question_id int64 0 16.1k | db_id stringclasses 259
values | dber_id stringlengths 15 29 | question stringlengths 16 325 | SQL stringlengths 18 1.25k | tokens listlengths 4 62 | entities listlengths 0 21 | entity_to_token listlengths 20 20 | dber_tags listlengths 4 62 |
|---|---|---|---|---|---|---|---|---|
10,693 | retails | bird:train.json:6719 | How many countries are there in the region whose comment description is "asymptotes sublate after the r." | SELECT COUNT(T1.n_nationkey) FROM nation AS T1 INNER JOIN region AS T2 ON T1.n_regionkey = T2.r_regionkey WHERE T2.r_comment = 'asymptotes sublate after the r' | [
"How",
"many",
"countries",
"are",
"there",
"in",
"the",
"region",
"whose",
"comment",
"description",
"is",
"\"",
"asymptotes",
"sublate",
"after",
"the",
"r.",
"\""
] | [
{
"id": 3,
"type": "value",
"value": "asymptotes sublate after the r"
},
{
"id": 4,
"type": "column",
"value": "n_nationkey"
},
{
"id": 5,
"type": "column",
"value": "n_regionkey"
},
{
"id": 6,
"type": "column",
"value": "r_regionkey"
},
{
"id": 2,... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
13,
14,
15,
16,
17
]
},
{
"entity_id": 4,
"token_idx... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
10,694 | icfp_1 | spider:train_spider.json:2908 | Find the first names of all the authors who have written a paper with title containing the word "Functional". | SELECT t1.fname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid = t2.authid JOIN papers AS t3 ON t2.paperid = t3.paperid WHERE t3.title LIKE "%Functional%" | [
"Find",
"the",
"first",
"names",
"of",
"all",
"the",
"authors",
"who",
"have",
"written",
"a",
"paper",
"with",
"title",
"containing",
"the",
"word",
"\"",
"Functional",
"\"",
"."
] | [
{
"id": 3,
"type": "column",
"value": "%Functional%"
},
{
"id": 5,
"type": "table",
"value": "authorship"
},
{
"id": 4,
"type": "table",
"value": "authors"
},
{
"id": 6,
"type": "column",
"value": "paperid"
},
{
"id": 1,
"type": "table",
"v... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
19
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"enti... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
10,695 | movies_4 | bird:train.json:506 | Among Warner Bros. Pictures' movies, which title made the highest revenue? | SELECT T3.title FROM production_company AS T1 INNER JOIN movie_company AS T2 ON T1.company_id = T2.company_id INNER JOIN movie AS T3 ON T2.movie_id = T3.movie_id WHERE T1.company_name = 'Warner Bros. Pictures' ORDER BY T3.revenue DESC LIMIT 1 | [
"Among",
"Warner",
"Bros.",
"Pictures",
"'",
"movies",
",",
"which",
"title",
"made",
"the",
"highest",
"revenue",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Warner Bros. Pictures"
},
{
"id": 5,
"type": "table",
"value": "production_company"
},
{
"id": 6,
"type": "table",
"value": "movie_company"
},
{
"id": 2,
"type": "column",
"value": "company_name"
},
{
"id": 8,
... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
1,
2,
3
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
... | [
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,696 | student_club | bird:dev.json:1429 | What is the name of the social event that was attended by the vice president of the Student_Club located at 900 E. Washington St.? | SELECT T2.event_name FROM attendance AS T1 INNER JOIN event AS T2 ON T2.event_id = T1.link_to_event INNER JOIN member AS T3 ON T1.link_to_member = T3.member_id WHERE T3.position = 'Vice President' AND T2.location = '900 E. Washington St.' AND T2.type = 'Social' | [
"What",
"is",
"the",
"name",
"of",
"the",
"social",
"event",
"that",
"was",
"attended",
"by",
"the",
"vice",
"president",
"of",
"the",
"Student_Club",
"located",
"at",
"900",
"E.",
"Washington",
"St.",
"?"
] | [
{
"id": 9,
"type": "value",
"value": "900 E. Washington St."
},
{
"id": 4,
"type": "column",
"value": "link_to_member"
},
{
"id": 7,
"type": "value",
"value": "Vice President"
},
{
"id": 13,
"type": "column",
"value": "link_to_event"
},
{
"id": 0,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
10,697 | program_share | spider:train_spider.json:3731 | Give me a list of all the channel names sorted by the channel rating in descending order. | SELECT name FROM channel ORDER BY rating_in_percent DESC | [
"Give",
"me",
"a",
"list",
"of",
"all",
"the",
"channel",
"names",
"sorted",
"by",
"the",
"channel",
"rating",
"in",
"descending",
"order",
"."
] | [
{
"id": 2,
"type": "column",
"value": "rating_in_percent"
},
{
"id": 0,
"type": "table",
"value": "channel"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
13,
14,
15
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O"
] |
10,698 | cs_semester | bird:train.json:946 | Name the students with above-average capability. | SELECT T1.f_name, T1.l_name FROM student AS T1 INNER JOIN RA AS T2 ON T1.student_id = T2.student_id WHERE T2.capability > ( SELECT AVG(capability) FROM RA ) | [
"Name",
"the",
"students",
"with",
"above",
"-",
"average",
"capability",
"."
] | [
{
"id": 4,
"type": "column",
"value": "capability"
},
{
"id": 5,
"type": "column",
"value": "student_id"
},
{
"id": 2,
"type": "table",
"value": "student"
},
{
"id": 0,
"type": "column",
"value": "f_name"
},
{
"id": 1,
"type": "column",
"va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
0
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"... | [
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,699 | sports_competition | spider:train_spider.json:3365 | List the types of competition and the number of competitions of each type. | SELECT Competition_type , COUNT(*) FROM competition GROUP BY Competition_type | [
"List",
"the",
"types",
"of",
"competition",
"and",
"the",
"number",
"of",
"competitions",
"of",
"each",
"type",
"."
