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-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
14,492
|
club_leader
|
bird:test.json:652
|
Show the nations that have at least two members.
|
SELECT Nationality FROM member GROUP BY Nationality HAVING COUNT(*) >= 2
|
[
"Show",
"the",
"nations",
"that",
"have",
"at",
"least",
"two",
"members",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "nationality"
},
{
"id": 0,
"type": "table",
"value": "member"
},
{
"id": 2,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"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",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,493
|
movies_4
|
bird:train.json:525
|
What is the most common keyword among all the movies released in 2006?
|
SELECT T3.keyword_name FROM movie AS T1 INNER JOIN movie_keywords AS T2 ON T1.movie_id = T2.movie_id INNER JOIN keyword AS T3 ON T2.keyword_id = T3.keyword_id WHERE T1.release_date LIKE '2006%' GROUP BY T3.keyword_name ORDER BY COUNT(T3.keyword_name) DESC LIMIT 1
|
[
"What",
"is",
"the",
"most",
"common",
"keyword",
"among",
"all",
"the",
"movies",
"released",
"in",
"2006",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "movie_keywords"
},
{
"id": 0,
"type": "column",
"value": "keyword_name"
},
{
"id": 2,
"type": "column",
"value": "release_date"
},
{
"id": 6,
"type": "column",
"value": "keyword_id"
},
{
"id": 7,
"type": "column",
"value": "movie_id"
},
{
"id": 1,
"type": "table",
"value": "keyword"
},
{
"id": 3,
"type": "value",
"value": "2006%"
},
{
"id": 4,
"type": "table",
"value": "movie"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"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",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
14,495
|
store_1
|
spider:train_spider.json:599
|
Who is the youngest employee in the company? List employee's first and last name.
|
SELECT first_name , last_name FROM employees ORDER BY birth_date DESC LIMIT 1;
|
[
"Who",
"is",
"the",
"youngest",
"employee",
"in",
"the",
"company",
"?",
"List",
"employee",
"'s",
"first",
"and",
"last",
"name",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "first_name"
},
{
"id": 3,
"type": "column",
"value": "birth_date"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 2,
"type": "column",
"value": "last_name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
10,
11
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
14,
15
]
},
{
"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",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,497
|
small_bank_1
|
spider:train_spider.json:1803
|
What are the names and balances of checking accounts belonging to the customer with the lowest savings balance?
|
SELECT T1.name , T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid = T2.custid JOIN savings AS T3 ON T1.custid = T3.custid ORDER BY T3.balance LIMIT 1
|
[
"What",
"are",
"the",
"names",
"and",
"balances",
"of",
"checking",
"accounts",
"belonging",
"to",
"the",
"customer",
"with",
"the",
"lowest",
"savings",
"balance",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "accounts"
},
{
"id": 4,
"type": "table",
"value": "checking"
},
{
"id": 1,
"type": "column",
"value": "balance"
},
{
"id": 2,
"type": "table",
"value": "savings"
},
{
"id": 5,
"type": "column",
"value": "custid"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,498
|
movies_4
|
bird:train.json:529
|
Provide the average revenue of all the French movies.
|
SELECT AVG(T1.revenue) FROM movie AS T1 INNER JOIN production_COUNTry AS T2 ON T1.movie_id = T2.movie_id INNER JOIN COUNTry AS T3 ON T2.COUNTry_id = T3.COUNTry_id WHERE T3.COUNTry_name = 'France'
|
[
"Provide",
"the",
"average",
"revenue",
"of",
"all",
"the",
"French",
"movies",
"."
] |
[
{
"id": 5,
"type": "table",
"value": "production_country"
},
{
"id": 1,
"type": "column",
"value": "country_name"
},
{
"id": 6,
"type": "column",
"value": "country_id"
},
{
"id": 7,
"type": "column",
"value": "movie_id"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 3,
"type": "column",
"value": "revenue"
},
{
"id": 2,
"type": "value",
"value": "France"
},
{
"id": 4,
"type": "table",
"value": "movie"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"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",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
14,499
|
customers_campaigns_ecommerce
|
spider:train_spider.json:4634
|
Show the names of customers having an order with shipping method FedEx and order status Paid.
|
SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id = T2.customer_id WHERE shipping_method_code = 'FedEx' AND order_status_code = 'Paid'
|
[
"Show",
"the",
"names",
"of",
"customers",
"having",
"an",
"order",
"with",
"shipping",
"method",
"FedEx",
"and",
"order",
"status",
"Paid",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "shipping_method_code"
},
{
"id": 6,
"type": "column",
"value": "order_status_code"
},
{
"id": 2,
"type": "table",
"value": "customer_orders"
},
{
"id": 0,
"type": "column",
"value": "customer_name"
},
{
"id": 3,
"type": "column",
"value": "customer_id"
},
{
"id": 1,
"type": "table",
"value": "customers"
},
{
"id": 5,
"type": "value",
"value": "FedEx"
},
{
"id": 7,
"type": "value",
"value": "Paid"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9,
10
]
},
{
"entity_id": 5,
"token_idxs": [
11
]
},
{
"entity_id": 6,
"token_idxs": [
13,
14
]
},
{
"entity_id": 7,
"token_idxs": [
15
]
},
{
"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",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
14,500
|
california_schools
|
bird:dev.json:33
|
If there are any, what are the websites address of the schools with a free meal count of 1,900-2,000 to students aged 5-17? Include the name of the school.
|
SELECT T2.Website, T1.`School Name` FROM frpm AS T1 INNER JOIN schools AS T2 ON T1.CDSCode = T2.CDSCode WHERE T1.`Free Meal Count (Ages 5-17)` BETWEEN 1900 AND 2000 AND T2.Website IS NOT NULL
|
[
"If",
"there",
"are",
"any",
",",
"what",
"are",
"the",
"websites",
"address",
"of",
"the",
"schools",
"with",
"a",
"free",
"meal",
"count",
"of",
"1,900",
"-",
"2,000",
"to",
"students",
"aged",
"5",
"-",
"17",
"?",
"Include",
"the",
"name",
"of",
"the",
"school",
"."
] |
[
{
"id": 5,
"type": "column",
"value": "Free Meal Count (Ages 5-17)"
},
{
"id": 1,
"type": "column",
"value": "School Name"
},
{
"id": 0,
"type": "column",
"value": "website"
},
{
"id": 3,
"type": "table",
"value": "schools"
},
{
"id": 4,
"type": "column",
"value": "cdscode"
},
{
"id": 2,
"type": "table",
"value": "frpm"
},
{
"id": 6,
"type": "value",
"value": "1900"
},
{
"id": 7,
"type": "value",
"value": "2000"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
34
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
15,
16,
17,
18
]
},
{
"entity_id": 6,
"token_idxs": [
19
]
},
{
"entity_id": 7,
"token_idxs": [
21
]
},
{
"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",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,501
|
insurance_and_eClaims
|
spider:train_spider.json:1511
|
Which type of policy is most frequently used? Give me the policy type code.
