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16.1k
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2,684
|
planet_1
|
bird:test.json:1915
|
List package number of packages shipped in 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";
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"."
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"type": "column",
"value": "accountnumber"
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"value": "shipmentid"
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"value": "shipment"
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"value": "shipment"
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"type": "table",
"value": "client"
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"id": 12,
"type": "column",
"value": "sender"
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"id": 5,
"type": "column",
"value": "name"
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2,685
|
match_season
|
spider:train_spider.json:1106
|
What are the names of countries that have both players with position forward and players with position defender?
|
SELECT T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id = T2.Country WHERE T2.Position = "Forward" INTERSECT SELECT T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id = T2.Country WHERE T2.Position = "Defender"
|
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"value": "Forward"
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{
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"type": "column",
"value": "country"
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"O",
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"O",
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"O"
] |
2,686
|
synthea
|
bird:train.json:1528
|
Calculate the percentage of male patients with viral sinusitis condition.
|
SELECT CAST(SUM(CASE WHEN T1.gender = 'M' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.patient) FROM patients AS T1 INNER JOIN conditions AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'Viral sinusitis (disorder)'
|
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"condition",
"."
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"id": 3,
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"value": "Viral sinusitis (disorder)"
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"value": "conditions"
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{
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"value": "patients"
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"value": "gender"
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"value": "100"
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{
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"type": "value",
"value": "1"
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{
"id": 9,
"type": "value",
"value": "M"
}
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"O",
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"O",
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"I-VALUE",
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"O"
] |
2,687
|
ice_hockey_draft
|
bird:train.json:6956
|
What is the birthplace of Aaron Gagnon?
|
SELECT birthplace FROM PlayerInfo WHERE PlayerName = 'Aaron Gagnon'
|
[
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{
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{
"id": 2,
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[
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
2,688
|
sakila_1
|
spider:train_spider.json:2936
|
How many addresses are in the district of California?
|
SELECT count(*) FROM address WHERE district = 'California'
|
[
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"California",
"?"
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{
"id": 2,
"type": "value",
"value": "California"
},
{
"id": 1,
"type": "column",
"value": "district"
},
{
"id": 0,
"type": "table",
"value": "address"
}
] |
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"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
2,689
|
cs_semester
|
bird:train.json:965
|
Among research postgraduate students, give the name of the course with the student satisfaction value of 1.
|
SELECT T3.name FROM student AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T2.sat = 1 AND T1.type = 'RPG'
|
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"."
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{
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"value": "student"
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{
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"type": "value",
"value": "RPG"
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{
"id": 6,
"type": "value",
"value": "1"
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"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
2,690
|
food_inspection
|
bird:train.json:8852
|
In businesses with an owner address 500 California St, 2nd Floor of Silicon Valley, list the type of inspection of the business with the highest score.
|
SELECT T1.type FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T2.owner_address = '500 California St, 2nd Floor' AND T2.owner_city = 'SAN FRANCISCO' ORDER BY T1.score DESC LIMIT 1
|
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"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
2,691
|
car_retails
|
bird:train.json:1662
|
List out sale rep that has sold 1969 Harley Davidson Ultimate Chopper. List out their names and quantity sold throughout the year.
|
SELECT t5.firstName, t5.lastName, SUM(t2.quantityOrdered) FROM products AS t1 INNER JOIN orderdetails AS t2 ON t1.productCode = t2.productCode INNER JOIN orders AS t3 ON t2.orderNumber = t3.orderNumber INNER JOIN customers AS t4 ON t3.customerNumber = t4.customerNumber INNER JOIN employees AS t5 ON t4.salesRepEmployeeNumber = t5.employeeNumber WHERE t1.productName = '1969 Harley Davidson Ultimate Chopper' GROUP BY t5.lastName, t5.firstName
|
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"id": 7,
"type": "column",
"value": "salesrepemployeenumber"
},
{
"id": 5,
"type": "column",
"value": "quantityordered"
},
{
"id": 8,
"type": "column",
"value": "employeenumber"
},
{
"id": 10,
"type": "column",
"value": "customernumber"
},
{
"id": 12,
"type": "table",
"value": "orderdetails"
},
{
"id": 3,
"type": "column",
"value": "productname"
},
{
"id": 13,
"type": "column",
"value": "ordernumber"
},
{
"id": 14,
"type": "column",
"value": "productcode"
},
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 2,
"type": "table",
"value": "employees"
},
{
"id": 6,
"type": "table",
"value": "customers"
},
{
"id": 0,
"type": "column",
"value": "lastname"
},
{
"id": 11,
"type": "table",
"value": "products"
},
{
"id": 9,
"type": "table",
"value": "orders"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7,
8,
9,
10,
11
]
},
{
"entity_id": 5,
"token_idxs": [
18,
19
]
},
{
"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-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
2,692
|
movies_4
|
bird:train.json:455
|
List the person IDs of the second film editors in Movie No. 12.
|
SELECT person_id FROM movie_crew WHERE movie_id = 12 AND job = 'Second Film Editor'
|
[
"List",
"the",
"person",
"IDs",
"of",
"the",
"second",
"film",
"editors",
"in",
"Movie",
"No",
".",
"12",
"."
] |
[
{
"id": 5,
"type": "value",
"value": "Second Film Editor"
},
{
"id": 0,
"type": "table",
"value": "movie_crew"
},
{
"id": 1,
"type": "column",
"value": "person_id"
},
{
"id": 2,
"type": "column",
"value": "movie_id"
},
{
"id": 4,
"type": "column",
"value": "job"
},
{
"id": 3,
"type": "value",
"value": "12"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6,
7,
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",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
2,693
|
works_cycles
|
bird:train.json:7365
|
What is the difference in percentage between the product descriptions written in Arabic and Thai?
|
SELECT CAST(SUM(CASE WHEN T1.Name = 'Arabic' THEN 1 ELSE 0 END) AS REAL) * 100 / SUM(CASE WHEN T1.Name = 'Thai' THEN 1 ELSE 0 END) FROM Culture AS T1 INNER JOIN ProductModelProductDescriptionCulture AS T2 ON T1.CultureID = T2.CultureID
|
[
"What",
"is",
"the",
"difference",
"in",
"percentage",
"between",
"the",
"product",
"descriptions",
"written",
"in",
"Arabic",
"and",
"Thai",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "productmodelproductdescriptionculture"
},
{
"id": 2,
"type": "column",
"value": "cultureid"
},
{
"id": 0,
"type": "table",
"value": "culture"
},
{
"id": 8,
"type": "value",
"value": "Arabic"
},
{
"id": 6,
"type": "column",
"value": "name"
},
{
"id": 7,
"type": "value",
"value": "Thai"
},
{
"id": 3,
"type": "value",
"value": "100"
},
{
"id": 4,
"type": "value",
"value": "0"
},
{
"id": 5,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7,
8,
9
]
},
{
"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": [
14
]
},
{
"entity_id": 8,
"token_idxs": [
12
]
},
{
"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",
"I-TABLE",
"I-TABLE",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
2,694
|
california_schools
|
bird:dev.json:17
|
Rank schools by their average score in Writing where the score is greater than 499, showing their charter numbers.
|
SELECT CharterNum, AvgScrWrite, RANK() OVER (ORDER BY AvgScrWrite DESC) AS WritingScoreRank FROM schools AS T1 INNER JOIN satscores AS T2 ON T1.CDSCode = T2.cds WHERE T2.AvgScrWrite > 499 AND CharterNum is not null
|
[
"Rank",
"schools",
"by",
"their",
"average",
"score",
"in",
"Writing",
"where",
"the",
"score",
"is",
"greater",
"than",
"499",
",",
"showing",
"their",
"charter",
"numbers",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "avgscrwrite"
},
{
"id": 0,
"type": "column",
"value": "charternum"
},
{
"id": 3,
"type": "table",
"value": "satscores"
},
{
"id": 2,
"type": "table",
"value": "schools"
},
{
"id": 4,
"type": "column",
"value": "cdscode"
},
{
"id": 5,
"type": "column",
"value": "cds"
},
{
"id": 6,
"type": "value",
"value": "499"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
18
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
14
]
},
{
"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",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
2,695
|
soccer_2
|
spider:train_spider.json:5002
|
Find the name and hours of the students whose tryout decision is yes.
|
SELECT T1.pName , T1.HS FROM player AS T1 JOIN tryout AS T2 ON T1.pID = T2.pID WHERE T2.decision = 'yes'
|
[
"Find",
"the",
"name",
"and",
"hours",
"of",
"the",
"students",
"whose",
"tryout",
"decision",
"is",
"yes",
"."
] |
[
{
"id": 4,
"type": "column",
"value": "decision"
},
{
"id": 2,
"type": "table",
"value": "player"
},
{
"id": 3,
"type": "table",
"value": "tryout"
},
{
"id": 0,
"type": "column",
"value": "pname"
},
{
"id": 5,
"type": "value",
"value": "yes"
},
{
"id": 6,
"type": "column",
"value": "pid"
},
{
"id": 1,
"type": "column",
"value": "hs"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": [
12
]
},
{
"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",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
2,696
|
thrombosis_prediction
|
bird:dev.json:1192
|
List all patients who were followed up at the outpatient clinic who underwent a laboratory test in October 1991 and had a total blood bilirubin level within the normal range.
|
SELECT DISTINCT T1.ID FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T1.Admission = '-' AND T2.`T-BIL` < 2.0 AND T2.Date LIKE '1991-10-%'
|
[
"List",
"all",
"patients",
"who",
"were",
"followed",
"up",
"at",
"the",
"outpatient",
"clinic",
"who",
"underwent",
"a",
"laboratory",
"test",
"in",
"October",
"1991",
"and",
"had",
"a",
"total",
"blood",
"bilirubin",
"level",
"within",
"the",
"normal",
"range",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "laboratory"
},
{
"id": 3,
"type": "column",
"value": "admission"
},
{
"id": 8,
"type": "value",
"value": "1991-10-%"
},
{
"id": 1,
"type": "table",
"value": "patient"
},
{
"id": 5,
"type": "column",
"value": "T-BIL"
},
{
"id": 7,
"type": "column",
"value": "date"
},
{
"id": 6,
"type": "value",
"value": "2.0"
},
{
"id": 0,
"type": "column",
"value": "id"
},
{
"id": 4,
"type": "value",
"value": "-"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
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": [
7
]
},
{
"entity_id": 8,
"token_idxs": [
18
]
},
{
"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-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,698
|
club_1
|
spider:train_spider.json:4250
|
How many clubs are there?
|
SELECT count(*) FROM club
|
[
"How",
"many",
"clubs",
"are",
"there",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "club"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
2,699
|
cre_Doc_and_collections
|
bird:test.json:708
|
What are the different owners of documents that are related to ones owned by Braeden?