] | [
{
"id": 1,
"type": "column",
"value": "competition_type"
},
{
"id": 0,
"type": "table",
"value": "competition"
}
] | [
{
"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": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,700 | cinema | spider:train_spider.json:1947 | Show all directors. | SELECT DISTINCT directed_by FROM film | [
"Show",
"all",
"directors",
"."
] | [
{
"id": 1,
"type": "column",
"value": "directed_by"
},
{
"id": 0,
"type": "table",
"value": "film"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O"
] |
10,701 | sing_contest | bird:test.json:742 | List the name of the songs in ascending, lexicographical order. | SELECT name FROM songs ORDER BY name | [
"List",
"the",
"name",
"of",
"the",
"songs",
"in",
"ascending",
",",
"lexicographical",
"order",
"."
] | [
{
"id": 0,
"type": "table",
"value": "songs"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,702 | tracking_grants_for_research | spider:train_spider.json:4324 | What is the total grant amount of the organisations described as research? | SELECT sum(grant_amount) FROM Grants AS T1 JOIN Organisations AS T2 ON T1.organisation_id = T2.organisation_id JOIN organisation_Types AS T3 ON T2.organisation_type = T3.organisation_type WHERE T3.organisation_type_description = 'Research' | [
"What",
"is",
"the",
"total",
"grant",
"amount",
"of",
"the",
"organisations",
"described",
"as",
"research",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "organisation_type_description"
},
{
"id": 0,
"type": "table",
"value": "organisation_types"
},
{
"id": 6,
"type": "column",
"value": "organisation_type"
},
{
"id": 7,
"type": "column",
"value": "organisation_id"
},
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,703 | network_2 | spider:train_spider.json:4420 | What is the average age for each gender? | SELECT avg(age) , gender FROM Person GROUP BY gender | [
"What",
"is",
"the",
"average",
"age",
"for",
"each",
"gender",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "person"
},
{
"id": 1,
"type": "column",
"value": "gender"
},
{
"id": 2,
"type": "column",
"value": "age"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
10,704 | soccer_2 | spider:train_spider.json:4947 | How many students, on average, does each college have enrolled? | SELECT avg(enr) FROM College | [
"How",
"many",
"students",
",",
"on",
"average",
",",
"does",
"each",
"college",
"have",
"enrolled",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "college"
},
{
"id": 1,
"type": "column",
"value": "enr"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,705 | restaurant | bird:train.json:1773 | Which county in northern California has the highest number of cities? | SELECT county FROM geographic WHERE region = 'northern california' GROUP BY county ORDER BY COUNT(city) DESC LIMIT 1 | [
"Which",
"county",
"in",
"northern",
"California",
"has",
"the",
"highest",
"number",
"of",
"cities",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "northern california"
},
{
"id": 0,
"type": "table",
"value": "geographic"
},
{
"id": 1,
"type": "column",
"value": "county"
},
{
"id": 2,
"type": "column",
"value": "region"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3,
4
]
},
{
"entity_id": 4,
"token_idxs": [
1
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,707 | musical | spider:train_spider.json:240 | List the name of actors whose age is not 20. | SELECT Name FROM actor WHERE Age != 20 | [
"List",
"the",
"name",
"of",
"actors",
"whose",
"age",
"is",
"not",
"20",
"."
] | [
{
"id": 0,
"type": "table",
"value": "actor"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "20"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
10,708 | retail_complains | bird:train.json:272 | How many priority urgent complaints were received in march of 2017? List the complaint ID of these complaints. | SELECT COUNT(`Complaint ID`) FROM callcenterlogs WHERE `Date received` LIKE '2017-01%' AND priority = ( SELECT MAX(priority) FROM callcenterlogs ) | [
"How",
"many",
"priority",
"urgent",
"complaints",
"were",
"received",
"in",
"march",
"of",
"2017",
"?",
"List",
"the",
"complaint",
"ID",
"of",
"these",
"complaints",
"."
] | [
{
"id": 0,
"type": "table",
"value": "callcenterlogs"
},
{
"id": 2,
"type": "column",
"value": "Date received"
},
{
"id": 1,
"type": "column",
"value": "Complaint ID"
},
{
"id": 3,
"type": "value",
"value": "2017-01%"
},
{
"id": 4,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
14,
15
]
},
{
"entity_id": 2,
"token_idxs": [
5,
6
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
10,709 | customers_and_orders | bird:test.json:241 | What is the price for the product with name Monitor? | SELECT product_price FROM Products WHERE product_name = "Monitor" | [
"What",
"is",
"the",
"price",
"for",
"the",
"product",
"with",
"name",
"Monitor",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "product_price"
},
{
"id": 2,
"type": "column",
"value": "product_name"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 3,
"type": "column",
"value": "Monitor"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O"
] |
10,710 | human_resources | bird:train.json:8989 | Calculate the monthly average salary of the employee with highest salary. Mention his name, position title and location city. | SELECT SUM(CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL)) / 12 AS avg, T1.firstname, T1.lastname , T2.positiontitle, T3.locationcity FROM employee AS T1 INNER JOIN position AS T2 ON T1.positionID = T2.positionID INNER JOIN location AS T3 ON T1.locationID = T3.locationID WHERE CAST(REPLACE(SUBSTR(T1.salary, 4)... | [
"Calculate",
"the",
"monthly",
"average",
"salary",
"of",
"the",
"employee",
"with",
"highest",
"salary",
".",
"Mention",
"his",
"name",
",",
"position",
"title",
"and",
"location",
"city",
"."