|
SELECT policy_type_code FROM policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1
|
[
"Which",
"type",
"of",
"policy",
"is",
"most",
"frequently",
"used",
"?",
"Give",
"me",
"the",
"policy",
"type",
"code",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "policy_type_code"
},
{
"id": 0,
"type": "table",
"value": "policies"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
12,
13,
14
]
},
{
"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-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
14,502
|
flight_4
|
spider:train_spider.json:6816
|
What are the countries of all airlines whose names start with Orbit?
|
SELECT country FROM airlines WHERE name LIKE 'Orbit%'
|
[
"What",
"are",
"the",
"countries",
"of",
"all",
"airlines",
"whose",
"names",
"start",
"with",
"Orbit",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "airlines"
},
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 3,
"type": "value",
"value": "Orbit%"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
14,503
|
apartment_rentals
|
spider:train_spider.json:1239
|
What are the facility codes of the apartments with more than four bedrooms?
|
SELECT T1.facility_code FROM Apartment_Facilities AS T1 JOIN Apartments AS T2 ON T1.apt_id = T2.apt_id WHERE T2.bedroom_count > 4
|
[
"What",
"are",
"the",
"facility",
"codes",
"of",
"the",
"apartments",
"with",
"more",
"than",
"four",
"bedrooms",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "apartment_facilities"
},
{
"id": 0,
"type": "column",
"value": "facility_code"
},
{
"id": 3,
"type": "column",
"value": "bedroom_count"
},
{
"id": 2,
"type": "table",
"value": "apartments"
},
{
"id": 5,
"type": "column",
"value": "apt_id"
},
{
"id": 4,
"type": "value",
"value": "4"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"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",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,504
|
customers_and_invoices
|
spider:train_spider.json:1620
|
How many distinct order ids correspond to each product?
|
SELECT product_id , count(DISTINCT order_id) FROM Order_items GROUP BY product_id
|
[
"How",
"many",
"distinct",
"order",
"ids",
"correspond",
"to",
"each",
"product",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "order_items"
},
{
"id": 1,
"type": "column",
"value": "product_id"
},
{
"id": 2,
"type": "column",
"value": "order_id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
3,
4
]
},
{
"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",
"O",
"O",
"B-COLUMN",
"O"
] |
14,506
|
cre_Doc_Workflow
|
bird:test.json:2018
|
Show all author names.
|
SELECT author_name FROM Authors
|
[
"Show",
"all",
"author",
"names",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "author_name"
},
{
"id": 0,
"type": "table",
"value": "authors"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,508
|
retail_world
|
bird:train.json:6563
|
Among the products under grains/cereals category, provide the contact person and title of the supplier with one digit ID.
|
SELECT DISTINCT T1.ContactName, T1.ContactTitle FROM Suppliers AS T1 INNER JOIN Products AS T2 ON T1.SupplierID = T2.SupplierID INNER JOIN Categories AS T3 ON T2.CategoryID = T3.CategoryID WHERE T3.CategoryName = 'Grains/Cereals' AND T1.SupplierID BETWEEN 1 AND 10 LIMIT 1
|
[
"Among",
"the",
"products",
"under",
"grains",
"/",
"cereals",
"category",
",",
"provide",
"the",
"contact",
"person",
"and",
"title",
"of",
"the",
"supplier",
"with",
"one",
"digit",
"ID",
"."
] |
[
{
"id": 7,
"type": "value",
"value": "Grains/Cereals"
},
{
"id": 1,
"type": "column",
"value": "contacttitle"
},
{
"id": 6,
"type": "column",
"value": "categoryname"
},
{
"id": 0,
"type": "column",
"value": "contactname"
},
{
"id": 2,
"type": "table",
"value": "categories"
},
{
"id": 5,
"type": "column",
"value": "categoryid"
},
{
"id": 8,
"type": "column",
"value": "supplierid"
},
{
"id": 3,
"type": "table",
"value": "suppliers"
},
{
"id": 4,
"type": "table",
"value": "products"
},
{
"id": 10,
"type": "value",
"value": "10"
},
{
"id": 9,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
17
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
"token_idxs": [
7
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
4,
5,
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",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
14,509
|
disney
|
bird:train.json:4703
|
What movies did director Jack Kinney direct?
|
SELECT name FROM director WHERE director = 'Jack Kinney'
|
[
"What",
"movies",
"did",
"director",
"Jack",
"Kinney",
"direct",
"?"
] |
[
{
"id": 3,
"type": "value",
"value": "Jack Kinney"
},
{
"id": 0,
"type": "table",
"value": "director"
},
{
"id": 2,
"type": "column",
"value": "director"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
4,
5
]
},
{
"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",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
14,510
|
tracking_grants_for_research
|
spider:train_spider.json:4320
|
What are the distinct grant amount for the grants where the documents were sent before '1986-08-26 20:49:27' and grant were ended after '1989-03-16 18:27:16'?
|
SELECT T1.grant_amount FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id = T2.grant_id WHERE T2.sent_date < '1986-08-26 20:49:27' INTERSECT SELECT grant_amount FROM grants WHERE grant_end_date > '1989-03-16 18:27:16'
|
[
"What",
"are",
"the",
"distinct",
"grant",
"amount",
"for",
"the",
"grants",
"where",
"the",
"documents",
"were",
"sent",
"before",
"'",
"1986",
"-",
"08",
"-",
"26",
"20:49:27",
"'",
"and",
"grant",
"were",
"ended",
"after",
"'",
"1989",
"-",
"03",
"-",
"16",
"18:27:16",
"'",
"?"
] |
[
{
"id": 4,
"type": "value",
"value": "1986-08-26 20:49:27"
},
{
"id": 6,
"type": "value",
"value": "1989-03-16 18:27:16"
},
{
"id": 5,
"type": "column",
"value": "grant_end_date"
},
{
"id": 1,
"type": "column",
"value": "grant_amount"
},
{
"id": 2,
"type": "table",
"value": "documents"
},
{
"id": 3,
"type": "column",
"value": "sent_date"
},
{
"id": 7,
"type": "column",
"value": "grant_id"
},
{
"id": 0,
"type": "table",
"value": "grants"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
16,
17,
18,
19,
20,
21
]
},
{
"entity_id": 5,
"token_idxs": [
24,
25,
26,
27
]
},
{
"entity_id": 6,
"token_idxs": [
29,
30,
31,
32,
33,
34
]
},
{
"entity_id": 7,
"token_idxs": [
4
]
},
{
"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-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
14,511
|
bbc_channels
|
bird:test.json:134
|
give me the name of channels that have both a director younger than 40 and a director older than 60.
|
SELECT t1.name FROM channel AS t1 JOIN director_admin AS t2 ON t1.channel_id = t2.channel_id JOIN director AS t3 ON t2.director_id = t3.director_id WHERE t3.age < 40 INTERSECT SELECT t1.name FROM channel AS t1 JOIN director_admin AS t2 ON t1.channel_id = t2.channel_id JOIN director AS t3 ON t2.director_id = t3.director_id WHERE t3.age > 60
|
[
"give",
"me",
"the",
"name",
"of",
"channels",
"that",
"have",
"both",
"a",
"director",
"younger",
"than",
"40",
"and",
"a",
"director",
"older",
"than",
"60",
"."