|
SELECT DISTINCT OWNER FROM Document_Subset_Members AS T1 JOIN Document_Objects AS T2 ON T1.Related_Document_Object_ID = T2.Document_Object_ID WHERE T2.Owner = 'Braeden';
|
[
"What",
"are",
"the",
"different",
"owners",
"of",
"documents",
"that",
"are",
"related",
"to",
"ones",
"owned",
"by",
"Braeden",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "related_document_object_id"
},
{
"id": 1,
"type": "table",
"value": "document_subset_members"
},
{
"id": 5,
"type": "column",
"value": "document_object_id"
},
{
"id": 2,
"type": "table",
"value": "document_objects"
},
{
"id": 3,
"type": "value",
"value": "Braeden"
},
{
"id": 0,
"type": "column",
"value": "owner"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"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",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
2,700
|
culture_company
|
spider:train_spider.json:6973
|
Return the publisher that has published the most books.
|
SELECT publisher FROM book_club GROUP BY publisher ORDER BY count(*) DESC LIMIT 1
|
[
"Return",
"the",
"publisher",
"that",
"has",
"published",
"the",
"most",
"books",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "book_club"
},
{
"id": 1,
"type": "column",
"value": "publisher"
}
] |
[
{
"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": []
},
{
"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",
"O",
"O"
] |
2,701
|
county_public_safety
|
spider:train_spider.json:2546
|
List the name of the county with the largest population.
|
SELECT Name FROM county_public_safety ORDER BY Population DESC LIMIT 1
|
[
"List",
"the",
"name",
"of",
"the",
"county",
"with",
"the",
"largest",
"population",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "county_public_safety"
},
{
"id": 2,
"type": "column",
"value": "population"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"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",
"O",
"B-COLUMN",
"O"
] |
2,702
|
car_road_race
|
bird:test.json:1354
|
Find the names of drivers who were in both "James Hinchcliffe" and "Carl Skerlong" pole positions before.
|
SELECT T1.Driver_Name FROM driver AS T1 JOIN race AS T2 ON T1.Driver_ID = T2.Driver_ID WHERE Pole_Position = "Carl Skerlong" INTERSECT SELECT T1.Driver_Name FROM driver AS T1 JOIN race AS T2 ON T1.Driver_ID = T2.Driver_ID WHERE Pole_Position = "James Hinchcliffe"
|
[
"Find",
"the",
"names",
"of",
"drivers",
"who",
"were",
"in",
"both",
"\"",
"James",
"Hinchcliffe",
"\"",
"and",
"\"",
"Carl",
"Skerlong",
"\"",
"pole",
"positions",
"before",
"."
] |
[
{
"id": 5,
"type": "column",
"value": "James Hinchcliffe"
},
{
"id": 3,
"type": "column",
"value": "pole_position"
},
{
"id": 4,
"type": "column",
"value": "Carl Skerlong"
},
{
"id": 0,
"type": "column",
"value": "driver_name"
},
{
"id": 6,
"type": "column",
"value": "driver_id"
},
{
"id": 1,
"type": "table",
"value": "driver"
},
{
"id": 2,
"type": "table",
"value": "race"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
18,
19
]
},
{
"entity_id": 4,
"token_idxs": [
15,
16
]
},
{
"entity_id": 5,
"token_idxs": [
10,
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",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
2,703
|
network_2
|
spider:train_spider.json:4474
|
Find the name, age, and job title of persons who are friends with Alice for the longest years.
|
SELECT T1.name , T1.age , T1.job FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name = T2.name WHERE T2.friend = 'Alice' AND T2.year = (SELECT max(YEAR) FROM PersonFriend WHERE friend = 'Alice')
|
[
"Find",
"the",
"name",
",",
"age",
",",
"and",
"job",
"title",
"of",
"persons",
"who",
"are",
"friends",
"with",
"Alice",
"for",
"the",
"longest",
"years",
"."
] |
[
{
"id": 4,
"type": "table",
"value": "personfriend"
},
{
"id": 3,
"type": "table",
"value": "person"
},
{
"id": 5,
"type": "column",
"value": "friend"
},
{
"id": 6,
"type": "value",
"value": "Alice"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 7,
"type": "column",
"value": "year"
},
{
"id": 1,
"type": "column",
"value": "age"
},
{
"id": 2,
"type": "column",
"value": "job"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
11,
12
]
},
{
"entity_id": 5,
"token_idxs": [
13
]
},
{
"entity_id": 6,
"token_idxs": [
15
]
},
{
"entity_id": 7,
"token_idxs": [
19
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
2,706
|
culture_company
|
spider:train_spider.json:6976
|
List categories that have at least two books after year 1989.
|
SELECT category FROM book_club WHERE YEAR > 1989 GROUP BY category HAVING count(*) >= 2
|
[
"List",
"categories",
"that",
"have",
"at",
"least",
"two",
"books",
"after",
"year",
"1989",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "book_club"
},
{
"id": 1,
"type": "column",
"value": "category"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "1989"
},
{
"id": 4,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
2,707
|
warehouse_1
|
bird:test.json:1755
|
Select the codes of all warehouses that are above capacity.
|
SELECT T2.code FROM boxes AS T1 JOIN Warehouses AS T2 ON T1.warehouse = T2.code GROUP BY T2.code HAVING count(*) > T2.capacity
|
[
"Select",
"the",
"codes",
"of",
"all",
"warehouses",
"that",
"are",
"above",
"capacity",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "warehouses"
},
{
"id": 4,
"type": "column",
"value": "warehouse"
},
{
"id": 3,
"type": "column",
"value": "capacity"
},
{
"id": 1,
"type": "table",
"value": "boxes"
},
{
"id": 0,
"type": "column",
"value": "code"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
2,708
|
csu_1
|
spider:train_spider.json:2382
|
How many faculty lines are there in the university that conferred the least number of degrees in year 2001?
|
SELECT T2.faculty FROM campuses AS T1 JOIN faculty AS T2 ON T1.id = t2.campus JOIN degrees AS T3 ON T1.id = t3.campus AND t2.year = t3.year WHERE t2.year = 2001 ORDER BY t3.degrees LIMIT 1
|
[
"How",
"many",
"faculty",
"lines",
"are",
"there",
"in",
"the",
"university",
"that",
"conferred",
"the",
"least",
"number",
"of",
"degrees",
"in",
"year",
"2001",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "campuses"
},
{
"id": 0,
"type": "column",
"value": "faculty"
},
{
"id": 1,
"type": "table",
"value": "degrees"
},
{
"id": 4,
"type": "column",
"value": "degrees"
},
{
"id": 6,
"type": "table",
"value": "faculty"
},
{
"id": 8,
"type": "column",
"value": "campus"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "2001"
},
{
"id": 7,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": [
18
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
2
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
2,709
|
government_shift
|
bird:test.json:362
|
Which services were used by the customer with details "Hardy Kutch"? Give me the service details.
|
SELECT t3.service_details FROM customers AS t1 JOIN customers_and_services AS t2 ON t1.customer_id = t2.customer_id JOIN services AS t3 ON t2.service_id = t3.service_id WHERE t1.customer_details = "Hardy Kutch"
|
[
"Which",
"services",
"were",
"used",
"by",
"the",
"customer",
"with",
"details",
"\"",
"Hardy",
"Kutch",
"\"",
"?",
"Give",
"me",
"the",
"service",
"details",
"."
] |
[
{
"id": 5,
"type": "table",
"value": "customers_and_services"
},
{
"id": 2,
"type": "column",
"value": "customer_details"
},
{
"id": 0,
"type": "column",
"value": "service_details"
},
{
"id": 3,
"type": "column",
"value": "Hardy Kutch"
},
{
"id": 7,
"type": "column",
"value": "customer_id"
},
{
"id": 6,
"type": "column",
"value": "service_id"
},
{
"id": 4,
"type": "table",
"value": "customers"
},
{
"id": 1,
"type": "table",
"value": "services"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
18
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
17
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
2,710
|
advertising_agencies
|
bird:test.json:2097
|
Show all invoice status codes and details and the corresponding client id and details and agency id and details.
|
SELECT T1.invoice_status , T1.invoice_details , T2.client_id , T2.client_details , T3.agency_id , T3.agency_details FROM Invoices AS T1 JOIN Clients AS T2 ON T1.client_id = T2.client_id JOIN Agencies AS T3 ON T2.agency_id = T3.agency_id
|
[
"Show",
"all",
"invoice",
"status",
"codes",
"and",
"details",
"and",
"the",
"corresponding",
"client",
"i",
"d",
"and",
"details",
"and",
"agency",
"i",
"d",
"and",
"details",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "invoice_details"
},
{
"id": 0,
"type": "column",
"value": "invoice_status"
},
{
"id": 3,
"type": "column",
"value": "client_details"
},
{
"id": 5,
"type": "column",
"value": "agency_details"
},
{
"id": 2,
"type": "column",
"value": "client_id"
},
{
"id": 4,
"type": "column",
"value": "agency_id"
},
{
"id": 6,
"type": "table",
"value": "agencies"
},
{
"id": 7,
"type": "table",
"value": "invoices"
},
{
"id": 8,
"type": "table",
"value": "clients"
}
] |
[
{
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3
]
},
{
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},
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11,
12
]
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{
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{
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{
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"token_idxs": []
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{
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"token_idxs": []
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{
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"token_idxs": []
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"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
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{
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O"
] |
2,711
|
book_1
|
bird:test.json:567
|
List all book titles which have the lowest sale price .
|
select title from book order by saleprice asc limit 1
|
[
"List",
"all",
"book",
"titles",
"which",
"have",
"the",
"lowest",
"sale",
"price",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "saleprice"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "table",
"value": "book"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
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8,
9
]
},
{
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"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
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},
{
"entity_id": 6,
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},
{
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{
"entity_id": 8,
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{
"entity_id": 9,
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},
{
"entity_id": 13,
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},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
2,712
|
film_rank
|
spider:train_spider.json:4150
|
Find the titles and studios of the films that are produced by some film studios that contained the word "Universal".
|
SELECT title , Studio FROM film WHERE Studio LIKE "%Universal%"
|
[
"Find",
"the",
"titles",
"and",
"studios",
"of",
"the",
"films",
"that",
"are",
"produced",
"by",
"some",
"film",
"studios",
"that",
"contained",
"the",
"word",
"\"",
"Universal",
"\"",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "%Universal%"
},
{
"id": 2,
"type": "column",
"value": "studio"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "table",
"value": "film"
}
] |
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{
"entity_id": 0,
"token_idxs": [
13
]
},
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"entity_id": 1,
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2
]
},
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"entity_id": 2,
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4
]
},
{
"entity_id": 3,
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20
]
},
{
"entity_id": 4,
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{
"entity_id": 5,
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},
{
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},
{
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},
{
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{
"entity_id": 9,
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{
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},
{
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"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
2,713
|
activity_1
|
spider:train_spider.json:6798
|
What are the first names of the professors who do not play Canoeing or Kayaking as activities?
|
SELECT lname FROM faculty WHERE rank = 'Professor' EXCEPT SELECT DISTINCT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID = T2.facID JOIN activity AS T3 ON T2.actid = T2.actid WHERE T3.activity_name = 'Canoeing' OR T3.activity_name = 'Kayaking'
|
[
"What",
"are",
"the",
"first",
"names",
"of",
"the",
"professors",
"who",
"do",
"not",
"play",
"Canoeing",
"or",
"Kayaking",
"as",
"activities",
"?"