] | [
{
"id": 2,
"type": "column",
"value": "positiontitle"
},
{
"id": 3,
"type": "column",
"value": "locationcity"
},
{
"id": 8,
"type": "column",
"value": "locationid"
},
{
"id": 9,
"type": "column",
"value": "positionid"
},
{
"id": 0,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": [
20
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
}... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
10,711 | authors | bird:train.json:3618 | How many papers are published under the journal "Software - Practice and Experience"? | SELECT COUNT(T1.Id) FROM Paper AS T1 INNER JOIN Journal AS T2 ON T1.JournalId = T2.Id WHERE T2.FullName = 'Software - Practice and Experience' | [
"How",
"many",
"papers",
"are",
"published",
"under",
"the",
"journal",
"\"",
"Software",
"-",
"Practice",
"and",
"Experience",
"\"",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Software - Practice and Experience"
},
{
"id": 5,
"type": "column",
"value": "journalid"
},
{
"id": 2,
"type": "column",
"value": "fullname"
},
{
"id": 1,
"type": "table",
"value": "journal"
},
{
"id": 0,
"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10,
11,
12,
13
]
},
{
"entity_id": 4,
"token_idxs... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
10,712 | superstore | bird:train.json:2381 | What are the total sales of the accumulated orders of Hon Valutask Swivel Chairs in the West region? | SELECT SUM(T1.Sales) FROM west_superstore AS T1 INNER JOIN product AS T2 ON T1.`Product ID` = T2.`Product ID` WHERE T2.`Product Name` = 'Hon Valutask Swivel Chairs' AND T1.Region = 'West' | [
"What",
"are",
"the",
"total",
"sales",
"of",
"the",
"accumulated",
"orders",
"of",
"Hon",
"Valutask",
"Swivel",
"Chairs",
"in",
"the",
"West",
"region",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Hon Valutask Swivel Chairs"
},
{
"id": 0,
"type": "table",
"value": "west_superstore"
},
{
"id": 4,
"type": "column",
"value": "Product Name"
},
{
"id": 3,
"type": "column",
"value": "Product ID"
},
{
"id": 1,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,713 | student_assessment | spider:train_spider.json:59 | What are the ids of the students who registered for some courses but had the least number of courses for all students? | SELECT student_id FROM student_course_registrations GROUP BY student_id ORDER BY count(*) LIMIT 1 | [
"What",
"are",
"the",
"ids",
"of",
"the",
"students",
"who",
"registered",
"for",
"some",
"courses",
"but",
"had",
"the",
"least",
"number",
"of",
"courses",
"for",
"all",
"students",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student_course_registrations"
},
{
"id": 1,
"type": "column",
"value": "student_id"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 1,
"token_idxs": [
21
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,715 | school_player | spider:train_spider.json:4892 | List the locations of schools that do not have any player. | SELECT LOCATION FROM school WHERE School_ID NOT IN (SELECT School_ID FROM Player) | [
"List",
"the",
"locations",
"of",
"schools",
"that",
"do",
"not",
"have",
"any",
"player",
"."
] | [
{
"id": 2,
"type": "column",
"value": "school_id"
},
{
"id": 1,
"type": "column",
"value": "location"
},
{
"id": 0,
"type": "table",
"value": "school"
},
{
"id": 3,
"type": "table",
"value": "player"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,716 | movie_3 | bird:train.json:9269 | What is the total amount paid for rentals made on July 29, 2005? | SELECT SUM(T2.amount) FROM rental AS T1 INNER JOIN payment AS T2 ON T1.rental_id = T2.rental_id WHERE date(T1.rental_date) = '2005-07-29%' | [
"What",
"is",
"the",
"total",
"amount",
"paid",
"for",
"rentals",
"made",
"on",
"July",
"29",
",",
"2005",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "2005-07-29%"
},
{
"id": 5,
"type": "column",
"value": "rental_date"
},
{
"id": 4,
"type": "column",
"value": "rental_id"
},
{
"id": 1,
"type": "table",
"value": "payment"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,717 | cs_semester | bird:train.json:920 | Provide the registered courses' names by undergraduate students with GPA of 3.7 and above. | SELECT DISTINCT T1.f_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 T1.type = 'UG' AND T1.gpa > 3.7 | [
"Provide",
"the",
"registered",
"courses",
"'",
"names",
"by",
"undergraduate",
"students",
"with",
"GPA",
"of",
"3.7",
"and",
"above",
"."
] | [
{
"id": 3,
"type": "table",
"value": "registration"
},
{
"id": 9,
"type": "column",
"value": "student_id"
},
{
"id": 4,
"type": "column",
"value": "course_id"
},
{
"id": 2,
"type": "table",
"value": "student"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
10,718 | disney | bird:train.json:4633 | Which character is the villain of the most popular movie? | SELECT T2.villian FROM `movies_total_gross` AS T1 INNER JOIN characters AS T2 ON T1.movie_title = T2.movie_title ORDER BY T1.total_gross DESC LIMIT 1 | [
"Which",
"character",
"is",
"the",
"villain",
"of",
"the",
"most",
"popular",
"movie",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "movies_total_gross"
},
{
"id": 3,
"type": "column",
"value": "total_gross"
},
{
"id": 4,
"type": "column",
"value": "movie_title"
},
{
"id": 2,
"type": "table",
"value": "characters"
},
{
"id": 0,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"... | [
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,719 | mondial_geo | bird:train.json:8504 | What is the population density of the nation whose capital city is in the Distrito Federal province, and what portion of its gross domestic product is devoted to its industries? | SELECT T1.Population / T1.Area, T2.Industry FROM country AS T1 INNER JOIN economy AS T2 ON T1.Code = T2.Country WHERE T1.Province = 'Distrito Federal' | [
"What",
"is",
"the",
"population",
"density",
"of",
"the",
"nation",
"whose",
"capital",
"city",
"is",
"in",
"the",
"Distrito",
"Federal",
"province",
",",
"and",
"what",
"portion",
"of",
"its",
"gross",
"domestic",
"product",
"is",
"devoted",
"to",
"its",
... | [
{
"id": 4,
"type": "value",
"value": "Distrito Federal"
},
{
"id": 5,
"type": "column",
"value": "population"
},
{
"id": 0,
"type": "column",
"value": "industry"
},
{
"id": 3,
"type": "column",
"value": "province"
},
{
"id": 1,
"type": "table",... | [
{
"entity_id": 0,
"token_idxs": [
30
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
14,
15
]
},
{
"entity_... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,720 | program_share | spider:train_spider.json:3740 | find the name of the program that was launched most recently. | SELECT name FROM program ORDER BY launch DESC LIMIT 1 | [
"find",
"the",
"name",
"of",
"the",
"program",
"that",
"was",
"launched",
"most",
"recently",
"."