] |
[
{
"id": 6,
"type": "table",
"value": "director_admin"
},
{
"id": 7,
"type": "column",
"value": "director_id"
},
{
"id": 8,
"type": "column",
"value": "channel_id"
},
{
"id": 1,
"type": "table",
"value": "director"
},
{
"id": 5,
"type": "table",
"value": "channel"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "40"
},
{
"id": 4,
"type": "value",
"value": "60"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"entity_id": 5,
"token_idxs": [
5
]
},
{
"entity_id": 6,
"token_idxs": [
16
]
},
{
"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",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
14,512
|
soccer_2
|
spider:train_spider.json:4959
|
How many different colleges were represented at tryouts?
|
SELECT count(DISTINCT cName) FROM tryout
|
[
"How",
"many",
"different",
"colleges",
"were",
"represented",
"at",
"tryouts",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "tryout"
},
{
"id": 1,
"type": "column",
"value": "cname"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"B-TABLE",
"O"
] |
14,513
|
planet_1
|
bird:test.json:1916
|
What are the number of packages shipped on Omicron Persei 8 planet or sent by Zapp Brannigan?
|
SELECT T1.PackageNumber FROM PACKAGE AS T1 JOIN Client AS T2 ON T1.Sender = T2.AccountNumber JOIN Shipment AS T3 ON T1.Shipment = T3.ShipmentID JOIN Planet AS T4 ON T3.Planet = T4.PlanetID WHERE T2.Name = "Zapp Brannigan" OR T4.Name = "Omicron Persei 8";
|
[
"What",
"are",
"the",
"number",
"of",
"packages",
"shipped",
"on",
"Omicron",
"Persei",
"8",
"planet",
"or",
"sent",
"by",
"Zapp",
"Brannigan",
"?"
] |
[
{
"id": 7,
"type": "column",
"value": "Omicron Persei 8"
},
{
"id": 6,
"type": "column",
"value": "Zapp Brannigan"
},
{
"id": 0,
"type": "column",
"value": "packagenumber"
},
{
"id": 13,
"type": "column",
"value": "accountnumber"
},
{
"id": 11,
"type": "column",
"value": "shipmentid"
},
{
"id": 2,
"type": "table",
"value": "shipment"
},
{
"id": 4,
"type": "column",
"value": "planetid"
},
{
"id": 10,
"type": "column",
"value": "shipment"
},
{
"id": 8,
"type": "table",
"value": "package"
},
{
"id": 1,
"type": "table",
"value": "planet"
},
{
"id": 3,
"type": "column",
"value": "planet"
},
{
"id": 9,
"type": "table",
"value": "client"
},
{
"id": 12,
"type": "column",
"value": "sender"
},
{
"id": 5,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
3
]
},
{
"entity_id": 6,
"token_idxs": [
15,
16
]
},
{
"entity_id": 7,
"token_idxs": [
8,
9,
10
]
},
{
"entity_id": 8,
"token_idxs": [
5
]
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": [
6
]
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": [
13
]
},
{
"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",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,514
|
cs_semester
|
bird:train.json:955
|
Calculate the difference between the average satisfaction of the students with high salaries and no salary.
|
SELECT AVG(T2.sat) - ( SELECT AVG(T2.sat) FROM RA AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id WHERE T1.salary = 'free' ) AS diff FROM RA AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id WHERE T1.salary = 'high'
|
[
"Calculate",
"the",
"difference",
"between",
"the",
"average",
"satisfaction",
"of",
"the",
"students",
"with",
"high",
"salaries",
"and",
"no",
"salary",
"."
] |
[
{
"id": 1,
"type": "table",
"value": "registration"
},
{
"id": 4,
"type": "column",
"value": "student_id"
},
{
"id": 2,
"type": "column",
"value": "salary"
},
{
"id": 3,
"type": "value",
"value": "high"
},
{
"id": 6,
"type": "value",
"value": "free"
},
{
"id": 5,
"type": "column",
"value": "sat"
},
{
"id": 0,
"type": "table",
"value": "ra"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"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-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,515
|
retails
|
bird:train.json:6675
|
How many orders in total have the customers in the household segment made?
|
SELECT COUNT(T1.o_orderkey) FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey WHERE T2.c_mktsegment = 'HOUSEHOLD'
|
[
"How",
"many",
"orders",
"in",
"total",
"have",
"the",
"customers",
"in",
"the",
"household",
"segment",
"made",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 4,
"type": "column",
"value": "o_orderkey"
},
{
"id": 3,
"type": "value",
"value": "HOUSEHOLD"
},
{
"id": 5,
"type": "column",
"value": "o_custkey"
},
{
"id": 6,
"type": "column",
"value": "c_custkey"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 0,
"type": "table",
"value": "orders"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O"
] |
14,516
|
video_games
|
bird:train.json:3453
|
Which region has the highest number of games sold on all platforms?
|
SELECT T.region_name FROM ( SELECT T2.region_name, SUM(T1.num_sales) FROM region_sales AS T1 INNER JOIN region AS T2 ON T1.region_id = T2.id INNER JOIN game_platform AS T3 ON T1.game_platform_id = T3.id INNER JOIN platform AS T4 ON T3.platform_id = T4.id GROUP BY T4.platform_name ORDER BY SUM(T1.num_sales) DESC LIMIT 1 ) t
|
[
"Which",
"region",
"has",
"the",
"highest",
"number",
"of",
"games",
"sold",
"on",
"all",
"platforms",
"?"
] |
[
{
"id": 9,
"type": "column",
"value": "game_platform_id"
},
{
"id": 1,
"type": "column",
"value": "platform_name"
},
{
"id": 4,
"type": "table",
"value": "game_platform"
},
{
"id": 7,
"type": "table",
"value": "region_sales"
},
{
"id": 0,
"type": "column",
"value": "region_name"
},
{
"id": 5,
"type": "column",
"value": "platform_id"
},
{
"id": 3,
"type": "column",
"value": "num_sales"
},
{
"id": 10,
"type": "column",
"value": "region_id"
},
{
"id": 2,
"type": "table",
"value": "platform"
},
{
"id": 8,
"type": "table",
"value": "region"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
2
]
},
{
"entity_id": 8,
"token_idxs": [
1
]
},
{
"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-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,517
|
browser_web
|
spider:train_spider.json:1841
|
Which accelerator name contains substring "Opera"?
|
SELECT name FROM web_client_accelerator WHERE name LIKE "%Opera%"
|
[
"Which",
"accelerator",
"name",
"contains",
"substring",
"\"",
"Opera",
"\"",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "web_client_accelerator"
},
{
"id": 2,
"type": "column",
"value": "%Opera%"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
14,519
|
chicago_crime
|
bird:train.json:8595
|
Please list the precise location coordinates of all the crimes in Central Chicago.
|
SELECT T2.latitude, T2.longitude FROM District AS T1 INNER JOIN Crime AS T2 ON T1.district_no = T2.district_no WHERE T1.district_name = 'Central'
|
[
"Please",
"list",
"the",
"precise",
"location",
"coordinates",
"of",
"all",
"the",
"crimes",
"in",
"Central",
"Chicago",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "district_name"
},
{
"id": 6,
"type": "column",
"value": "district_no"
},
{
"id": 1,
"type": "column",
"value": "longitude"
},
{
"id": 0,
"type": "column",
"value": "latitude"
},
{
"id": 2,
"type": "table",
"value": "district"
},
{
"id": 5,
"type": "value",
"value": "Central"
},
{
"id": 3,
"type": "table",
"value": "crime"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"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": [
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",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O"
] |
14,520
|
music_tracker
|
bird:train.json:2071
|
Indicates groups with id from 10 to 20 with singles downloaded at least 20.