] |
[
{
"id": 5,
"type": "table",
"value": "faculty_participates_in"
},
{
"id": 7,
"type": "column",
"value": "activity_name"
},
{
"id": 3,
"type": "value",
"value": "Professor"
},
{
"id": 4,
"type": "table",
"value": "activity"
},
{
"id": 8,
"type": "value",
"value": "Canoeing"
},
{
"id": 9,
"type": "value",
"value": "Kayaking"
},
{
"id": 0,
"type": "table",
"value": "faculty"
},
{
"id": 1,
"type": "column",
"value": "lname"
},
{
"id": 6,
"type": "column",
"value": "actid"
},
{
"id": 10,
"type": "column",
"value": "facid"
},
{
"id": 2,
"type": "column",
"value": "rank"
}
] |
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{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
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4
]
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
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]
},
{
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{
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},
{
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]
},
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14
]
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},
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"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-TABLE",
"O"
] |
2,714
|
authors
|
bird:train.json:3646
|
What is the short name for the journal that published the paper "A Case of Unilateral Ashy Dermatosis"?
|
SELECT T2.ShortName FROM Paper AS T1 INNER JOIN Journal AS T2 ON T1.JournalId = T2.Id WHERE T1.Title = 'A Case of Unilateral Ashy Dermatosis'
|
[
"What",
"is",
"the",
"short",
"name",
"for",
"the",
"journal",
"that",
"published",
"the",
"paper",
"\"",
"A",
"Case",
"of",
"Unilateral",
"Ashy",
"Dermatosis",
"\"",
"?"
] |
[
{
"id": 4,
"type": "value",
"value": "A Case of Unilateral Ashy Dermatosis"
},
{
"id": 0,
"type": "column",
"value": "shortname"
},
{
"id": 5,
"type": "column",
"value": "journalid"
},
{
"id": 2,
"type": "table",
"value": "journal"
},
{
"id": 1,
"type": "table",
"value": "paper"
},
{
"id": 3,
"type": "column",
"value": "title"
},
{
"id": 6,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
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11
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13,
14,
15,
16,
17,
18
]
},
{
"entity_id": 5,
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},
{
"entity_id": 6,
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},
{
"entity_id": 7,
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
2,715
|
codebase_community
|
bird:dev.json:559
|
Indicate the creation date of the maximum number of votes.
|
SELECT CreationDate FROM votes GROUP BY CreationDate ORDER BY COUNT(Id) DESC LIMIT 1
|
[
"Indicate",
"the",
"creation",
"date",
"of",
"the",
"maximum",
"number",
"of",
"votes",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "creationdate"
},
{
"id": 0,
"type": "table",
"value": "votes"
},
{
"id": 2,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"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",
"B-TABLE",
"O"
] |
2,716
|
movie_1
|
spider:train_spider.json:2448
|
What is the total number of ratings that has more than 3 stars?
|
SELECT count(*) FROM Rating WHERE stars > 3
|
[
"What",
"is",
"the",
"total",
"number",
"of",
"ratings",
"that",
"has",
"more",
"than",
"3",
"stars",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "rating"
},
{
"id": 1,
"type": "column",
"value": "stars"
},
{
"id": 2,
"type": "value",
"value": "3"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
2,717
|
toxicology
|
bird:dev.json:283
|
Identify whether the chemical compound that contains Calcium is carcinogenic.
|
SELECT T2.label AS flag_carcinogenic FROM atom AS T1 INNER JOIN molecule AS T2 ON T1.molecule_id = T2.molecule_id WHERE T1.element = 'ca'
|
[
"Identify",
"whether",
"the",
"chemical",
"compound",
"that",
"contains",
"Calcium",
"is",
"carcinogenic",
"."
] |
[
{
"id": 5,
"type": "column",
"value": "molecule_id"
},
{
"id": 2,
"type": "table",
"value": "molecule"
},
{
"id": 3,
"type": "column",
"value": "element"
},
{
"id": 0,
"type": "column",
"value": "label"
},
{
"id": 1,
"type": "table",
"value": "atom"
},
{
"id": 4,
"type": "value",
"value": "ca"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
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},
{
"entity_id": 2,
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},
{
"entity_id": 3,
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},
{
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},
{
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},
{
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"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
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},
{
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"token_idxs": []
},
{
"entity_id": 12,
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},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,718
|
book_publishing_company
|
bird:train.json:173
|
Provide a list of titles together with its publisher name for all publishers located in the USA.
|
SELECT T1.title, T2.pub_name FROM titles AS T1 INNER JOIN publishers AS T2 ON T1.pub_id = T2.pub_id WHERE T2.country = 'USA'
|
[
"Provide",
"a",
"list",
"of",
"titles",
"together",
"with",
"its",
"publisher",
"name",
"for",
"all",
"publishers",
"located",
"in",
"the",
"USA",
"."
] |
[
{
"id": 3,
"type": "table",
"value": "publishers"
},
{
"id": 1,
"type": "column",
"value": "pub_name"
},
{
"id": 4,
"type": "column",
"value": "country"
},
{
"id": 2,
"type": "table",
"value": "titles"
},
{
"id": 6,
"type": "column",
"value": "pub_id"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 5,
"type": "value",
"value": "USA"
}
] |
[
{
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"token_idxs": [
4
]
},
{
"entity_id": 1,
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9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
2,719
|
farm
|
spider:train_spider.json:44
|
Please show the different statuses, ordered by the number of cities that have each.
|
SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) ASC
|
[
"Please",
"show",
"the",
"different",
"statuses",
",",
"ordered",
"by",
"the",
"number",
"of",
"cities",
"that",
"have",
"each",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "status"
},
{
"id": 0,
"type": "table",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
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},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
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"token_idxs": []
},
{
"entity_id": 10,
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},
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},
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},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
2,720
|
retail_complains
|
bird:train.json:335
|
List all the server of the phone complaints with a late response from the company.
|
SELECT DISTINCT T2.server FROM events AS T1 INNER JOIN callcenterlogs AS T2 ON T1.`Complaint ID` = T2.`Complaint ID` WHERE T1.`Submitted via` = 'Phone' AND T1.`Timely response?` = 'No'
|
[
"List",
"all",
"the",
"server",
"of",
"the",
"phone",
"complaints",
"with",
"a",
"late",
"response",
"from",
"the",
"company",
"."
] |
[
{
"id": 6,
"type": "column",
"value": "Timely response?"
},
{
"id": 2,
"type": "table",
"value": "callcenterlogs"
},
{
"id": 4,
"type": "column",
"value": "Submitted via"
},
{
"id": 3,
"type": "column",
"value": "Complaint ID"
},
{
"id": 0,
"type": "column",
"value": "server"
},
{
"id": 1,
"type": "table",
"value": "events"
},
{
"id": 5,
"type": "value",
"value": "Phone"
},
{
"id": 7,
"type": "value",
"value": "No"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"entity_id": 6,
"token_idxs": [
10,
11
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
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"entity_id": 11,
"token_idxs": []
},
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"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
2,721
|
address
|
bird:train.json:5151
|
Give the name of the country and state of the city with elevation of 1039.
|
SELECT DISTINCT T1.name, T2.state FROM state AS T1 INNER JOIN country AS T2 ON T1.abbreviation = T2.state INNER JOIN zip_data AS T3 ON T2.zip_code = T3.zip_code WHERE T3.elevation = 1039
|
[
"Give",
"the",
"name",
"of",
"the",
"country",
"and",
"state",
"of",
"the",
"city",
"with",
"elevation",
"of",
"1039",
"."
] |
[
{
"id": 8,
"type": "column",
"value": "abbreviation"
},
{
"id": 3,
"type": "column",
"value": "elevation"
},
{
"id": 2,
"type": "table",
"value": "zip_data"
},
{
"id": 7,
"type": "column",
"value": "zip_code"
},
{
"id": 6,
"type": "table",
"value": "country"
},
{
"id": 1,
"type": "column",
"value": "state"
},
{
"id": 5,
"type": "table",
"value": "state"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "value",
"value": "1039"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
"entity_id": 5,
"token_idxs": [
7
]
},
{
"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": []
},
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},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
2,722
|
chicago_crime
|
bird:train.json:8623
|
How many incidents of domestic violence occurred in an abandoned building in 2018?
|
SELECT SUM(CASE WHEN location_description = 'ABANDONED BUILDING' THEN 1 ELSE 0 END) FROM Crime WHERE date LIKE '%2018%' AND domestic = 'TRUE'
|
[
"How",
"many",
"incidents",
"of",
"domestic",
"violence",
"occurred",
"in",
"an",
"abandoned",
"building",
"in",
"2018",
"?"
] |
[
{
"id": 7,
"type": "column",
"value": "location_description"
},
{
"id": 8,
"type": "value",
"value": "ABANDONED BUILDING"
},
{
"id": 3,
"type": "column",
"value": "domestic"
},
{
"id": 2,
"type": "value",
"value": "%2018%"
},
{
"id": 0,
"type": "table",
"value": "crime"
},
{
"id": 1,
"type": "column",
"value": "date"
},
{
"id": 4,
"type": "value",
"value": "TRUE"
},
{
"id": 5,
"type": "value",
"value": "0"
},
{
"id": 6,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
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12
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
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9,
10
]
},
{
"entity_id": 9,
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},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O"
] |
2,723
|
software_company
|
bird:train.json:8522
|
Of the first 60,000 customers who sent a true response to the incentive mailing sent by the marketing department, how many of them are from a place with more than 30,000 inhabitants?
|
SELECT COUNT(T1.ID) FROM Customers AS T1 INNER JOIN Mailings1_2 AS T2 ON T1.ID = T2.REFID INNER JOIN Demog AS T3 ON T1.GEOID = T3.GEOID WHERE T3.INHABITANTS_K > 30 AND T2.RESPONSE = 'true'
|
[
"Of",
"the",
"first",
"60,000",
"customers",
"who",
"sent",
"a",
"true",
"response",
"to",
"the",
"incentive",
"mailing",
"sent",
"by",
"the",
"marketing",
"department",
",",
"how",
"many",
"of",
"them",
"are",
"from",
"a",
"place",
"with",
"more",
"than",
"30,000",
"inhabitants",
"?"