] | [
{
"id": 0,
"type": "table",
"value": "program"
},
{
"id": 2,
"type": "column",
"value": "launch"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
10,721 | movie | bird:train.json:746 | Give the name of the No.1 character in the credit list from the highest rating thriller movie. | SELECT T2.`Character Name` FROM movie AS T1 INNER JOIN characters AS T2 ON T1.MovieID = T2.MovieID WHERE T2.creditOrder = '1' AND T1.Genre = 'Thriller' ORDER BY T1.Rating DESC LIMIT 1 | [
"Give",
"the",
"name",
"of",
"the",
"No.1",
"character",
"in",
"the",
"credit",
"list",
"from",
"the",
"highest",
"rating",
"thriller",
"movie",
"."
] | [
{
"id": 0,
"type": "column",
"value": "Character Name"
},
{
"id": 5,
"type": "column",
"value": "creditorder"
},
{
"id": 2,
"type": "table",
"value": "characters"
},
{
"id": 8,
"type": "value",
"value": "Thriller"
},
{
"id": 4,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
16
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"B-TABLE",
"O"
] |
10,722 | hockey | bird:train.json:7707 | For the team which had three different goalies in the 2011 postseason games, how many games did they win in the regular season? | SELECT SUM(T2.W) FROM Goalies AS T1 INNER JOIN Teams AS T2 ON T1.tmID = T2.tmID WHERE T2.year = 2011 GROUP BY T1.tmID HAVING COUNT(DISTINCT T1.playerID) = 3 | [
"For",
"the",
"team",
"which",
"had",
"three",
"different",
"goalies",
"in",
"the",
"2011",
"postseason",
"games",
",",
"how",
"many",
"games",
"did",
"they",
"win",
"in",
"the",
"regular",
"season",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "playerid"
},
{
"id": 1,
"type": "table",
"value": "goalies"
},
{
"id": 2,
"type": "table",
"value": "teams"
},
{
"id": 0,
"type": "column",
"value": "tmid"
},
{
"id": 3,
"type": "column",
"value": "yea... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,723 | authors | bird:train.json:3569 | List the short name of all conferences whose full name begins with International Symposium. | SELECT ShortName FROM Conference WHERE FullName LIKE 'International Symposium%' | [
"List",
"the",
"short",
"name",
"of",
"all",
"conferences",
"whose",
"full",
"name",
"begins",
"with",
"International",
"Symposium",
"."
] | [
{
"id": 3,
"type": "value",
"value": "International Symposium%"
},
{
"id": 0,
"type": "table",
"value": "conference"
},
{
"id": 1,
"type": "column",
"value": "shortname"
},
{
"id": 2,
"type": "column",
"value": "fullname"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
8,
9
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_id... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
10,724 | human_resources | bird:train.json:8982 | What are the maximum and minimum salary range and position title of Bill Marlin? | SELECT T2.maxsalary, T2.minsalary, T2.positiontitle FROM employee AS T1 INNER JOIN position AS T2 ON T1.positionID = T2.positionID WHERE T1.firstname = 'Bill' AND T1.lastname = 'Marlin' | [
"What",
"are",
"the",
"maximum",
"and",
"minimum",
"salary",
"range",
"and",
"position",
"title",
"of",
"Bill",
"Marlin",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "positiontitle"
},
{
"id": 5,
"type": "column",
"value": "positionid"
},
{
"id": 0,
"type": "column",
"value": "maxsalary"
},
{
"id": 1,
"type": "column",
"value": "minsalary"
},
{
"id": 6,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"B-VALUE",
"O"
] |
10,725 | thrombosis_prediction | bird:dev.json:1240 | From laboratory examinations in 1991, what is the average hematoclit level that is lower than the normal range. | SELECT AVG(T2.HCT) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.HCT < 29 AND STRFTIME('%Y', T2.Date) = '1991' | [
"From",
"laboratory",
"examinations",
"in",
"1991",
",",
"what",
"is",
"the",
"average",
"hematoclit",
"level",
"that",
"is",
"lower",
"than",
"the",
"normal",
"range",
"."
] | [
{
"id": 1,
"type": "table",
"value": "laboratory"
},
{
"id": 0,
"type": "table",
"value": "patient"
},
{
"id": 5,
"type": "value",
"value": "1991"
},
{
"id": 7,
"type": "column",
"value": "date"
},
{
"id": 2,
"type": "column",
"value": "hct... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
4
... | [
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,727 | sports_competition | spider:train_spider.json:3350 | What is the total number of clubs that have less than 10 medals in total? | SELECT count(*) FROM club_rank WHERE Total < 10 | [
"What",
"is",
"the",
"total",
"number",
"of",
"clubs",
"that",
"have",
"less",
"than",
"10",
"medals",
"in",
"total",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "club_rank"
},
{
"id": 1,
"type": "column",
"value": "total"
},
{
"id": 2,
"type": "value",
"value": "10"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O"
] |
10,728 | entertainment_awards | spider:train_spider.json:4612 | Show the id, name of each festival and the number of artworks it has nominated. | SELECT T1.Festival_ID , T3.Festival_Name , COUNT(*) FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID = T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID = T3.Festival_ID GROUP BY T1.Festival_ID | [
"Show",
"the",
"i",
"d",
",",
"name",
"of",
"each",
"festival",
"and",
"the",
"number",
"of",
"artworks",
"it",
"has",
"nominated",
"."