|
SELECT groupName FROM torrents WHERE totalSnatched >= 20 AND releaseType LIKE 'single' AND id BETWEEN 10 AND 20
|
[
"Indicates",
"groups",
"with",
"i",
"d",
"from",
"10",
"to",
"20",
"with",
"singles",
"downloaded",
"at",
"least",
"20",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "totalsnatched"
},
{
"id": 4,
"type": "column",
"value": "releasetype"
},
{
"id": 1,
"type": "column",
"value": "groupname"
},
{
"id": 0,
"type": "table",
"value": "torrents"
},
{
"id": 5,
"type": "value",
"value": "single"
},
{
"id": 3,
"type": "value",
"value": "20"
},
{
"id": 6,
"type": "column",
"value": "id"
},
{
"id": 7,
"type": "value",
"value": "10"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"entity_id": 6,
"token_idxs": [
3,
4
]
},
{
"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",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
14,521
|
cs_semester
|
bird:train.json:956
|
Find the university from which the professor who advised most undergraduate students graduated.
|
SELECT T1.graduate_from FROM prof AS T1 INNER JOIN RA AS T2 ON T1.prof_id = T2.prof_id INNER JOIN student AS T3 ON T2.student_id = T3.student_id WHERE T3.type = 'UG' GROUP BY T1.prof_id ORDER BY COUNT(T2.student_id) DESC LIMIT 1
|
[
"Find",
"the",
"university",
"from",
"which",
"the",
"professor",
"who",
"advised",
"most",
"undergraduate",
"students",
"graduated",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "graduate_from"
},
{
"id": 7,
"type": "column",
"value": "student_id"
},
{
"id": 0,
"type": "column",
"value": "prof_id"
},
{
"id": 2,
"type": "table",
"value": "student"
},
{
"id": 3,
"type": "column",
"value": "type"
},
{
"id": 5,
"type": "table",
"value": "prof"
},
{
"id": 4,
"type": "value",
"value": "UG"
},
{
"id": 6,
"type": "table",
"value": "ra"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"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": []
},
{
"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-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,522
|
products_gen_characteristics
|
spider:train_spider.json:5580
|
Find the product category description of the product category with code "Spices".
|
SELECT product_category_description FROM ref_product_categories WHERE product_category_code = "Spices"
|
[
"Find",
"the",
"product",
"category",
"description",
"of",
"the",
"product",
"category",
"with",
"code",
"\"",
"Spices",
"\"",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "product_category_description"
},
{
"id": 0,
"type": "table",
"value": "ref_product_categories"
},
{
"id": 2,
"type": "column",
"value": "product_category_code"
},
{
"id": 3,
"type": "column",
"value": "Spices"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"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",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
14,523
|
loan_1
|
spider:train_spider.json:3074
|
Find the the name of the customers who have a loan with amount more than 3000.
|
SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id = T2.cust_id WHERE amount > 3000
|
[
"Find",
"the",
"the",
"name",
"of",
"the",
"customers",
"who",
"have",
"a",
"loan",
"with",
"amount",
"more",
"than",
"3000",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "cust_name"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 5,
"type": "column",
"value": "cust_id"
},
{
"id": 3,
"type": "column",
"value": "amount"
},
{
"id": 2,
"type": "table",
"value": "loan"
},
{
"id": 4,
"type": "value",
"value": "3000"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"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-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
14,524
|
works_cycles
|
bird:train.json:7005
|
Provide all the transactions whereby the quantiy is more than 10,000 pieces. State the product name and the selling price.
|
SELECT DISTINCT T1.Name, T1.ListPrice FROM Product AS T1 INNER JOIN TransactionHistory AS T2 ON T1.ProductID = T2.ProductID WHERE T2.Quantity > 10000
|
[
"Provide",
"all",
"the",
"transactions",
"whereby",
"the",
"quantiy",
"is",
"more",
"than",
"10,000",
"pieces",
".",
"State",
"the",
"product",
"name",
"and",
"the",
"selling",
"price",
"."
] |
[
{
"id": 3,
"type": "table",
"value": "transactionhistory"
},
{
"id": 1,
"type": "column",
"value": "listprice"
},
{
"id": 6,
"type": "column",
"value": "productid"
},
{
"id": 4,
"type": "column",
"value": "quantity"
},
{
"id": 2,
"type": "table",
"value": "product"
},
{
"id": 5,
"type": "value",
"value": "10000"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
20
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"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-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,525
|
retail_complains
|
bird:train.json:352
|
State the full name of clients with server time of 20 minutes and above.
|
SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN callcenterlogs AS T2 ON T1.client_id = T2.`rand client` WHERE strftime('%M', T2.ser_time) > '20'
|
[
"State",
"the",
"full",
"name",
"of",
"clients",
"with",
"server",
"time",
"of",
"20",
"minutes",
"and",
"above",
"."
] |
[
{
"id": 4,
"type": "table",
"value": "callcenterlogs"
},
{
"id": 7,
"type": "column",
"value": "rand client"
},
{
"id": 6,
"type": "column",
"value": "client_id"
},
{
"id": 9,
"type": "column",
"value": "ser_time"
},
{
"id": 1,
"type": "column",
"value": "middle"
},
{
"id": 3,
"type": "table",
"value": "client"
},
{
"id": 0,
"type": "column",
"value": "first"
},
{
"id": 2,
"type": "column",
"value": "last"
},
{
"id": 5,
"type": "value",
"value": "20"
},
{
"id": 8,
"type": "value",
"value": "%M"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
4
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": [
7,
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",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
14,526
|
county_public_safety
|
spider:train_spider.json:2556
|
Show the number of cities in counties that have a population more than 20000.
|
SELECT count(*) FROM city WHERE county_ID IN (SELECT county_ID FROM county_public_safety WHERE population > 20000)
|
[
"Show",
"the",
"number",
"of",
"cities",
"in",
"counties",
"that",
"have",
"a",
"population",
"more",
"than",
"20000",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "county_public_safety"
},
{
"id": 3,
"type": "column",
"value": "population"
},
{
"id": 1,
"type": "column",
"value": "county_id"
},
{
"id": 4,
"type": "value",
"value": "20000"
},
{
"id": 0,
"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": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"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-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
14,527
|
manufactory_1
|
spider:train_spider.json:5347
|
What is the name and price of the cheapest product?
|
SELECT name , price FROM Products ORDER BY price ASC LIMIT 1
|
[
"What",
"is",
"the",
"name",
"and",
"price",
"of",
"the",
"cheapest",
"product",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 2,
"type": "column",
"value": "price"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,528
|
food_inspection_2
|
bird:train.json:6236
|
Provide the inspection ID of the inspection with the comment "MUST CLEAN AND BETTER ORGANIZE HALLWAY AREA" and sanitary operating requirement code of 7-38-030, 015, 010 (A), 005 (A).
|
SELECT T2.inspection_id FROM inspection_point AS T1 INNER JOIN violation AS T2 ON T1.point_id = T2.point_id WHERE T2.inspector_comment = 'MUST CLEAN AND BETTER ORGANIZE HALLWAY AREA' AND T1.code = '7-38-030, 015, 010 (A), 005 (A)'
|
[
"Provide",
"the",
"inspection",
"ID",
"of",
"the",
"inspection",
"with",
"the",
"comment",
"\"",
"MUST",
"CLEAN",
"AND",
"BETTER",
"ORGANIZE",
"HALLWAY",
"AREA",
"\"",
"and",
"sanitary",
"operating",
"requirement",
"code",
"of",
"7",
"-",
"38",
"-",
"030",
",",
"015",
",",
"010",
"(",
"A",
")",
",",
"005",
"(",
"A",
")",
"."