] |
[
{
"id": 5,
"type": "column",
"value": "inhabitants_k"
},
{
"id": 3,
"type": "table",
"value": "mailings1_2"
},
{
"id": 2,
"type": "table",
"value": "customers"
},
{
"id": 7,
"type": "column",
"value": "response"
},
{
"id": 0,
"type": "table",
"value": "demog"
},
{
"id": 4,
"type": "column",
"value": "geoid"
},
{
"id": 9,
"type": "column",
"value": "refid"
},
{
"id": 8,
"type": "value",
"value": "true"
},
{
"id": 1,
"type": "column",
"value": "id"
},
{
"id": 6,
"type": "value",
"value": "30"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
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32
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},
{
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9
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8
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},
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},
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},
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"token_idxs": []
},
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"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
2,725
|
synthea
|
bird:train.json:1526
|
Indicate the start date of patient Walter Bahringer's care plan.
|
SELECT DISTINCT T2.start FROM patients AS T1 INNER JOIN careplans AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Walter' AND T1.last = 'Bahringer'
|
[
"Indicate",
"the",
"start",
"date",
"of",
"patient",
"Walter",
"Bahringer",
"'s",
"care",
"plan",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "careplans"
},
{
"id": 7,
"type": "value",
"value": "Bahringer"
},
{
"id": 1,
"type": "table",
"value": "patients"
},
{
"id": 3,
"type": "column",
"value": "patient"
},
{
"id": 5,
"type": "value",
"value": "Walter"
},
{
"id": 0,
"type": "column",
"value": "start"
},
{
"id": 4,
"type": "column",
"value": "first"
},
{
"id": 6,
"type": "column",
"value": "last"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
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"token_idxs": []
},
{
"entity_id": 7,
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7
]
},
{
"entity_id": 8,
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
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},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"B-VALUE",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
2,726
|
retail_complains
|
bird:train.json:399
|
In reviews for the Eagle National Bank product, how many of the 5 star reviews where from Nashville, Tennessee?
|
SELECT COUNT(T2.Stars) FROM district AS T1 INNER JOIN reviews AS T2 ON T1.district_id = T2.district_id WHERE T1.city = 'Nashville' AND T1.state_abbrev = 'TN' AND T2.Product = 'Eagle National Mortgage' AND T2.Stars = 5
|
[
"In",
"reviews",
"for",
"the",
"Eagle",
"National",
"Bank",
"product",
",",
"how",
"many",
"of",
"the",
"5",
"star",
"reviews",
"where",
"from",
"Nashville",
",",
"Tennessee",
"?"
] |
[
{
"id": 9,
"type": "value",
"value": "Eagle National Mortgage"
},
{
"id": 6,
"type": "column",
"value": "state_abbrev"
},
{
"id": 3,
"type": "column",
"value": "district_id"
},
{
"id": 5,
"type": "value",
"value": "Nashville"
},
{
"id": 0,
"type": "table",
"value": "district"
},
{
"id": 1,
"type": "table",
"value": "reviews"
},
{
"id": 8,
"type": "column",
"value": "product"
},
{
"id": 2,
"type": "column",
"value": "stars"
},
{
"id": 4,
"type": "column",
"value": "city"
},
{
"id": 7,
"type": "value",
"value": "TN"
},
{
"id": 10,
"type": "value",
"value": "5"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
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},
{
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18
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},
{
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},
{
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},
{
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7
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},
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4,
5
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13
]
},
{
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"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
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},
{
"entity_id": 14,
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
2,727
|
scientist_1
|
spider:train_spider.json:6473
|
How many different scientists are assigned to any project?
|
SELECT count(DISTINCT scientist) FROM assignedto
|
[
"How",
"many",
"different",
"scientists",
"are",
"assigned",
"to",
"any",
"project",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "assignedto"
},
{
"id": 1,
"type": "column",
"value": "scientist"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5,
6
]
},
{
"entity_id": 1,
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3
]
},
{
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},
{
"entity_id": 3,
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},
{
"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",
"I-TABLE",
"O",
"O",
"O"
] |
2,728
|
sales
|
bird:train.json:5388
|
Calculate the total price for products from id 400 to 500.
|
SELECT SUM(T1.Price * T2.quantity) FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ProductID BETWEEN 400 AND 500
|
[
"Calculate",
"the",
"total",
"price",
"for",
"products",
"from",
"i",
"d",
"400",
"to",
"500",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "productid"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 6,
"type": "column",
"value": "quantity"
},
{
"id": 1,
"type": "table",
"value": "sales"
},
{
"id": 5,
"type": "column",
"value": "price"
},
{
"id": 3,
"type": "value",
"value": "400"
},
{
"id": 4,
"type": "value",
"value": "500"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"entity_id": 5,
"token_idxs": [
3
]
},
{
"entity_id": 6,
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},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
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"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
2,729
|
movie
|
bird:train.json:749
|
Who played the character named "Chanice Kobolowski"?
|
SELECT T2.Name FROM characters AS T1 INNER JOIN actor AS T2 ON T1.ActorID = T2.ActorID WHERE T1.`Character Name` = 'Chanice Kobolowski'
|
[
"Who",
"played",
"the",
"character",
"named",
"\"",
"Chanice",
"Kobolowski",
"\"",
"?"
] |
[
{
"id": 4,
"type": "value",
"value": "Chanice Kobolowski"
},
{
"id": 3,
"type": "column",
"value": "Character Name"
},
{
"id": 1,
"type": "table",
"value": "characters"
},
{
"id": 5,
"type": "column",
"value": "actorid"
},
{
"id": 2,
"type": "table",
"value": "actor"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6,
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-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
2,730
|
codebase_comments
|
bird:train.json:611
|
What is the repository number for the solution of method "SCore.Poisson.ngtIndex"?
|
SELECT T1.RepoId FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.Name = 'SCore.Poisson.ngtIndex'
|
[
"What",
"is",
"the",
"repository",
"number",
"for",
"the",
"solution",
"of",
"method",
"\"",
"SCore",
".",
"Poisson.ngtIndex",
"\"",
"?"
] |
[
{
"id": 4,
"type": "value",
"value": "SCore.Poisson.ngtIndex"
},
{
"id": 6,
"type": "column",
"value": "solutionid"
},
{
"id": 1,
"type": "table",
"value": "solution"
},
{
"id": 0,
"type": "column",
"value": "repoid"
},
{
"id": 2,
"type": "table",
"value": "method"
},
{
"id": 3,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
11,
12,
13
]
},
{
"entity_id": 5,
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},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
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"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
2,731
|
news_report
|
spider:train_spider.json:2817
|
what are the average and maximum attendances of all events?
|
SELECT avg(Event_Attendance) , max(Event_Attendance) FROM event
|
[
"what",
"are",
"the",
"average",
"and",
"maximum",
"attendances",
"of",
"all",
"events",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "event_attendance"
},
{
"id": 0,
"type": "table",
"value": "event"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"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",
"O",
"O",
"B-TABLE",
"O"
] |
2,732
|
manufactory_1
|
spider:train_spider.json:5309
|
What are the names and revenues of the companies with the highest revenues in each headquarter city?
|
SELECT name , max(revenue) , Headquarter FROM manufacturers GROUP BY Headquarter
|
[
"What",
"are",
"the",
"names",
"and",
"revenues",
"of",
"the",
"companies",
"with",
"the",
"highest",
"revenues",
"in",
"each",
"headquarter",
"city",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "manufacturers"
},
{
"id": 1,
"type": "column",
"value": "headquarter"
},
{
"id": 3,
"type": "column",
"value": "revenue"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
2,733
|
california_schools
|
bird:dev.json:79
|
Between San Diego and Santa Barbara, which county offers the most number of schools that does not offer physical building? Indicate the amount.
|
SELECT County, COUNT(Virtual) FROM schools WHERE (County = 'San Diego' OR County = 'Santa Barbara') AND Virtual = 'F' GROUP BY County ORDER BY COUNT(Virtual) DESC LIMIT 1
|
[
"Between",
"San",
"Diego",
"and",
"Santa",
"Barbara",
",",
"which",
"county",
"offers",
"the",
"most",
"number",
"of",
"schools",
"that",
"does",
"not",
"offer",
"physical",
"building",
"?",
"Indicate",
"the",
"amount",
"."
] |
[
{
"id": 5,
"type": "value",
"value": "Santa Barbara"
},
{
"id": 4,
"type": "value",
"value": "San Diego"
},
{
"id": 0,
"type": "table",
"value": "schools"
},
{
"id": 2,
"type": "column",
"value": "virtual"
},
{
"id": 1,
"type": "column",
"value": "county"
},
{
"id": 3,
"type": "value",
"value": "F"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
1,
2
]
},
{
"entity_id": 5,
"token_idxs": [
4,
5
]
},
{
"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-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,734
|
county_public_safety
|
spider:train_spider.json:2553
|
What are the white percentages of cities, and the corresponding crime rates of the counties they correspond to?
|
SELECT T1.White , T2.Crime_rate FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID = T2.County_ID
|
[
"What",
"are",
"the",
"white",
"percentages",
"of",
"cities",
",",
"and",
"the",
"corresponding",
"crime",
"rates",
"of",
"the",
"counties",
"they",
"correspond",
"to",
"?"
] |
[
{
"id": 3,
"type": "table",
"value": "county_public_safety"
},
{
"id": 1,
"type": "column",
"value": "crime_rate"
},
{
"id": 4,
"type": "column",
"value": "county_id"
},
{
"id": 0,
"type": "column",
"value": "white"
},
{
"id": 2,
"type": "table",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
11,
12
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"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",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
2,735
|
retails
|
bird:train.json:6844
|
How many customers belong to the household segment in Germany?
|
SELECT COUNT(T1.c_name) FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T1.c_mktsegment = 'HOUSEHOLD' AND T2.n_name = 'GERMANY'
|
[
"How",
"many",
"customers",
"belong",
"to",
"the",
"household",
"segment",
"in",
"Germany",
"?"
] |
[
{
"id": 5,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 3,
"type": "column",
"value": "c_nationkey"
},
{
"id": 4,
"type": "column",
"value": "n_nationkey"
},
{
"id": 6,
"type": "value",
"value": "HOUSEHOLD"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 8,
"type": "value",
"value": "GERMANY"
},
{
"id": 1,
"type": "table",
"value": "nation"
},
{
"id": 2,
"type": "column",
"value": "c_name"
},
{
"id": 7,
"type": "column",
"value": "n_name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
7
]
},
{
"entity_id": 6,
"token_idxs": [
6
]
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": [
9
]
},
{
"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",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
2,736
|
wedding
|
spider:train_spider.json:1647
|
Show all countries and the number of people from each country.
|
SELECT country , count(*) FROM people GROUP BY country
|
[
"Show",
"all",
"countries",
"and",
"the",
"number",
"of",
"people",
"from",
"each",
"country",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 0,
"type": "table",
"value": "people"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"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",
"O",
"B-COLUMN",
"O"
] |
2,737
|
address
|
bird:train.json:5175
|
Provide the zip codes and the congress representatives' names of the postal points which are affiliated with Readers Digest.
|
SELECT T1.zip_code, T3.first_name, T3.last_name FROM zip_data AS T1 INNER JOIN zip_congress AS T2 ON T1.zip_code = T2.zip_code INNER JOIN congress AS T3 ON T2.district = T3.cognress_rep_id WHERE T1.organization = 'Readers Digest'
|
[
"Provide",
"the",
"zip",
"codes",
"and",
"the",
"congress",
"representatives",
"'",
"names",
"of",
"the",
"postal",
"points",
"which",
"are",
"affiliated",
"with",
"Readers",
"Digest",
"."