] | [
{
"id": 2,
"type": "table",
"value": "festival_detail"
},
{
"id": 1,
"type": "column",
"value": "festival_name"
},
{
"id": 0,
"type": "column",
"value": "festival_id"
},
{
"id": 3,
"type": "table",
"value": "nomination"
},
{
"id": 5,
"type": "c... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O"
] |
10,729 | journal_committee | spider:train_spider.json:649 | How many editors are there? | SELECT count(*) FROM editor | [
"How",
"many",
"editors",
"are",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "editor"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,730 | student_club | bird:dev.json:1334 | List the full name of the Student_Club members that grew up in Illinois state. | SELECT T1.first_name, T1.last_name FROM member AS T1 INNER JOIN zip_code AS T2 ON T1.zip = T2.zip_code WHERE T2.state = 'Illinois' | [
"List",
"the",
"full",
"name",
"of",
"the",
"Student_Club",
"members",
"that",
"grew",
"up",
"in",
"Illinois",
"state",
"."
] | [
{
"id": 0,
"type": "column",
"value": "first_name"
},
{
"id": 1,
"type": "column",
"value": "last_name"
},
{
"id": 3,
"type": "table",
"value": "zip_code"
},
{
"id": 5,
"type": "value",
"value": "Illinois"
},
{
"id": 7,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,731 | sakila_1 | spider:train_spider.json:2983 | Count the number of different film ratings. | SELECT count(DISTINCT rating) FROM film | [
"Count",
"the",
"number",
"of",
"different",
"film",
"ratings",
"."
] | [
{
"id": 1,
"type": "column",
"value": "rating"
},
{
"id": 0,
"type": "table",
"value": "film"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
10,732 | software_company | bird:train.json:8557 | Among the customers with an average income per inhabitant above 3000, what percentage are in their eighties? | SELECT CAST(SUM(CASE WHEN T1.age BETWEEN 80 AND 89 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.ID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T2.INCOME_K > 3000 | [
"Among",
"the",
"customers",
"with",
"an",
"average",
"income",
"per",
"inhabitant",
"above",
"3000",
",",
"what",
"percentage",
"are",
"in",
"their",
"eighties",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 2,
"type": "column",
"value": "income_k"
},
{
"id": 1,
"type": "table",
"value": "demog"
},
{
"id": 4,
"type": "column",
"value": "geoid"
},
{
"id": 3,
"type": "value",
"value": "3... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
0
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
10,733 | sports_competition | spider:train_spider.json:3380 | What are the names of all players that got more than the average number of points? | SELECT name FROM player WHERE points > (SELECT avg(points) FROM player) | [
"What",
"are",
"the",
"names",
"of",
"all",
"players",
"that",
"got",
"more",
"than",
"the",
"average",
"number",
"of",
"points",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "player"
},
{
"id": 2,
"type": "column",
"value": "points"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,734 | retail_world | bird:train.json:6448 | Make a list of all the territories in the Southern region. | SELECT DISTINCT T1.TerritoryDescription FROM Territories AS T1 INNER JOIN Region AS T2 ON T1.RegionID = T2.RegionID WHERE T2.RegionDescription = 'Southern' | [
"Make",
"a",
"list",
"of",
"all",
"the",
"territories",
"in",
"the",
"Southern",
"region",
"."
] | [
{
"id": 0,
"type": "column",
"value": "territorydescription"
},
{
"id": 3,
"type": "column",
"value": "regiondescription"
},
{
"id": 1,
"type": "table",
"value": "territories"
},
{
"id": 4,
"type": "value",
"value": "Southern"
},
{
"id": 5,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
10,735 | shipping | bird:train.json:5599 | Provide the ship date of the first shipment to customers in South Carolina. | SELECT MIN(T1.ship_date) FROM shipment AS T1 INNER JOIN customer AS T2 ON T1.cust_id = T2.cust_id WHERE T2.state = 'SC' | [
"Provide",
"the",
"ship",
"date",
"of",
"the",
"first",
"shipment",
"to",
"customers",
"in",
"South",
"Carolina",
"."
] | [
{
"id": 4,
"type": "column",
"value": "ship_date"
},
{
"id": 0,
"type": "table",
"value": "shipment"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 5,
"type": "column",
"value": "cust_id"
},
{
"id": 2,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
10,736 | manufactory_1 | spider:train_spider.json:5291 | What are the names of companies with revenue between 100 and 150? | SELECT name FROM manufacturers WHERE revenue BETWEEN 100 AND 150 | [
"What",
"are",
"the",
"names",
"of",
"companies",
"with",
"revenue",
"between",
"100",
"and",
"150",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "manufacturers"
},
{
"id": 2,
"type": "column",
"value": "revenue"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"value": "100"
},
{
"id": 4,
"type": "value",
"value": "1... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
10,737 | customers_and_invoices | spider:train_spider.json:1585 | Show ids for all transactions whose amounts are greater than the average. | SELECT transaction_id FROM Financial_transactions WHERE transaction_amount > (SELECT avg(transaction_amount) FROM Financial_transactions) | [
"Show",
"ids",
"for",
"all",
"transactions",
"whose",
"amounts",
"are",
"greater",
"than",
"the",
"average",
"."
] | [
{
"id": 0,
"type": "table",
"value": "financial_transactions"
},
{
"id": 2,
"type": "column",
"value": "transaction_amount"
},
{
"id": 1,
"type": "column",
"value": "transaction_id"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
5,
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,739 | authors | bird:train.json:3637 | What is the oldest published book? | SELECT Title FROM Paper WHERE Year > 0 ORDER BY Year ASC LIMIT 1 | [
"What",
"is",
"the",
"oldest",
"published",
"book",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "paper"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "0"
}
] | [
{
"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... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,740 | customers_and_addresses | spider:train_spider.json:6137 | Find the names of customers who never ordered product Latte. | SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id = t2.customer_id JOIN order_items AS t3 ON t2.order_id = t3.order_id JOIN products AS t4 ON t3.product_id = t4.product_id WHERE t4.product_details = 'Latte' | [
"Find",
"the",
"names",
"of",
"customers",
"who",
"never",
"ordered",
"product",
"Latte",
"."