] |
[
{
"id": 5,
"type": "value",
"value": "MUST CLEAN AND BETTER ORGANIZE HALLWAY AREA"
},
{
"id": 7,
"type": "value",
"value": "7-38-030, 015, 010 (A), 005 (A)"
},
{
"id": 4,
"type": "column",
"value": "inspector_comment"
},
{
"id": 1,
"type": "table",
"value": "inspection_point"
},
{
"id": 0,
"type": "column",
"value": "inspection_id"
},
{
"id": 2,
"type": "table",
"value": "violation"
},
{
"id": 3,
"type": "column",
"value": "point_id"
},
{
"id": 6,
"type": "column",
"value": "code"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7,
8,
9
]
},
{
"entity_id": 5,
"token_idxs": [
11,
12,
13,
14,
15,
16,
17
]
},
{
"entity_id": 6,
"token_idxs": [
23
]
},
{
"entity_id": 7,
"token_idxs": [
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41
]
},
{
"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",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
14,529
|
gymnast
|
spider:train_spider.json:1770
|
Show the hometowns shared by people older than 23 and younger than 20.
|
SELECT Hometown FROM people WHERE Age > 23 INTERSECT SELECT Hometown FROM people WHERE Age < 20
|
[
"Show",
"the",
"hometowns",
"shared",
"by",
"people",
"older",
"than",
"23",
"and",
"younger",
"than",
"20",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "hometown"
},
{
"id": 0,
"type": "table",
"value": "people"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "23"
},
{
"id": 4,
"type": "value",
"value": "20"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"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",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,530
|
human_resources
|
bird:train.json:8946
|
Please list the social security numbers of all the employees who work in California.
|
SELECT T1.ssn FROM employee AS T1 INNER JOIN location AS T2 ON T1.locationID = T2.locationID WHERE T2.state = 'CA'
|
[
"Please",
"list",
"the",
"social",
"security",
"numbers",
"of",
"all",
"the",
"employees",
"who",
"work",
"in",
"California",
"."
] |
[
{
"id": 5,
"type": "column",
"value": "locationid"
},
{
"id": 1,
"type": "table",
"value": "employee"
},
{
"id": 2,
"type": "table",
"value": "location"
},
{
"id": 3,
"type": "column",
"value": "state"
},
{
"id": 0,
"type": "column",
"value": "ssn"
},
{
"id": 4,
"type": "value",
"value": "CA"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
1,
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
13
]
},
{
"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",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,531
|
loan_1
|
spider:train_spider.json:3063
|
What are the names of customers who have taken out more than one loan?
|
SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id = T2.cust_id GROUP BY T1.cust_name HAVING count(*) > 1
|
[
"What",
"are",
"the",
"names",
"of",
"customers",
"who",
"have",
"taken",
"out",
"more",
"than",
"one",
"loan",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "cust_name"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 4,
"type": "column",
"value": "cust_id"
},
{
"id": 2,
"type": "table",
"value": "loan"
},
{
"id": 3,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,532
|
pilot_1
|
bird:test.json:1109
|
Which plane does the pilot Jones with age 32 has?
|
SELECT plane_name FROM pilotskills WHERE pilot_name = 'Jones' AND age = 32
|
[
"Which",
"plane",
"does",
"the",
"pilot",
"Jones",
"with",
"age",
"32",
"has",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "pilotskills"
},
{
"id": 1,
"type": "column",
"value": "plane_name"
},
{
"id": 2,
"type": "column",
"value": "pilot_name"
},
{
"id": 3,
"type": "value",
"value": "Jones"
},
{
"id": 4,
"type": "column",
"value": "age"
},
{
"id": 5,
"type": "value",
"value": "32"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs": [
8
]
},
{
"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-COLUMN",
"B-VALUE",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O"
] |
14,533
|
professional_basketball
|
bird:train.json:2920
|
State the name of teams ranked first five or more times and lost a league two or more times between 1980 and 2000?
|
SELECT T1.name FROM teams AS T1 INNER JOIN series_post AS T2 ON T1.tmID = T2.tmIDLoser AND T1.year = T2.year WHERE T1.rank < 5 AND T2.lgIDLoser > 2 AND T2.year BETWEEN 1980 AND 2000
|
[
"State",
"the",
"name",
"of",
"teams",
"ranked",
"first",
"five",
"or",
"more",
"times",
"and",
"lost",
"a",
"league",
"two",
"or",
"more",
"times",
"between",
"1980",
"and",
"2000",
"?"
] |
[
{
"id": 2,
"type": "table",
"value": "series_post"
},
{
"id": 5,
"type": "column",
"value": "lgidloser"
},
{
"id": 11,
"type": "column",
"value": "tmidloser"
},
{
"id": 1,
"type": "table",
"value": "teams"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "column",
"value": "rank"
},
{
"id": 7,
"type": "column",
"value": "year"
},
{
"id": 8,
"type": "value",
"value": "1980"
},
{
"id": 9,
"type": "value",
"value": "2000"
},
{
"id": 10,
"type": "column",
"value": "tmid"
},
{
"id": 4,
"type": "value",
"value": "5"
},
{
"id": 6,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"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": [
20
]
},
{
"entity_id": 9,
"token_idxs": [
22
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
14,534
|
department_store
|
spider:train_spider.json:4749
|
Find the id and name of the staff who has been assigned for the shortest period.
|
SELECT T1.staff_id , T1.staff_name FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id = T2.staff_id ORDER BY date_assigned_to - date_assigned_from LIMIT 1
|
[
"Find",
"the",
"i",
"d",
"and",
"name",
"of",
"the",
"staff",
"who",
"has",
"been",
"assigned",
"for",
"the",
"shortest",
"period",
"."
] |
[
{
"id": 3,
"type": "table",
"value": "staff_department_assignments"
},
{
"id": 5,
"type": "column",
"value": "date_assigned_from"
},
{
"id": 4,
"type": "column",
"value": "date_assigned_to"
},
{
"id": 1,
"type": "column",
"value": "staff_name"
},
{
"id": 0,
"type": "column",
"value": "staff_id"
},
{
"id": 2,
"type": "table",
"value": "staff"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity_id": 5,
"token_idxs": [
13
]
},
{
"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-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
14,535
|
disney
|
bird:train.json:4644
|
For the movie in which Tress MacNeille was the voice actor for its character "Hyacinth Hippo", what was the release date of that movie?
|
SELECT T1.release_date FROM characters AS T1 INNER JOIN `voice-actors` AS T2 ON T2.movie = T1.movie_title WHERE T2.character = 'Hyacinth Hippo' AND T2.`voice-actor` = 'Tress MacNeille'
|
[
"For",
"the",
"movie",
"in",
"which",
"Tress",
"MacNeille",
"was",
"the",
"voice",
"actor",
"for",
"its",
"character",
"\"",
"Hyacinth",
"Hippo",
"\"",
",",
"what",
"was",
"the",
"release",
"date",
"of",
"that",
"movie",
"?"