] |
[
{
"id": 9,
"type": "column",
"value": "cognress_rep_id"
},
{
"id": 5,
"type": "value",
"value": "Readers Digest"
},
{
"id": 4,
"type": "column",
"value": "organization"
},
{
"id": 7,
"type": "table",
"value": "zip_congress"
},
{
"id": 1,
"type": "column",
"value": "first_name"
},
{
"id": 2,
"type": "column",
"value": "last_name"
},
{
"id": 0,
"type": "column",
"value": "zip_code"
},
{
"id": 3,
"type": "table",
"value": "congress"
},
{
"id": 6,
"type": "table",
"value": "zip_data"
},
{
"id": 8,
"type": "column",
"value": "district"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"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": [
18,
19
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
2,738
|
college_2
|
spider:train_spider.json:1478
|
Find courses that ran in Fall 2009 and in Spring 2010.
|
SELECT course_id FROM SECTION WHERE semester = 'Fall' AND YEAR = 2009 INTERSECT SELECT course_id FROM SECTION WHERE semester = 'Spring' AND YEAR = 2010
|
[
"Find",
"courses",
"that",
"ran",
"in",
"Fall",
"2009",
"and",
"in",
"Spring",
"2010",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "course_id"
},
{
"id": 2,
"type": "column",
"value": "semester"
},
{
"id": 0,
"type": "table",
"value": "section"
},
{
"id": 6,
"type": "value",
"value": "Spring"
},
{
"id": 3,
"type": "value",
"value": "Fall"
},
{
"id": 4,
"type": "column",
"value": "year"
},
{
"id": 5,
"type": "value",
"value": "2009"
},
{
"id": 7,
"type": "value",
"value": "2010"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
6
]
},
{
"entity_id": 6,
"token_idxs": [
9
]
},
{
"entity_id": 7,
"token_idxs": [
10
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O"
] |
2,739
|
loan_1
|
spider:train_spider.json:3015
|
What is the average number of bank customers?
|
SELECT avg(no_of_customers) FROM bank
|
[
"What",
"is",
"the",
"average",
"number",
"of",
"bank",
"customers",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "no_of_customers"
},
{
"id": 0,
"type": "table",
"value": "bank"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
2,740
|
cars
|
bird:train.json:3087
|
Which car in the database provides the best crash protection based on its weight? How much is it?
|
SELECT T1.ID, T2.price FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID ORDER BY T1.weight DESC LIMIT 1
|
[
"Which",
"car",
"in",
"the",
"database",
"provides",
"the",
"best",
"crash",
"protection",
"based",
"on",
"its",
"weight",
"?",
"How",
"much",
"is",
"it",
"?"
] |
[
{
"id": 4,
"type": "column",
"value": "weight"
},
{
"id": 1,
"type": "column",
"value": "price"
},
{
"id": 3,
"type": "table",
"value": "price"
},
{
"id": 2,
"type": "table",
"value": "data"
},
{
"id": 0,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
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",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,741
|
formula_1
|
bird:dev.json:1009
|
Please list the time each driver spent at the pit stop during the 2011 Australian Grand Prix.
|
SELECT T1.duration FROM pitStops AS T1 INNER JOIN races AS T2 on T1.raceId = T2.raceId WHERE T2.year = 2011 AND T2.name = 'Australian Grand Prix'
|
[
"Please",
"list",
"the",
"time",
"each",
"driver",
"spent",
"at",
"the",
"pit",
"stop",
"during",
"the",
"2011",
"Australian",
"Grand",
"Prix",
"."
] |
[
{
"id": 7,
"type": "value",
"value": "Australian Grand Prix"
},
{
"id": 0,
"type": "column",
"value": "duration"
},
{
"id": 1,
"type": "table",
"value": "pitstops"
},
{
"id": 3,
"type": "column",
"value": "raceid"
},
{
"id": 2,
"type": "table",
"value": "races"
},
{
"id": 4,
"type": "column",
"value": "year"
},
{
"id": 5,
"type": "value",
"value": "2011"
},
{
"id": 6,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
9,
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
13
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
14,
15,
16
]
},
{
"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",
"I-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
2,742
|
student_club
|
bird:dev.json:1391
|
What is the ratio between students majored in finance and physics?
|
SELECT SUM(CASE WHEN major_name = 'Finance' THEN 1 ELSE 0 END) / SUM(CASE WHEN major_name = 'Physics' THEN 1 ELSE 0 END) AS ratio FROM major
|
[
"What",
"is",
"the",
"ratio",
"between",
"students",
"majored",
"in",
"finance",
"and",
"physics",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "major_name"
},
{
"id": 4,
"type": "value",
"value": "Finance"
},
{
"id": 5,
"type": "value",
"value": "Physics"
},
{
"id": 0,
"type": "table",
"value": "major"
},
{
"id": 1,
"type": "value",
"value": "0"
},
{
"id": 2,
"type": "value",
"value": "1"
}
] |
[
{
"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": [
8
]
},
{
"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",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
2,743
|
movie_2
|
bird:test.json:1845
|
Find the name of the movie theaters that are playing the movies whose rating is ‘G’.
|
SELECT T2.name FROM movies AS T1 JOIN movietheaters AS T2 ON T1.code = T2.movie WHERE T1.rating = 'G'
|
[
"Find",
"the",
"name",
"of",
"the",
"movie",
"theaters",
"that",
"are",
"playing",
"the",
"movies",
"whose",
"rating",
"is",
"‘",
"G",
"’",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "movietheaters"
},
{
"id": 1,
"type": "table",
"value": "movies"
},
{
"id": 3,
"type": "column",
"value": "rating"
},
{
"id": 6,
"type": "column",
"value": "movie"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "column",
"value": "code"
},
{
"id": 4,
"type": "value",
"value": "G"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
2,744
|
donor
|
bird:train.json:3187
|
Which projects created by teachers with Doctor Degree where the project have more than 300 students involved. List down the title of the project.
|
SELECT T1.title FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.teacher_prefix LIKE 'Dr.' AND T2.students_reached > 300
|
[
"Which",
"projects",
"created",
"by",
"teachers",
"with",
"Doctor",
"Degree",
"where",
"the",
"project",
"have",
"more",
"than",
"300",
"students",
"involved",
".",
"List",
"down",
"the",
"title",
"of",
"the",
"project",
"."
] |
[
{
"id": 6,
"type": "column",
"value": "students_reached"
},
{
"id": 4,
"type": "column",
"value": "teacher_prefix"
},
{
"id": 3,
"type": "column",
"value": "projectid"
},
{
"id": 2,
"type": "table",
"value": "projects"
},
{
"id": 1,
"type": "table",
"value": "essays"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 5,
"type": "value",
"value": "Dr."
},
{
"id": 7,
"type": "value",
"value": "300"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
21
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
15
]
},
{
"entity_id": 7,
"token_idxs": [
14
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
2,745
|
movie_3
|
bird:train.json:9416
|
What is the address of Mary Smith?
|
SELECT T1.address FROM address AS T1 INNER JOIN customer AS T2 ON T1.address_id = T2.address_id WHERE T2.first_name = 'MARY' AND T2.last_name = 'SMITH'
|
[
"What",
"is",
"the",
"address",
"of",
"Mary",
"Smith",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "address_id"
},
{
"id": 4,
"type": "column",
"value": "first_name"
},
{
"id": 6,
"type": "column",
"value": "last_name"
},
{
"id": 2,
"type": "table",
"value": "customer"
},
{
"id": 0,
"type": "column",
"value": "address"
},
{
"id": 1,
"type": "table",
"value": "address"
},
{
"id": 7,
"type": "value",
"value": "SMITH"
},
{
"id": 5,
"type": "value",
"value": "MARY"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
5
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
6
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-VALUE",
"O"
] |
2,746
|
soccer_2016
|
bird:train.json:2030
|
Count the matches that were won by wickets in all season.
|
SELECT COUNT(T1.Match_Id) FROM Match AS T1 INNER JOIN Win_By AS T2 ON T1.Win_Type = T2.Win_Id WHERE T2.Win_type = 'wickets'
|
[
"Count",
"the",
"matches",
"that",
"were",
"won",
"by",
"wickets",
"in",
"all",
"season",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "win_type"
},
{
"id": 4,
"type": "column",
"value": "match_id"
},
{
"id": 3,
"type": "value",
"value": "wickets"
},
{
"id": 1,
"type": "table",
"value": "win_by"
},
{
"id": 5,
"type": "column",
"value": "win_id"
},
{
"id": 0,
"type": "table",
"value": "match"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
2,747
|
hockey
|
bird:train.json:7654
|
How many people were in the Hall of Fame's Builder category?
|
SELECT COUNT(hofID) FROM HOF WHERE category = 'Builder'
|
[
"How",
"many",
"people",
"were",
"in",
"the",
"Hall",
"of",
"Fame",
"'s",
"Builder",
"category",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "category"
},
{
"id": 2,
"type": "value",
"value": "Builder"
},
{
"id": 3,
"type": "column",
"value": "hofid"
},
{
"id": 0,
"type": "table",
"value": "hof"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
2,748
|
retail_complains
|
bird:train.json:245
|
What is the detailed product of the complaint filed by Diesel Galloway on 2014/7/3?
|
SELECT T2.`Sub-product` FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.first = 'Diesel' AND T1.last = 'Galloway' AND T2.`Date received` = '2014-07-03'
|
[
"What",
"is",
"the",
"detailed",
"product",
"of",
"the",
"complaint",
"filed",
"by",
"Diesel",
"Galloway",
"on",
"2014/7/3",
"?"
] |
[
{
"id": 8,
"type": "column",
"value": "Date received"
},
{
"id": 0,
"type": "column",
"value": "Sub-product"
},
{
"id": 9,
"type": "value",
"value": "2014-07-03"
},
{
"id": 3,
"type": "column",
"value": "client_id"
},
{
"id": 7,
"type": "value",
"value": "Galloway"
},
{
"id": 1,
"type": "table",
"value": "client"
},
{
"id": 2,
"type": "table",
"value": "events"
},
{
"id": 5,
"type": "value",
"value": "Diesel"
},
{
"id": 4,
"type": "column",
"value": "first"
},
{
"id": 6,
"type": "column",
"value": "last"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
11
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": [
13
]
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
2,749
|
address_1
|
bird:test.json:799
|
Which country has least number of students?
|
SELECT T1.country FROM City AS T1 JOIN Student AS T2 ON T1.city_code = T2.city_code GROUP BY T1.country ORDER BY count(*) LIMIT 1
|
[
"Which",
"country",
"has",
"least",
"number",
"of",
"students",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "city_code"
},
{
"id": 0,
"type": "column",
"value": "country"
},
{
"id": 2,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "table",
"value": "city"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"O"
] |
2,750
|
cinema
|
spider:train_spider.json:1944
|
Which locations have 2 or more cinemas with capacity over 300?
|
SELECT LOCATION FROM cinema WHERE capacity > 300 GROUP BY LOCATION HAVING count(*) >= 2
|
[
"Which",
"locations",
"have",
"2",
"or",
"more",
"cinemas",
"with",
"capacity",
"over",
"300",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "location"
},
{
"id": 2,
"type": "column",
"value": "capacity"
},
{
"id": 0,
"type": "table",
"value": "cinema"
},
{
"id": 3,
"type": "value",
"value": "300"
},
{
"id": 4,
"type": "value",
"value": "2"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"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",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
2,751
|
warehouse_1
|
bird:test.json:1710
|
Find the contents that are stored in both Chicago and New York.