] | [
{
"id": 3,
"type": "column",
"value": "product_details"
},
{
"id": 7,
"type": "table",
"value": "customer_orders"
},
{
"id": 1,
"type": "column",
"value": "customer_name"
},
{
"id": 5,
"type": "table",
"value": "order_items"
},
{
"id": 9,
"type... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-VALUE",
"O"
] |
10,741 | software_company | bird:train.json:8572 | What is the age of female customers within the number of inhabitants below 30? | SELECT age FROM Customers WHERE GEOID IN ( SELECT GEOID FROM Demog WHERE INHABITANTS_K < 30 ) AND SEX = 'Female' | [
"What",
"is",
"the",
"age",
"of",
"female",
"customers",
"within",
"the",
"number",
"of",
"inhabitants",
"below",
"30",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "inhabitants_k"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 4,
"type": "value",
"value": "Female"
},
{
"id": 2,
"type": "column",
"value": "geoid"
},
{
"id": 5,
"type": "table",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,742 | game_1 | spider:train_spider.json:5983 | Show all video game types and the number of video games in each type. | SELECT gtype , count(*) FROM Video_games GROUP BY gtype | [
"Show",
"all",
"video",
"game",
"types",
"and",
"the",
"number",
"of",
"video",
"games",
"in",
"each",
"type",
"."
] | [
{
"id": 0,
"type": "table",
"value": "video_games"
},
{
"id": 1,
"type": "column",
"value": "gtype"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9,
10
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
10,743 | california_schools | bird:dev.json:56 | Of all the schools with a mailing state address in California, how many are active in San Joaquin city? | SELECT COUNT(CDSCode) FROM schools WHERE City = 'San Joaquin' AND MailState = 'CA' AND StatusType = 'Active' | [
"Of",
"all",
"the",
"schools",
"with",
"a",
"mailing",
"state",
"address",
"in",
"California",
",",
"how",
"many",
"are",
"active",
"in",
"San",
"Joaquin",
"city",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "San Joaquin"
},
{
"id": 6,
"type": "column",
"value": "statustype"
},
{
"id": 4,
"type": "column",
"value": "mailstate"
},
{
"id": 0,
"type": "table",
"value": "schools"
},
{
"id": 1,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
19
]
},
{
"entity_id": 3,
"token_idxs": [
17,
18
]
},
{
"entity_id": 4,
"token_idxs": [
6,
7
]
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
10,744 | soccer_2016 | bird:train.json:1912 | How many games were played in March 2010? | SELECT SUM(CASE WHEN Match_Date LIKE '2010-03%' THEN 1 ELSE 0 END) FROM `Match` | [
"How",
"many",
"games",
"were",
"played",
"in",
"March",
"2010",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "match_date"
},
{
"id": 4,
"type": "value",
"value": "2010-03%"
},
{
"id": 0,
"type": "table",
"value": "Match"
},
{
"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": [
7
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"O"
] |
10,745 | driving_school | spider:train_spider.json:6660 | List email address and birthday of customer whose first name as Carole. | SELECT email_address , date_of_birth FROM Customers WHERE first_name = "Carole" | [
"List",
"email",
"address",
"and",
"birthday",
"of",
"customer",
"whose",
"first",
"name",
"as",
"Carole",
"."
] | [
{
"id": 1,
"type": "column",
"value": "email_address"
},
{
"id": 2,
"type": "column",
"value": "date_of_birth"
},
{
"id": 3,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 4,
"type": "colu... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
1,
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
... | [
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O"
] |
10,746 | codebase_comments | bird:train.json:599 | Among the repositories with over 200 likes, how many of them have files contained by solutions with a processed time of under 636439500080712000? | SELECT COUNT(T2.RepoId) FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T2.ProcessedTime < 636439500080712000 AND T1.Stars > 200 | [
"Among",
"the",
"repositories",
"with",
"over",
"200",
"likes",
",",
"how",
"many",
"of",
"them",
"have",
"files",
"contained",
"by",
"solutions",
"with",
"a",
"processed",
"time",
"of",
"under",
"636439500080712000",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "636439500080712000"
},
{
"id": 4,
"type": "column",
"value": "processedtime"
},
{
"id": 1,
"type": "table",
"value": "solution"
},
{
"id": 2,
"type": "column",
"value": "repoid"
},
{
"id": 6,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
16
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
19,
20
]
},
{
"entity_id": 5,
"t... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
10,747 | public_review_platform | bird:train.json:3892 | List the categories of active businesses in Surprise, AZ. | SELECT T3.category_name FROM Business AS T1 INNER JOIN Business_Categories ON T1.business_id = Business_Categories.business_id INNER JOIN Categories AS T3 ON Business_Categories.category_id = T3.category_id WHERE T1.active LIKE 'TRUE' AND T1.state LIKE 'AZ' AND T1.city LIKE 'Surprise' GROUP BY T3.category_name | [
"List",
"the",
"categories",
"of",
"active",
"businesses",
"in",
"Surprise",
",",
"AZ",
"."