] |
[
{
"id": 8,
"type": "value",
"value": "Tress MacNeille"
},
{
"id": 6,
"type": "value",
"value": "Hyacinth Hippo"
},
{
"id": 0,
"type": "column",
"value": "release_date"
},
{
"id": 2,
"type": "table",
"value": "voice-actors"
},
{
"id": 4,
"type": "column",
"value": "movie_title"
},
{
"id": 7,
"type": "column",
"value": "voice-actor"
},
{
"id": 1,
"type": "table",
"value": "characters"
},
{
"id": 5,
"type": "column",
"value": "character"
},
{
"id": 3,
"type": "column",
"value": "movie"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
22,
23
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"token_idxs": [
13
]
},
{
"entity_id": 6,
"token_idxs": [
15,
16
]
},
{
"entity_id": 7,
"token_idxs": [
9,
10
]
},
{
"entity_id": 8,
"token_idxs": [
5,
6
]
},
{
"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-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
14,536
|
codebase_comments
|
bird:train.json:661
|
How many more stars in percentage are there for the repository of solution "1" than solution "2"?
|
SELECT CAST(SUM(CASE WHEN T2.Id = 1 THEN T1.Stars ELSE 0 END) - SUM(CASE WHEN T2.Id = 2 THEN T1.Stars ELSE 0 END) AS REAL) * 100 / SUM(CASE WHEN T2.Id = 2 THEN T1.Stars ELSE 0 END) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId
|
[
"How",
"many",
"more",
"stars",
"in",
"percentage",
"are",
"there",
"for",
"the",
"repository",
"of",
"solution",
"\"",
"1",
"\"",
"than",
"solution",
"\"",
"2",
"\"",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "solution"
},
{
"id": 3,
"type": "column",
"value": "repoid"
},
{
"id": 6,
"type": "column",
"value": "stars"
},
{
"id": 0,
"type": "table",
"value": "repo"
},
{
"id": 4,
"type": "value",
"value": "100"
},
{
"id": 2,
"type": "column",
"value": "id"
},
{
"id": 5,
"type": "value",
"value": "0"
},
{
"id": 7,
"type": "value",
"value": "2"
},
{
"id": 8,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"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": [
3
]
},
{
"entity_id": 7,
"token_idxs": [
19
]
},
{
"entity_id": 8,
"token_idxs": [
14
]
},
{
"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",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O"
] |
14,537
|
cre_Drama_Workshop_Groups
|
spider:train_spider.json:5120
|
Which city is the address of the store named "FJA Filming" located in?
|
SELECT T1.City_Town FROM Addresses AS T1 JOIN Stores AS T2 ON T1.Address_ID = T2.Address_ID WHERE T2.Store_Name = "FJA Filming"
|
[
"Which",
"city",
"is",
"the",
"address",
"of",
"the",
"store",
"named",
"\"",
"FJA",
"Filming",
"\"",
"located",
"in",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "FJA Filming"
},
{
"id": 3,
"type": "column",
"value": "store_name"
},
{
"id": 5,
"type": "column",
"value": "address_id"
},
{
"id": 0,
"type": "column",
"value": "city_town"
},
{
"id": 1,
"type": "table",
"value": "addresses"
},
{
"id": 2,
"type": "table",
"value": "stores"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
10,
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-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
14,538
|
works_cycles
|
bird:train.json:7057
|
What is the highest pay rate of the employees who are exempt from collective bargaining?
|
SELECT T1.Rate FROM EmployeePayHistory AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.SalariedFlag = 1 ORDER BY T1.Rate DESC LIMIT 1
|
[
"What",
"is",
"the",
"highest",
"pay",
"rate",
"of",
"the",
"employees",
"who",
"are",
"exempt",
"from",
"collective",
"bargaining",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "employeepayhistory"
},
{
"id": 5,
"type": "column",
"value": "businessentityid"
},
{
"id": 3,
"type": "column",
"value": "salariedflag"
},
{
"id": 2,
"type": "table",
"value": "employee"
},
{
"id": 0,
"type": "column",
"value": "rate"
},
{
"id": 4,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"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-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,539
|
superhero
|
bird:dev.json:753
|
Among the superheroes with height from 170 to 190, list the names of the superheroes with no eye color.
|
SELECT DISTINCT T1.superhero_name FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.eye_colour_id = T2.id WHERE T1.height_cm BETWEEN 170 AND 190 AND T2.colour = 'No Colour'
|
[
"Among",
"the",
"superheroes",
"with",
"height",
"from",
"170",
"to",
"190",
",",
"list",
"the",
"names",
"of",
"the",
"superheroes",
"with",
"no",
"eye",
"color",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "superhero_name"
},
{
"id": 3,
"type": "column",
"value": "eye_colour_id"
},
{
"id": 1,
"type": "table",
"value": "superhero"
},
{
"id": 5,
"type": "column",
"value": "height_cm"
},
{
"id": 9,
"type": "value",
"value": "No Colour"
},
{
"id": 2,
"type": "table",
"value": "colour"
},
{
"id": 8,
"type": "column",
"value": "colour"
},
{
"id": 6,
"type": "value",
"value": "170"
},
{
"id": 7,
"type": "value",
"value": "190"
},
{
"id": 4,
"type": "column",
"value": "id"
}
] |
[
{
"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": [
4
]
},
{
"entity_id": 6,
"token_idxs": [
6
]
},
{
"entity_id": 7,
"token_idxs": [
8
]
},
{
"entity_id": 8,
"token_idxs": [
19
]
},
{
"entity_id": 9,
"token_idxs": [
17,
18
]
},
{
"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",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
14,540
|
dorm_1
|
spider:train_spider.json:5728
|
Find the average age and number of male students (with sex M) from each city.
|
SELECT count(*) , avg(age) , city_code FROM student WHERE sex = 'M' GROUP BY city_code
|
[
"Find",
"the",
"average",
"age",
"and",
"number",
"of",
"male",
"students",
"(",
"with",
"sex",
"M",
")",
"from",
"each",
"city",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "city_code"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 2,
"type": "column",
"value": "sex"
},
{
"id": 4,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "M"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
16
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"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",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,541
|
legislator
|
bird:train.json:4790
|
Among the legislators who will end in 2009, how many are from the Republican party?