|
SELECT T1.contents FROM boxes AS T1 JOIN warehouses AS T2 ON T1.warehouse = T2.code WHERE T2.location = 'Chicago' INTERSECT SELECT T1.contents FROM boxes AS T1 JOIN warehouses AS T2 ON T1.warehouse = T2.code WHERE T2.location = 'New York'
|
[
"Find",
"the",
"contents",
"that",
"are",
"stored",
"in",
"both",
"Chicago",
"and",
"New",
"York",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "warehouses"
},
{
"id": 6,
"type": "column",
"value": "warehouse"
},
{
"id": 0,
"type": "column",
"value": "contents"
},
{
"id": 3,
"type": "column",
"value": "location"
},
{
"id": 5,
"type": "value",
"value": "New York"
},
{
"id": 4,
"type": "value",
"value": "Chicago"
},
{
"id": 1,
"type": "table",
"value": "boxes"
},
{
"id": 7,
"type": "column",
"value": "code"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id": 5,
"token_idxs": [
10,
11
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
2,752
|
soccer_2
|
spider:train_spider.json:4969
|
What is average number of students enrolled in Florida colleges?
|
SELECT avg(enr) FROM College WHERE state = 'FL'
|
[
"What",
"is",
"average",
"number",
"of",
"students",
"enrolled",
"in",
"Florida",
"colleges",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "college"
},
{
"id": 1,
"type": "column",
"value": "state"
},
{
"id": 3,
"type": "column",
"value": "enr"
},
{
"id": 2,
"type": "value",
"value": "FL"
}
] |
[
{
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},
{
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},
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"token_idxs": []
},
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},
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"entity_id": 7,
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},
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},
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"entity_id": 12,
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},
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"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
2,753
|
election
|
spider:train_spider.json:2774
|
Show the name of each county along with the corresponding number of delegates from that county.
|
SELECT T1.County_name , COUNT(*) FROM county AS T1 JOIN election AS T2 ON T1.County_id = T2.District GROUP BY T1.County_id
|
[
"Show",
"the",
"name",
"of",
"each",
"county",
"along",
"with",
"the",
"corresponding",
"number",
"of",
"delegates",
"from",
"that",
"county",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "county_name"
},
{
"id": 0,
"type": "column",
"value": "county_id"
},
{
"id": 3,
"type": "table",
"value": "election"
},
{
"id": 4,
"type": "column",
"value": "district"
},
{
"id": 2,
"type": "table",
"value": "county"
}
] |
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{
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"token_idxs": []
},
{
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},
{
"entity_id": 2,
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5
]
},
{
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},
{
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},
{
"entity_id": 5,
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},
{
"entity_id": 6,
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},
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},
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},
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},
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"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"
] |
2,754
|
gas_company
|
spider:train_spider.json:1997
|
Show the company name and the main industry for all companies whose headquarters are not from USA.
|
SELECT company , main_industry FROM company WHERE headquarters != 'USA'
|
[
"Show",
"the",
"company",
"name",
"and",
"the",
"main",
"industry",
"for",
"all",
"companies",
"whose",
"headquarters",
"are",
"not",
"from",
"USA",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "main_industry"
},
{
"id": 3,
"type": "column",
"value": "headquarters"
},
{
"id": 0,
"type": "table",
"value": "company"
},
{
"id": 1,
"type": "column",
"value": "company"
},
{
"id": 4,
"type": "value",
"value": "USA"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
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2
]
},
{
"entity_id": 2,
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6,
7
]
},
{
"entity_id": 3,
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12
]
},
{
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16
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},
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"entity_id": 5,
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},
{
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},
{
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},
{
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},
{
"entity_id": 9,
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},
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},
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"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
2,755
|
department_store
|
spider:train_spider.json:4784
|
What are the order ids and customer ids for orders that have been Cancelled, sorted by their order dates?
|
SELECT order_id , customer_id FROM customer_orders WHERE order_status_code = "Cancelled" ORDER BY order_date
|
[
"What",
"are",
"the",
"order",
"ids",
"and",
"customer",
"ids",
"for",
"orders",
"that",
"have",
"been",
"Cancelled",
",",
"sorted",
"by",
"their",
"order",
"dates",
"?"
] |
[
{
"id": 3,
"type": "column",
"value": "order_status_code"
},
{
"id": 0,
"type": "table",
"value": "customer_orders"
},
{
"id": 2,
"type": "column",
"value": "customer_id"
},
{
"id": 5,
"type": "column",
"value": "order_date"
},
{
"id": 4,
"type": "column",
"value": "Cancelled"
},
{
"id": 1,
"type": "column",
"value": "order_id"
}
] |
[
{
"entity_id": 0,
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8,
9
]
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6,
7
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{
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},
{
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},
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{
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},
{
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},
{
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},
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},
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},
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"entity_id": 13,
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},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
2,756
|
university
|
bird:train.json:8132
|
What is the location and number of female students in university ID 23 in 2011?
|
SELECT T3.country_name, CAST(T2.num_students * T2.pct_female_students AS REAL) / 100 FROM university AS T1 INNER JOIN university_year AS T2 ON T1.id = T2.university_id INNER JOIN country AS T3 ON T3.id = T1.country_id WHERE T2.year = 2011 AND T1.id = 23
|
[
"What",
"is",
"the",
"location",
"and",
"number",
"of",
"female",
"students",
"in",
"university",
"ID",
"23",
"in",
"2011",
"?"
] |
[
{
"id": 12,
"type": "column",
"value": "pct_female_students"
},
{
"id": 4,
"type": "table",
"value": "university_year"
},
{
"id": 10,
"type": "column",
"value": "university_id"
},
{
"id": 0,
"type": "column",
"value": "country_name"
},
{
"id": 11,
"type": "column",
"value": "num_students"
},
{
"id": 3,
"type": "table",
"value": "university"
},
{
"id": 6,
"type": "column",
"value": "country_id"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 7,
"type": "column",
"value": "year"
},
{
"id": 8,
"type": "value",
"value": "2011"
},
{
"id": 2,
"type": "value",
"value": "100"
},
{
"id": 5,
"type": "column",
"value": "id"
},
{
"id": 9,
"type": "value",
"value": "23"
}
] |
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{
"entity_id": 2,
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12
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8
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7
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{
"entity_id": 13,
"token_idxs": []
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{
"entity_id": 14,
"token_idxs": []
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{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
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"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
2,757
|
architecture
|
spider:train_spider.json:6958
|
What are the distinct name of the mills built by the architects who have also built a bridge longer than 80 meters?
|
SELECT DISTINCT T1.name FROM mill AS T1 JOIN architect AS t2 ON T1.architect_id = T2.id JOIN bridge AS T3 ON T3.architect_id = T2.id WHERE T3.length_meters > 80
|
[
"What",
"are",
"the",
"distinct",
"name",
"of",
"the",
"mills",
"built",
"by",
"the",
"architects",
"who",
"have",
"also",
"built",
"a",
"bridge",
"longer",
"than",
"80",
"meters",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "length_meters"
},
{
"id": 6,
"type": "column",
"value": "architect_id"
},
{
"id": 5,
"type": "table",
"value": "architect"
},
{
"id": 1,
"type": "table",
"value": "bridge"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "table",
"value": "mill"
},
{
"id": 3,
"type": "value",
"value": "80"
},
{
"id": 7,
"type": "column",
"value": "id"
}
] |
[
{
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"token_idxs": [
4
]
},
{
"entity_id": 1,
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]
},
{
"entity_id": 2,
"token_idxs": [
18,
19,
21
]
},
{
"entity_id": 3,
"token_idxs": [
20
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs": [
11
]
},
{
"entity_id": 6,
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},
{
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},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
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},
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"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"B-COLUMN",
"O"
] |
2,758
|
club_1
|
spider:train_spider.json:4262
|
How many members does the club "Tennis Club" has?
|
SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid = t2.clubid JOIN student AS t3 ON t2.stuid = t3.stuid WHERE t1.clubname = "Tennis Club"
|
[
"How",
"many",
"members",
"does",
"the",
"club",
"\"",
"Tennis",
"Club",
"\"",
"has",
"?"
] |
[
{
"id": 4,
"type": "table",
"value": "member_of_club"
},
{
"id": 2,
"type": "column",
"value": "Tennis Club"
},
{
"id": 1,
"type": "column",
"value": "clubname"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 6,
"type": "column",
"value": "clubid"
},
{
"id": 5,
"type": "column",
"value": "stuid"
},
{
"id": 3,
"type": "table",
"value": "club"
}
] |
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{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
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7
]
},
{
"entity_id": 3,
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5
]
},
{
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"token_idxs": [
2,
3,
4
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},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": [
8
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},
{
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},
{
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},
{
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},
{
"entity_id": 10,
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},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"B-TABLE",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O"
] |
2,759
|
college_completion
|
bird:train.json:3736
|
What is the institution's name of american students within the number of degree-seeking students in the cohort that ranges from 1 to 3?
|
SELECT DISTINCT T1.chronname FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T2.grad_cohort BETWEEN 1 AND 3 AND T2.race = 'Ai'
|
[
"What",
"is",
"the",
"institution",
"'s",
"name",
"of",
"american",
"students",
"within",
"the",
"number",
"of",
"degree",
"-",
"seeking",
"students",
"in",
"the",
"cohort",
"that",
"ranges",
"from",
"1",
"to",
"3",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "institution_details"
},
{
"id": 2,
"type": "table",
"value": "institution_grads"
},
{
"id": 4,
"type": "column",
"value": "grad_cohort"
},
{
"id": 0,
"type": "column",
"value": "chronname"
},
{
"id": 3,
"type": "column",
"value": "unitid"
},
{
"id": 7,
"type": "column",
"value": "race"
},
{
"id": 8,
"type": "value",
"value": "Ai"
},
{
"id": 5,
"type": "value",
"value": "1"
},
{
"id": 6,
"type": "value",
"value": "3"
}
] |
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},
{
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3,
4
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},
{
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},
{
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19
]
},
{
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23
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},
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"O",
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"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
2,760
|
college_2
|
spider:train_spider.json:1377
|
Give the name of the department with the lowest budget.
|
SELECT dept_name FROM department ORDER BY budget LIMIT 1
|
[
"Give",
"the",
"name",
"of",
"the",
"department",
"with",
"the",
"lowest",
"budget",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "department"
},
{
"id": 1,
"type": "column",
"value": "dept_name"
},
{
"id": 2,
"type": "column",
"value": "budget"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"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",
"O",
"B-COLUMN",
"O"
] |
2,761
|
movie_3
|
bird:train.json:9246
|
Please list the top ten movies with the most price per day in descending order of price per day.
|
SELECT title FROM film ORDER BY rental_rate / rental_duration DESC LIMIT 10
|
[
"Please",
"list",
"the",
"top",
"ten",
"movies",
"with",
"the",
"most",
"price",
"per",
"day",
"in",
"descending",
"order",
"of",
"price",
"per",
"day",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "rental_duration"
},
{
"id": 2,
"type": "column",
"value": "rental_rate"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "table",
"value": "film"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,762
|
food_inspection
|
bird:train.json:8778
|
What was the type of inspection Tiramisu Kitchen had on 2014/1/14?
|
SELECT T1.type FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.`date` = '2014-01-14' AND T2.name = 'Tiramisu Kitchen'
|
[
"What",
"was",
"the",
"type",
"of",
"inspection",
"Tiramisu",
"Kitchen",
"had",
"on",
"2014/1/14",
"?"