] | [
{
"id": 3,
"type": "table",
"value": "business_categories"
},
{
"id": 0,
"type": "column",
"value": "category_name"
},
{
"id": 4,
"type": "column",
"value": "category_id"
},
{
"id": 11,
"type": "column",
"value": "business_id"
},
{
"id": 1,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
10,749 | menu | bird:train.json:5540 | Calculate the image area of the page menu for the dish named "Baked Stuffed Mullet & Sauce Pomard". Please include the page number and image ID. | SELECT T1.full_height * T1.full_width, T1.page_number, T1.image_id FROM MenuPage AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.menu_page_id INNER JOIN Dish AS T3 ON T2.dish_id = T3.id WHERE T3.name = 'Baked Stuffed Mullet & Sauce Pomard' | [
"Calculate",
"the",
"image",
"area",
"of",
"the",
"page",
"menu",
"for",
"the",
"dish",
"named",
"\"",
"Baked",
"Stuffed",
"Mullet",
"&",
"Sauce",
"Pomard",
"\"",
".",
"Please",
"include",
"the",
"page",
"number",
"and",
"image",
"ID",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Baked Stuffed Mullet & Sauce Pomard"
},
{
"id": 11,
"type": "column",
"value": "menu_page_id"
},
{
"id": 0,
"type": "column",
"value": "page_number"
},
{
"id": 5,
"type": "column",
"value": "full_height"
},
{
"... | [
{
"entity_id": 0,
"token_idxs": [
25
]
},
{
"entity_id": 1,
"token_idxs": [
27
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
13,
14... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
10,750 | car_retails | bird:train.json:1652 | What is the total price of the order 10100? | SELECT SUM(t.priceEach * t.quantityOrdered) FROM orderdetails t WHERE t.orderNumber = '10100' | [
"What",
"is",
"the",
"total",
"price",
"of",
"the",
"order",
"10100",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "quantityordered"
},
{
"id": 0,
"type": "table",
"value": "orderdetails"
},
{
"id": 1,
"type": "column",
"value": "ordernumber"
},
{
"id": 3,
"type": "column",
"value": "priceeach"
},
{
"id": 2,
"type": "va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
10,751 | manufactory_1 | spider:train_spider.json:5314 | Find the number of different products that are produced by companies at different headquarter cities. | SELECT count(DISTINCT T1.name) , T2.Headquarter FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer = T2.code GROUP BY T2.Headquarter | [
"Find",
"the",
"number",
"of",
"different",
"products",
"that",
"are",
"produced",
"by",
"companies",
"at",
"different",
"headquarter",
"cities",
"."
] | [
{
"id": 2,
"type": "table",
"value": "manufacturers"
},
{
"id": 4,
"type": "column",
"value": "manufacturer"
},
{
"id": 0,
"type": "column",
"value": "headquarter"
},
{
"id": 1,
"type": "table",
"value": "products"
},
{
"id": 3,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
10,752 | university | bird:train.json:8106 | Calculate the average number of criterias among "Times Higher Education World University Ranking","Shanghai Ranking" and "Center for World University Rankings". | SELECT (SUM(CASE WHEN T1.system_name = 'Center for World University Rankings' THEN 1 ELSE 0 END) + SUM(CASE WHEN T1.system_name = 'Shanghai Ranking' THEN 1 ELSE 0 END) + SUM(CASE WHEN T1.system_name = 'Times Higher Education World University Ranking' THEN 1 ELSE 0 END)) / 3 FROM ranking_system AS T1 INNER JOIN ranking_... | [
"Calculate",
"the",
"average",
"number",
"of",
"criterias",
"among",
"\"",
"Times",
"Higher",
"Education",
"World",
"University",
"Ranking\",\"Shanghai",
"Ranking",
"\"",
"and",
"\"",
"Center",
"for",
"World",
"University",
"Rankings",
"\"",
"."
] | [
{
"id": 8,
"type": "value",
"value": "Times Higher Education World University Ranking"
},
{
"id": 9,
"type": "value",
"value": "Center for World University Rankings"
},
{
"id": 4,
"type": "column",
"value": "ranking_system_id"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
22
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"O"
] |
10,753 | college_1 | spider:train_spider.json:3227 | How many different classes are there? | SELECT count(DISTINCT class_code) FROM CLASS | [
"How",
"many",
"different",
"classes",
"are",
"there",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "class_code"
},
{
"id": 0,
"type": "table",
"value": "class"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,754 | soccer_2016 | bird:train.json:1893 | Among the matches, what percentage have a winning margin above 100? | SELECT CAST(COUNT(CASE WHEN Win_Margin > 100 THEN 1 ELSE 0 END) AS REAL) * 100 / TOTAL(Match_Id) FROM `Match` | [
"Among",
"the",
"matches",
",",
"what",
"percentage",
"have",
"a",
"winning",
"margin",
"above",
"100",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "win_margin"
},
{
"id": 2,
"type": "column",
"value": "match_id"
},
{
"id": 0,
"type": "table",
"value": "Match"
},
{
"id": 1,
"type": "value",
"value": "100"
},
{
"id": 3,
"type": "value",
"value": "0"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,755 | world | bird:train.json:7833 | Provide the name, located country, and life expectancy of the most populated city | SELECT T2.Name, T1.Code, T1.LifeExpectancy FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode ORDER BY T2.Population DESC LIMIT 1 | [
"Provide",
"the",
"name",
",",
"located",
"country",
",",
"and",
"life",
"expectancy",
"of",
"the",
"most",
"populated",
"city"
] | [
{
"id": 2,
"type": "column",
"value": "lifeexpectancy"
},
{
"id": 6,
"type": "column",
"value": "countrycode"
},
{
"id": 5,
"type": "column",
"value": "population"
},
{
"id": 3,
"type": "table",
"value": "country"
},
{
"id": 0,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8,
9
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE"
] |
10,756 | university_rank | bird:test.json:1793 | List all university names in ascending order of their reputation points. | SELECT T1.university_name FROM University AS T1 JOIN Overall_ranking AS T2 ON T1.university_id = T2.university_id ORDER BY T2.reputation_point | [
"List",
"all",
"university",
"names",
"in",
"ascending",
"order",
"of",
"their",
"reputation",
"points",
"."