|
SELECT `END`, party FROM `current-terms` WHERE STRFTIME('%Y', `END`) = '2009' AND party = 'Republican'
|
[
"Among",
"the",
"legislators",
"who",
"will",
"end",
"in",
"2009",
",",
"how",
"many",
"are",
"from",
"the",
"Republican",
"party",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "current-terms"
},
{
"id": 4,
"type": "value",
"value": "Republican"
},
{
"id": 2,
"type": "column",
"value": "party"
},
{
"id": 3,
"type": "value",
"value": "2009"
},
{
"id": 1,
"type": "column",
"value": "END"
},
{
"id": 5,
"type": "value",
"value": "%Y"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
"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-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
14,542
|
superstore
|
bird:train.json:2356
|
For how many times has Aimee Bixby ordered the product Xerox 1952?
|
SELECT COUNT(DISTINCT T2.`Order ID`) FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.`Customer Name` = 'Aimee Bixby' AND T3.`Product Name` = 'Xerox 1952'
|
[
"For",
"how",
"many",
"times",
"has",
"Aimee",
"Bixby",
"ordered",
"the",
"product",
"Xerox",
"1952",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "central_superstore"
},
{
"id": 5,
"type": "column",
"value": "Customer Name"
},
{
"id": 7,
"type": "column",
"value": "Product Name"
},
{
"id": 6,
"type": "value",
"value": "Aimee Bixby"
},
{
"id": 9,
"type": "column",
"value": "Customer ID"
},
{
"id": 4,
"type": "column",
"value": "Product ID"
},
{
"id": 8,
"type": "value",
"value": "Xerox 1952"
},
{
"id": 1,
"type": "column",
"value": "Order ID"
},
{
"id": 0,
"type": "table",
"value": "product"
},
{
"id": 2,
"type": "table",
"value": "people"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
5,
6
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
10,
11
]
},
{
"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",
"I-VALUE",
"B-COLUMN",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O"
] |
14,543
|
insurance_fnol
|
spider:train_spider.json:932
|
What are the open and close dates of all the policies used by the customer who have "Diana" in part of their names?
|
SELECT t2.date_opened , t2.date_closed FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name LIKE "%Diana%"
|
[
"What",
"are",
"the",
"open",
"and",
"close",
"dates",
"of",
"all",
"the",
"policies",
"used",
"by",
"the",
"customer",
"who",
"have",
"\"",
"Diana",
"\"",
"in",
"part",
"of",
"their",
"names",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "customers_policies"
},
{
"id": 4,
"type": "column",
"value": "customer_name"
},
{
"id": 0,
"type": "column",
"value": "date_opened"
},
{
"id": 1,
"type": "column",
"value": "date_closed"
},
{
"id": 6,
"type": "column",
"value": "customer_id"
},
{
"id": 2,
"type": "table",
"value": "customers"
},
{
"id": 5,
"type": "column",
"value": "%Diana%"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
18
]
},
{
"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",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,544
|
region_building
|
bird:test.json:349
|
Show the completed years shared by buildings with more than 20 stories and buildings with less than 15 stories.
|
SELECT Completed_Year FROM building WHERE Number_of_Stories > 20 INTERSECT SELECT Completed_Year FROM building WHERE Number_of_Stories < 15
|
[
"Show",
"the",
"completed",
"years",
"shared",
"by",
"buildings",
"with",
"more",
"than",
"20",
"stories",
"and",
"buildings",
"with",
"less",
"than",
"15",
"stories",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "number_of_stories"
},
{
"id": 1,
"type": "column",
"value": "completed_year"
},
{
"id": 0,
"type": "table",
"value": "building"
},
{
"id": 3,
"type": "value",
"value": "20"
},
{
"id": 4,
"type": "value",
"value": "15"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
18
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"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",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
14,545
|
movie_2
|
bird:test.json:1812
|
How many movies had a 'G' rating?
|
SELECT count(*) FROM movies WHERE rating = 'G'
|
[
"How",
"many",
"movies",
"had",
"a",
"'",
"G",
"'",
"rating",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "movies"
},
{
"id": 1,
"type": "column",
"value": "rating"
},
{
"id": 2,
"type": "value",
"value": "G"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
14,546
|
driving_school
|
spider:train_spider.json:6688
|
Which city does has most number of customers?
|
SELECT T2.city FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id GROUP BY T2.city ORDER BY count(*) DESC LIMIT 1;
|
[
"Which",
"city",
"does",
"has",
"most",
"number",
"of",
"customers",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "customer_address_id"
},
{
"id": 4,
"type": "column",
"value": "address_id"
},
{
"id": 1,
"type": "table",
"value": "customers"
},
{
"id": 2,
"type": "table",
"value": "addresses"
},
{
"id": 0,
"type": "column",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"O",
"O",
"B-TABLE",
"O"
] |
14,547
|
local_govt_mdm
|
spider:train_spider.json:2650
|
Wat is the tax source system code and master customer id of the taxes related to each parking fine id?
|
SELECT T1.source_system_code , T1.master_customer_id , T2.council_tax_id FROM CMI_Cross_References AS T1 JOIN Parking_Fines AS T2 ON T1.cmi_cross_ref_id = T2.cmi_cross_ref_id
|
[
"Wat",
"is",
"the",
"tax",
"source",
"system",
"code",
"and",
"master",
"customer",
"i",
"d",
"of",
"the",
"taxes",
"related",
"to",
"each",
"parking",
"fine",
"i",
"d",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "cmi_cross_references"
},
{
"id": 0,
"type": "column",
"value": "source_system_code"
},
{
"id": 1,
"type": "column",
"value": "master_customer_id"
},
{
"id": 5,
"type": "column",
"value": "cmi_cross_ref_id"
},
{
"id": 2,
"type": "column",
"value": "council_tax_id"
},
{
"id": 4,
"type": "table",
"value": "parking_fines"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 1,
"token_idxs": [
8,
9,
10,
11
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
18,
19
]
},
{
"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-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O"
] |
14,548
|
car_retails
|
bird:train.json:1560
|
Please calculate the total value of Motorcycles orders.
|
SELECT SUM(T1.quantityOrdered * T1.priceEach) FROM orderdetails AS T1 INNER JOIN products AS T2 ON T1.productCode = T2.productCode WHERE T2.productLine = 'Motorcycles'
|
[
"Please",
"calculate",
"the",
"total",
"value",
"of",
"Motorcycles",
"orders",
"."
] |
[
{
"id": 5,
"type": "column",
"value": "quantityordered"
},
{
"id": 0,
"type": "table",
"value": "orderdetails"
},
{
"id": 2,
"type": "column",
"value": "productline"
},
{
"id": 3,
"type": "value",
"value": "Motorcycles"
},
{
"id": 4,
"type": "column",
"value": "productcode"
},
{
"id": 6,
"type": "column",
"value": "priceeach"
},
{
"id": 1,
"type": "table",
"value": "products"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"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": []
},
{
"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-VALUE",
"B-TABLE",
"O"
] |
14,549
|
chinook_1
|
spider:train_spider.json:825
|
Which artist has the most albums?
|
SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId = T2.ArtistId GROUP BY T2.Name ORDER BY COUNT(*) DESC LIMIT 1
|
[
"Which",
"artist",
"has",
"the",
"most",
"albums",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "artistid"
},
{
"id": 2,
"type": "table",
"value": "artist"
},
{
"id": 1,
"type": "table",
"value": "album"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,550
|
formula_1
|
bird:dev.json:898
|
How old is the youngest Japanese driver? What is his name?