] |
[
{
"id": 7,
"type": "value",
"value": "Tiramisu Kitchen"
},
{
"id": 1,
"type": "table",
"value": "inspections"
},
{
"id": 3,
"type": "column",
"value": "business_id"
},
{
"id": 2,
"type": "table",
"value": "businesses"
},
{
"id": 5,
"type": "value",
"value": "2014-01-14"
},
{
"id": 0,
"type": "column",
"value": "type"
},
{
"id": 4,
"type": "column",
"value": "date"
},
{
"id": 6,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"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": [
10
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": [
6,
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-COLUMN",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"O"
] |
2,763
|
bike_1
|
spider:train_spider.json:152
|
What is the mean longitude for all stations that have never had more than 10 bikes available?
|
SELECT avg(long) FROM station WHERE id NOT IN (SELECT station_id FROM status GROUP BY station_id HAVING max(bikes_available) > 10)
|
[
"What",
"is",
"the",
"mean",
"longitude",
"for",
"all",
"stations",
"that",
"have",
"never",
"had",
"more",
"than",
"10",
"bikes",
"available",
"?"
] |
[
{
"id": 6,
"type": "column",
"value": "bikes_available"
},
{
"id": 4,
"type": "column",
"value": "station_id"
},
{
"id": 0,
"type": "table",
"value": "station"
},
{
"id": 3,
"type": "table",
"value": "status"
},
{
"id": 1,
"type": "column",
"value": "long"
},
{
"id": 2,
"type": "column",
"value": "id"
},
{
"id": 5,
"type": "value",
"value": "10"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
14
]
},
{
"entity_id": 6,
"token_idxs": [
15,
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",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"O"
] |
2,764
|
headphone_store
|
bird:test.json:924
|
Which headphone class contains the most headphones?
|
SELECT CLASS FROM headphone GROUP BY CLASS ORDER BY count(*) DESC LIMIT 1
|
[
"Which",
"headphone",
"class",
"contains",
"the",
"most",
"headphones",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "headphone"
},
{
"id": 1,
"type": "column",
"value": "class"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"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",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
2,765
|
vehicle_driver
|
bird:test.json:185
|
Show all driver names in the alphabetical order.
|
SELECT name FROM driver ORDER BY name
|
[
"Show",
"all",
"driver",
"names",
"in",
"the",
"alphabetical",
"order",
"."
] |
[
{
"id": 0,
"type": "table",
"value": "driver"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"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",
"O",
"O",
"O",
"O"
] |
2,766
|
car_retails
|
bird:train.json:1562
|
How many orders which expected profits greater than 100?
|
SELECT COUNT(T1.productCode) FROM orderdetails AS T1 INNER JOIN products AS T2 ON T1.productCode = T2.productCode WHERE T2.MSRP - T2.buyPrice > 100
|
[
"How",
"many",
"orders",
"which",
"expected",
"profits",
"greater",
"than",
"100",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "orderdetails"
},
{
"id": 3,
"type": "column",
"value": "productcode"
},
{
"id": 1,
"type": "table",
"value": "products"
},
{
"id": 5,
"type": "column",
"value": "buyprice"
},
{
"id": 4,
"type": "column",
"value": "msrp"
},
{
"id": 2,
"type": "value",
"value": "100"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"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",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
2,767
|
book_press
|
bird:test.json:1981
|
Find the name of the publisher whose monthly profit is the highest or the lowest.
|
SELECT name FROM press WHERE Month_Profits_billion = (SELECT min(Month_Profits_billion) FROM press) OR Month_Profits_billion = (SELECT max(Month_Profits_billion) FROM press)
|
[
"Find",
"the",
"name",
"of",
"the",
"publisher",
"whose",
"monthly",
"profit",
"is",
"the",
"highest",
"or",
"the",
"lowest",
"."
] |
[
{
"id": 2,
"type": "column",
"value": "month_profits_billion"
},
{
"id": 0,
"type": "table",
"value": "press"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8,
9
]
},
{
"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",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,768
|
hockey
|
bird:train.json:7752
|
What is the power play chances of New York Rangers in 2009?
|
SELECT CAST(PPG AS REAL) / PPC FROM Teams WHERE year = 2009 AND name = 'New York Rangers'
|
[
"What",
"is",
"the",
"power",
"play",
"chances",
"of",
"New",
"York",
"Rangers",
"in",
"2009",
"?"
] |
[
{
"id": 5,
"type": "value",
"value": "New York Rangers"
},
{
"id": 0,
"type": "table",
"value": "teams"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "2009"
},
{
"id": 4,
"type": "column",
"value": "name"
},
{
"id": 1,
"type": "column",
"value": "ppc"
},
{
"id": 6,
"type": "column",
"value": "ppg"
}
] |
[
{
"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": [
7,
8,
9
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O"
] |
2,769
|
store_product
|
spider:train_spider.json:4913
|
What is the total number of residents for the districts with the 3 largest areas?
|
SELECT sum(city_population) FROM district ORDER BY city_area DESC LIMIT 3
|
[
"What",
"is",
"the",
"total",
"number",
"of",
"residents",
"for",
"the",
"districts",
"with",
"the",
"3",
"largest",
"areas",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "city_population"
},
{
"id": 1,
"type": "column",
"value": "city_area"
},
{
"id": 0,
"type": "table",
"value": "district"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
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},
{
"entity_id": 8,
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},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
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},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
2,770
|
shipping
|
bird:train.json:5623
|
What is the average shipment weight carried by the oldest Mack?
|
SELECT AVG(T2.weight) FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id WHERE T1.make = 'Mack'
|
[
"What",
"is",
"the",
"average",
"shipment",
"weight",
"carried",
"by",
"the",
"oldest",
"Mack",
"?"
] |
[
{
"id": 1,
"type": "table",
"value": "shipment"
},
{
"id": 5,
"type": "column",
"value": "truck_id"
},
{
"id": 4,
"type": "column",
"value": "weight"
},
{
"id": 0,
"type": "table",
"value": "truck"
},
{
"id": 2,
"type": "column",
"value": "make"
},
{
"id": 3,
"type": "value",
"value": "Mack"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
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},
{
"entity_id": 9,
"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",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
2,771
|
architecture
|
spider:train_spider.json:6957
|
Which of the mill names contains the french word 'Moulin'?
|
SELECT name FROM mill WHERE name LIKE '%Moulin%'
|
[
"Which",
"of",
"the",
"mill",
"names",
"contains",
"the",
"french",
"word",
"'",
"Moulin",
"'",
"?"
] |
[
{
"id": 2,
"type": "value",
"value": "%Moulin%"
},
{
"id": 0,
"type": "table",
"value": "mill"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
2,772
|
software_company
|
bird:train.json:8550
|
Find and list the id and geographic ID of the elderly customers with an education level below 3.
|
SELECT ID, GEOID FROM Customers WHERE EDUCATIONNUM < 3 AND age > 65
|
[
"Find",
"and",
"list",
"the",
"i",
"d",
"and",
"geographic",
"ID",
"of",
"the",
"elderly",
"customers",
"with",
"an",
"education",
"level",
"below",
"3",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "educationnum"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 2,
"type": "column",
"value": "geoid"
},
{
"id": 5,
"type": "column",
"value": "age"
},
{
"id": 1,
"type": "column",
"value": "id"
},
{
"id": 6,
"type": "value",
"value": "65"
},
{
"id": 4,
"type": "value",
"value": "3"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": [
18
]
},
{
"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,
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},
{
"entity_id": 11,
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},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
2,773
|
european_football_2
|
bird:dev.json:1091
|
How many matches were held in the Belgium Jupiler League in April, 2009?
|
SELECT COUNT(t2.id) FROM League AS t1 INNER JOIN Match AS t2 ON t1.id = t2.league_id WHERE t1.name = 'Belgium Jupiler League' AND SUBSTR(t2.`date`, 1, 7) = '2009-04'
|
[
"How",
"many",
"matches",
"were",
"held",
"in",
"the",
"Belgium",
"Jupiler",
"League",
"in",
"April",
",",
"2009",
"?"
] |
[
{
"id": 5,
"type": "value",
"value": "Belgium Jupiler League"
},
{
"id": 3,
"type": "column",
"value": "league_id"
},
{
"id": 6,
"type": "value",
"value": "2009-04"
},
{
"id": 0,
"type": "table",
"value": "league"
},
{
"id": 1,
"type": "table",
"value": "match"
},
{
"id": 4,
"type": "column",
"value": "name"
},
{
"id": 7,
"type": "column",
"value": "date"
},
{
"id": 2,
"type": "column",
"value": "id"
},
{
"id": 8,
"type": "value",
"value": "1"
},
{
"id": 9,
"type": "value",
"value": "7"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
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},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
7,
8
]
},
{
"entity_id": 6,
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13
]
},
{
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},
{
"entity_id": 8,
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},
{
"entity_id": 9,
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},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
2,774
|
european_football_1
|
bird:train.json:2755
|
Which division had the most draft matches in the 2008 season?
|
SELECT Div FROM matchs WHERE season = 2008 AND FTR = 'D' GROUP BY Div ORDER BY COUNT(FTR) DESC LIMIT 1
|
[
"Which",
"division",
"had",
"the",
"most",
"draft",
"matches",
"in",
"the",
"2008",
"season",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "matchs"
},
{
"id": 2,
"type": "column",
"value": "season"
},
{
"id": 3,
"type": "value",
"value": "2008"
},
{
"id": 1,
"type": "column",
"value": "div"
},
{
"id": 4,
"type": "column",
"value": "ftr"
},
{
"id": 5,
"type": "value",
"value": "D"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"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",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
2,775
|
donor
|
bird:train.json:3212
|
Please list the titles of projects by which schools in Abington was donated.
|
SELECT T2.title FROM projects AS T1 INNER JOIN essays AS T2 ON T1.projectid = T2.projectid WHERE T1.school_city LIKE 'Abington'
|
[
"Please",
"list",
"the",
"titles",
"of",
"projects",
"by",
"which",
"schools",
"in",
"Abington",
"was",
"donated",
"."
] |
[
{
"id": 3,
"type": "column",
"value": "school_city"
},
{
"id": 5,
"type": "column",
"value": "projectid"
},
{
"id": 1,
"type": "table",
"value": "projects"
},
{
"id": 4,
"type": "value",
"value": "Abington"
},
{
"id": 2,
"type": "table",
"value": "essays"
},
{
"id": 0,
"type": "column",
"value": "title"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O"
] |
2,776
|
regional_sales
|
bird:train.json:2694
|
How many stores with less need for products, and purchased through a distributor, are located in Washtenaw County?
|
SELECT SUM(CASE WHEN T1.`Order Quantity` = 1 AND T1.`Sales Channel` = 'Distributor' AND T2.County = 'Washtenaw County' THEN 1 ELSE 0 END) FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID
|
[
"How",
"many",
"stores",
"with",
"less",
"need",
"for",
"products",
",",
"and",
"purchased",
"through",
"a",
"distributor",
",",
"are",
"located",
"in",
"Washtenaw",
"County",
"?"