|
SELECT STRFTIME('%Y', CURRENT_TIMESTAMP) - STRFTIME('%Y', dob), forename , surname FROM drivers WHERE nationality = 'Japanese' ORDER BY dob DESC LIMIT 1
|
[
"How",
"old",
"is",
"the",
"youngest",
"Japanese",
"driver",
"?",
"What",
"is",
"his",
"name",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "nationality"
},
{
"id": 1,
"type": "column",
"value": "forename"
},
{
"id": 4,
"type": "value",
"value": "Japanese"
},
{
"id": 0,
"type": "table",
"value": "drivers"
},
{
"id": 2,
"type": "column",
"value": "surname"
},
{
"id": 5,
"type": "column",
"value": "dob"
},
{
"id": 6,
"type": "value",
"value": "%Y"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,551
|
works_cycles
|
bird:train.json:7179
|
What are the product assembly ID that come with unit measure code EA and BOM level of 2, at the same time have per assembly quantity of more than 10?
|
SELECT ProductAssemblyID FROM BillOfMaterials WHERE UnitMeasureCode = 'EA' AND BOMLevel = 2 AND PerAssemblyQty > 10
|
[
"What",
"are",
"the",
"product",
"assembly",
"ID",
"that",
"come",
"with",
"unit",
"measure",
"code",
"EA",
"and",
"BOM",
"level",
"of",
"2",
",",
"at",
"the",
"same",
"time",
"have",
"per",
"assembly",
"quantity",
"of",
"more",
"than",
"10",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "productassemblyid"
},
{
"id": 0,
"type": "table",
"value": "billofmaterials"
},
{
"id": 2,
"type": "column",
"value": "unitmeasurecode"
},
{
"id": 6,
"type": "column",
"value": "perassemblyqty"
},
{
"id": 4,
"type": "column",
"value": "bomlevel"
},
{
"id": 3,
"type": "value",
"value": "EA"
},
{
"id": 7,
"type": "value",
"value": "10"
},
{
"id": 5,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
9,
10,
11
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
14,
15
]
},
{
"entity_id": 5,
"token_idxs": [
17
]
},
{
"entity_id": 6,
"token_idxs": [
24,
25
]
},
{
"entity_id": 7,
"token_idxs": [
30
]
},
{
"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",
"I-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,552
|
tracking_grants_for_research
|
spider:train_spider.json:4387
|
What details are there on the research staff? List the result in ascending alphabetical order.
|
SELECT staff_details FROM Research_Staff ORDER BY staff_details ASC
|
[
"What",
"details",
"are",
"there",
"on",
"the",
"research",
"staff",
"?",
"List",
"the",
"result",
"in",
"ascending",
"alphabetical",
"order",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "research_staff"
},
{
"id": 1,
"type": "column",
"value": "staff_details"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6,
7
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,553
|
tracking_grants_for_research
|
spider:train_spider.json:4351
|
For each project id, how many staff does it have? List them in increasing order.
|
SELECT T1.project_id , count(*) FROM Project_Staff AS T1 JOIN Projects AS T2 ON T1.project_id = T2.project_id GROUP BY T1.project_id ORDER BY count(*) ASC
|
[
"For",
"each",
"project",
"i",
"d",
",",
"how",
"many",
"staff",
"does",
"it",
"have",
"?",
"List",
"them",
"in",
"increasing",
"order",
"."
] |
[
{
"id": 1,
"type": "table",
"value": "project_staff"
},
{
"id": 0,
"type": "column",
"value": "project_id"
},
{
"id": 2,
"type": "table",
"value": "projects"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"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",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,554
|
entrepreneur
|
spider:train_spider.json:2296
|
What are the names of people who are not entrepreneurs?
|
SELECT Name FROM people WHERE People_ID NOT IN (SELECT People_ID FROM entrepreneur)
|
[
"What",
"are",
"the",
"names",
"of",
"people",
"who",
"are",
"not",
"entrepreneurs",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "entrepreneur"
},
{
"id": 2,
"type": "column",
"value": "people_id"
},
{
"id": 0,
"type": "table",
"value": "people"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,555
|
public_review_platform
|
bird:train.json:4109
|
In which categories does the only business located in the city of Arcadia appear?
|
SELECT T1.category_name FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T3.city = 'Arcadia'
|
[
"In",
"which",
"categories",
"does",
"the",
"only",
"business",
"located",
"in",
"the",
"city",
"of",
"Arcadia",
"appear",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "business_categories"
},
{
"id": 0,
"type": "column",
"value": "category_name"
},
{
"id": 6,
"type": "column",
"value": "business_id"
},
{
"id": 7,
"type": "column",
"value": "category_id"
},
{
"id": 4,
"type": "table",
"value": "categories"
},
{
"id": 1,
"type": "table",
"value": "business"
},
{
"id": 3,
"type": "value",
"value": "Arcadia"
},
{
"id": 2,
"type": "column",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
14,556
|
movies_4
|
bird:train.json:514
|
Work out the difference in revenues made between the English and Latin movies.
|
SELECT SUM(CASE WHEN T3.language_name = 'English' THEN T1.revenue ELSE 0 END) - SUM(CASE WHEN T3.language_name = 'Latin' THEN T1.revenue ELSE 0 END) AS DIFFERENCE 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
|
[
"Work",
"out",
"the",
"difference",
"in",
"revenues",
"made",
"between",
"the",
"English",
"and",
"Latin",
"movies",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "movie_languages"
},
{
"id": 7,
"type": "column",
"value": "language_name"
},
{
"id": 3,
"type": "column",
"value": "language_id"
},
{
"id": 0,
"type": "table",
"value": "language"
},
{
"id": 4,
"type": "column",
"value": "movie_id"
},
{
"id": 6,
"type": "column",
"value": "revenue"
},
{
"id": 8,
"type": "value",
"value": "English"
},
{
"id": 1,
"type": "table",
"value": "movie"
},
{
"id": 9,
"type": "value",
"value": "Latin"
},
{
"id": 5,
"type": "value",
"value": "0"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"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": [
5
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
9
]
},
{
"entity_id": 9,
"token_idxs": [
11
]
},
{
"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",
"O",
"B-VALUE",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
14,557
|
cs_semester
|
bird:train.json:894
|
How many students got an A in Applied Deep Learning?
|
SELECT COUNT(T2.student_id) FROM course AS T1 INNER JOIN registration AS T2 ON T1.course_id = T2.course_id WHERE T2.grade = 'A' AND T1.name = 'Applied Deep Learning '
|
[
"How",
"many",
"students",
"got",
"an",
"A",
"in",
"Applied",
"Deep",
"Learning",
"?"
] |
[
{
"id": 7,
"type": "value",
"value": "Applied Deep Learning "
},
{
"id": 1,
"type": "table",
"value": "registration"
},
{
"id": 2,
"type": "column",
"value": "student_id"
},
{
"id": 3,
"type": "column",
"value": "course_id"
},
{
"id": 0,
"type": "table",
"value": "course"
},
{
"id": 4,
"type": "column",
"value": "grade"
},
{
"id": 6,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "value",
"value": "A"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
5
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
7,
8,
9
]
},
{
"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",
"B-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.