] |
[
{
"id": 10,
"type": "value",
"value": "Washtenaw County"
},
{
"id": 1,
"type": "table",
"value": "Store Locations"
},
{
"id": 6,
"type": "column",
"value": "Order Quantity"
},
{
"id": 7,
"type": "column",
"value": "Sales Channel"
},
{
"id": 0,
"type": "table",
"value": "Sales Orders"
},
{
"id": 8,
"type": "value",
"value": "Distributor"
},
{
"id": 3,
"type": "column",
"value": "_storeid"
},
{
"id": 2,
"type": "column",
"value": "storeid"
},
{
"id": 9,
"type": "column",
"value": "county"
},
{
"id": 4,
"type": "value",
"value": "0"
},
{
"id": 5,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
15,
16,
17
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
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},
{
"entity_id": 4,
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},
{
"entity_id": 5,
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},
{
"entity_id": 6,
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},
{
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},
{
"entity_id": 8,
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13
]
},
{
"entity_id": 9,
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19
]
},
{
"entity_id": 10,
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18
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"B-VALUE",
"B-COLUMN",
"O"
] |
2,777
|
culture_company
|
spider:train_spider.json:6975
|
How many books fall into each category?
|
SELECT category , count(*) FROM book_club GROUP BY category
|
[
"How",
"many",
"books",
"fall",
"into",
"each",
"category",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "book_club"
},
{
"id": 1,
"type": "column",
"value": "category"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
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6
]
},
{
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},
{
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{
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{
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{
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},
{
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},
{
"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"
] |
2,778
|
student_assessment
|
spider:train_spider.json:95
|
List the names of courses in alphabetical order?
|
SELECT course_name FROM courses ORDER BY course_name
|
[
"List",
"the",
"names",
"of",
"courses",
"in",
"alphabetical",
"order",
"?"
] |
[
{
"id": 1,
"type": "column",
"value": "course_name"
},
{
"id": 0,
"type": "table",
"value": "courses"
}
] |
[
{
"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",
"O",
"O",
"O"
] |
2,779
|
activity_1
|
spider:train_spider.json:6724
|
What are the first name, last name, and phone number of all the female faculty members?
|
SELECT Fname , Lname , phone FROM Faculty WHERE Sex = 'F'
|
[
"What",
"are",
"the",
"first",
"name",
",",
"last",
"name",
",",
"and",
"phone",
"number",
"of",
"all",
"the",
"female",
"faculty",
"members",
"?"
] |
[
{
"id": 0,
"type": "table",
"value": "faculty"
},
{
"id": 1,
"type": "column",
"value": "fname"
},
{
"id": 2,
"type": "column",
"value": "lname"
},
{
"id": 3,
"type": "column",
"value": "phone"
},
{
"id": 4,
"type": "column",
"value": "sex"
},
{
"id": 5,
"type": "value",
"value": "F"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
12
]
},
{
"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",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
2,781
|
retails
|
bird:train.json:6904
|
How many customers in the building segments have orders with a total price of no less than 50,000?
|
SELECT COUNT(T2.c_name) FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey WHERE T2.c_mktsegment = 'BUILDING' AND T1.o_totalprice > 50000
|
[
"How",
"many",
"customers",
"in",
"the",
"building",
"segments",
"have",
"orders",
"with",
"a",
"total",
"price",
"of",
"no",
"less",
"than",
"50,000",
"?"
] |
[
{
"id": 5,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 7,
"type": "column",
"value": "o_totalprice"
},
{
"id": 3,
"type": "column",
"value": "o_custkey"
},
{
"id": 4,
"type": "column",
"value": "c_custkey"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 6,
"type": "value",
"value": "BUILDING"
},
{
"id": 0,
"type": "table",
"value": "orders"
},
{
"id": 2,
"type": "column",
"value": "c_name"
},
{
"id": 8,
"type": "value",
"value": "50000"
}
] |
[
{
"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": [
6
]
},
{
"entity_id": 6,
"token_idxs": [
5
]
},
{
"entity_id": 7,
"token_idxs": [
11,
12
]
},
{
"entity_id": 8,
"token_idxs": [
17
]
},
{
"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",
"B-VALUE",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
2,782
|
disney
|
bird:train.json:4721
|
Who was the first ever Disney villain?
|
SELECT villian FROM characters ORDER BY SUBSTR(release_date, LENGTH(release_date) - 1, LENGTH(release_date)) DESC LIMIT 1
|
[
"Who",
"was",
"the",
"first",
"ever",
"Disney",
"villain",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "release_date"
},
{
"id": 0,
"type": "table",
"value": "characters"
},
{
"id": 1,
"type": "column",
"value": "villian"
},
{
"id": 3,
"type": "value",
"value": "1"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"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"
] |
2,783
|
mountain_photos
|
spider:train_spider.json:3725
|
Show the name and prominence of the mountains whose picture is not taken by a lens of brand 'Sigma'.
|
SELECT name , prominence FROM mountain EXCEPT SELECT T1.name , T1.prominence FROM mountain AS T1 JOIN photos AS T2 ON T1.id = T2.mountain_id JOIN camera_lens AS T3 ON T2.camera_lens_id = T3.id WHERE T3.brand = 'Sigma'
|
[
"Show",
"the",
"name",
"and",
"prominence",
"of",
"the",
"mountains",
"whose",
"picture",
"is",
"not",
"taken",
"by",
"a",
"lens",
"of",
"brand",
"'",
"Sigma",
"'",
"."
] |
[
{
"id": 7,
"type": "column",
"value": "camera_lens_id"
},
{
"id": 3,
"type": "table",
"value": "camera_lens"
},
{
"id": 9,
"type": "column",
"value": "mountain_id"
},
{
"id": 2,
"type": "column",
"value": "prominence"
},
{
"id": 0,
"type": "table",
"value": "mountain"
},
{
"id": 6,
"type": "table",
"value": "photos"
},
{
"id": 4,
"type": "column",
"value": "brand"
},
{
"id": 5,
"type": "value",
"value": "Sigma"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 8,
"type": "column",
"value": "id"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
14,
15
]
},
{
"entity_id": 4,
"token_idxs": [
17
]
},
{
"entity_id": 5,
"token_idxs": [
19
]
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
2,784
|
department_store
|
spider:train_spider.json:4732
|
Return the product type, name, and price for products supplied by supplier 3.
|
SELECT T2.product_type_code , T2.product_name , T2.product_price FROM product_suppliers AS T1 JOIN products AS T2 ON T1.product_id = T2.product_id WHERE T1.supplier_id = 3
|
[
"Return",
"the",
"product",
"type",
",",
"name",
",",
"and",
"price",
"for",
"products",
"supplied",
"by",
"supplier",
"3",
"."
] |
[
{
"id": 0,
"type": "column",
"value": "product_type_code"
},
{
"id": 3,
"type": "table",
"value": "product_suppliers"
},
{
"id": 2,
"type": "column",
"value": "product_price"
},
{
"id": 1,
"type": "column",
"value": "product_name"
},
{
"id": 5,
"type": "column",
"value": "supplier_id"
},
{
"id": 7,
"type": "column",
"value": "product_id"
},
{
"id": 4,
"type": "table",
"value": "products"
},
{
"id": 6,
"type": "value",
"value": "3"
}
] |
[
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
"token_idxs": [
13
]
},
{
"entity_id": 6,
"token_idxs": [
14
]
},
{
"entity_id": 7,
"token_idxs": [
2
]
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
2,785
|
customers_card_transactions
|
spider:train_spider.json:671
|
Show other account details for account with name 338.
|
SELECT other_account_details FROM Accounts WHERE account_name = "338"
|
[
"Show",
"other",
"account",
"details",
"for",
"account",
"with",
"name",
"338",
"."
] |
[
{
"id": 1,
"type": "column",
"value": "other_account_details"
},
{
"id": 2,
"type": "column",
"value": "account_name"
},
{
"id": 0,
"type": "table",
"value": "accounts"
},
{
"id": 3,
"type": "column",
"value": "338"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
1,
3
]
},
{
"entity_id": 2,
"token_idxs": [
5,
6,
7
]
},
{
"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",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O"
] |
2,786
|
college_1
|
spider:train_spider.json:3208
|
How many classes exist for each school?
|
SELECT count(*) , T3.school_code FROM CLASS AS T1 JOIN course AS T2 ON T1.crs_code = T2.crs_code JOIN department AS T3 ON T2.dept_code = T3.dept_code GROUP BY T3.school_code
|
[
"How",
"many",
"classes",
"exist",
"for",
"each",
"school",
"?"
] |
[
{
"id": 0,
"type": "column",
"value": "school_code"
},
{
"id": 1,
"type": "table",
"value": "department"
},
{
"id": 4,
"type": "column",
"value": "dept_code"
},
{
"id": 5,
"type": "column",
"value": "crs_code"
},
{
"id": 3,
"type": "table",
"value": "course"
},
{
"id": 2,
"type": "table",
"value": "class"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"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",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
2,787
|
books
|
bird:train.json:5965
|
List the ISBN of the book published in Spanish.
|
SELECT T1.isbn13 FROM book AS T1 INNER JOIN book_language AS T2 ON T1.language_id = T2.language_id WHERE T2.language_name = 'Spanish'
|
[
"List",
"the",
"ISBN",
"of",
"the",
"book",
"published",
"in",
"Spanish",
"."
] |
[
{
"id": 2,
"type": "table",
"value": "book_language"
},
{
"id": 3,
"type": "column",
"value": "language_name"
},
{
"id": 5,
"type": "column",
"value": "language_id"
},
{
"id": 4,
"type": "value",
"value": "Spanish"
},
{
"id": 0,
"type": "column",
"value": "isbn13"
},
{
"id": 1,
"type": "table",
"value": "book"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
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",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
2,788
|
wrestler
|
spider:train_spider.json:1869
|
What are the reigns and days held of all wrestlers?
|
SELECT Reign , Days_held FROM wrestler
|
[
"What",
"are",
"the",
"reigns",
"and",
"days",
"held",
"of",
"all",
"wrestlers",
"?"
] |
[
{
"id": 2,
"type": "column",
"value": "days_held"
},
{
"id": 0,
"type": "table",
"value": "wrestler"
},
{
"id": 1,
"type": "column",
"value": "reign"
}
] |
[
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5,
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id": 6,
"token_idxs": []
},
{
"entity_id": 7,
"token_idxs": []
},
{
"entity_id": 8,
"token_idxs": []
},
{
"entity_id": 9,
"token_idxs": []
},
{
"entity_id": 10,
"token_idxs": []
},
{
"entity_id": 11,
"token_idxs": []
},
{
"entity_id": 12,
"token_idxs": []
},
{
"entity_id": 13,
"token_idxs": []
},
{
"entity_id": 14,
"token_idxs": []
},
{
"entity_id": 15,
"token_idxs": []
},
{
"entity_id": 16,
"token_idxs": []
},
{
"entity_id": 17,
"token_idxs": []
},
{
"entity_id": 18,
"token_idxs": []
},
{
"entity_id": 19,
"token_idxs": []
}
] |
[
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
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