] | [
{
"id": 3,
"type": "column",
"value": "reputation_point"
},
{
"id": 0,
"type": "column",
"value": "university_name"
},
{
"id": 2,
"type": "table",
"value": "overall_ranking"
},
{
"id": 4,
"type": "column",
"value": "university_id"
},
{
"id": 1,
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
10,757 | store_1 | spider:train_spider.json:625 | List name of all tracks in Balls to the Wall. | SELECT T2.name FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.title = "Balls to the Wall"; | [
"List",
"name",
"of",
"all",
"tracks",
"in",
"Balls",
"to",
"the",
"Wall",
"."
] | [
{
"id": 4,
"type": "column",
"value": "Balls to the Wall"
},
{
"id": 6,
"type": "column",
"value": "genre_id"
},
{
"id": 1,
"type": "table",
"value": "albums"
},
{
"id": 2,
"type": "table",
"value": "tracks"
},
{
"id": 3,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6,
7,
8,
9
]
},
... | [
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
10,758 | cre_Theme_park | spider:train_spider.json:5912 | Find the names and opening hours of the tourist attractions that we get to by bus or walk. | SELECT Name , Opening_Hours FROM TOURIST_ATTRACTIONS WHERE How_to_Get_There = "bus" OR How_to_Get_There = "walk" | [
"Find",
"the",
"names",
"and",
"opening",
"hours",
"of",
"the",
"tourist",
"attractions",
"that",
"we",
"get",
"to",
"by",
"bus",
"or",
"walk",
"."
] | [
{
"id": 0,
"type": "table",
"value": "tourist_attractions"
},
{
"id": 3,
"type": "column",
"value": "how_to_get_there"
},
{
"id": 2,
"type": "column",
"value": "opening_hours"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 5,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
8,
9
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
15
]
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O"
] |
10,759 | retail_world | bird:train.json:6502 | What percentage of orders were placed by customers in Madrid city in 1996? | SELECT CAST(COUNT(CASE WHEN T1.City = 'Madrid' THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T1.City) FROM Customers AS T1 INNER JOIN Orders AS T2 ON T1.CustomerID = T2.CustomerID WHERE STRFTIME('%Y', T2.OrderDate) = 1996 | [
"What",
"percentage",
"of",
"orders",
"were",
"placed",
"by",
"customers",
"in",
"Madrid",
"city",
"in",
"1996",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "customerid"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 5,
"type": "column",
"value": "orderdate"
},
{
"id": 1,
"type": "table",
"value": "orders"
},
{
"id": 9,
"type": "value",
"val... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,760 | simpson_episodes | bird:train.json:4362 | List the stars of episodes aired in November 2008. | SELECT T2.stars FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE SUBSTR(T1.air_date, 1, 7) = '2008-11'; | [
"List",
"the",
"stars",
"of",
"episodes",
"aired",
"in",
"November",
"2008",
"."
] | [
{
"id": 4,
"type": "column",
"value": "episode_id"
},
{
"id": 5,
"type": "column",
"value": "air_date"
},
{
"id": 1,
"type": "table",
"value": "episode"
},
{
"id": 3,
"type": "value",
"value": "2008-11"
},
{
"id": 0,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
10,761 | retail_complains | bird:train.json:262 | Did Ms. Lyric Emely Taylor provide the consent for result of the complaint call on 2016/5/20? | SELECT CASE WHEN T2.`Consumer consent provided?` IN (NULL, 'N/A', '') THEN 'No' ELSE 'Yes' END FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.first = 'Lyric' AND T1.middle = 'Emely' AND T1.last = 'Taylor' AND T1.sex = 'Female' AND T2.`Date received` = '2016-05-20' | [
"Did",
"Ms.",
"Lyric",
"Emely",
"Taylor",
"provide",
"the",
"consent",
"for",
"result",
"of",
"the",
"complaint",
"call",
"on",
"2016/5/20",
"?"
] | [
{
"id": 15,
"type": "column",
"value": "Consumer consent provided?"
},
{
"id": 12,
"type": "column",
"value": "Date received"
},
{
"id": 13,
"type": "value",
"value": "2016-05-20"
},
{
"id": 3,
"type": "column",
"value": "client_id"
},
{
"id": 0,
... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"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": [
2
... | [
"O",
"O",
"B-VALUE",
"B-VALUE",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
10,762 | retail_complains | bird:train.json:375 | How many female clients are there older than 30? | SELECT COUNT(sex) FROM client WHERE sex = 'Female' AND age > 30 | [
"How",
"many",
"female",
"clients",
"are",
"there",
"older",
"than",
"30",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "client"
},
{
"id": 2,
"type": "value",
"value": "Female"
},
{
"id": 1,
"type": "column",
"value": "sex"
},
{
"id": 3,
"type": "column",
"value": "age"
},
{
"id": 4,
"type": "value",
"value": "30"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_... | [
"O",
"O",
"B-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
10,763 | soccer_2016 | bird:train.json:1966 | List the ball IDs, scores, and innings numbers in the over ID 20 of match ID "335988". | SELECT Ball_Id, Runs_Scored, Innings_No FROM Batsman_Scored WHERE Match_Id = 335988 AND Over_Id = 20 | [
"List",
"the",
"ball",
"IDs",
",",
"scores",
",",
"and",
"innings",
"numbers",
"in",
"the",
"over",
"ID",
"20",
"of",
"match",
"ID",
"\"",
"335988",
"\"",
"."
] | [
{
"id": 0,
"type": "table",
"value": "batsman_scored"
},
{
"id": 2,
"type": "column",
"value": "runs_scored"
},
{
"id": 3,
"type": "column",
"value": "innings_no"
},
{
"id": 4,
"type": "column",
"value": "match_id"
},
{
"id": 1,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
16,
17
]
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
10,764 | public_review_platform | bird:train.json:3957 | For the Yelp business in "Tempe" city which got "3.5" stars and review count as "Uber", how many "long" reviews did it get? | SELECT COUNT(T2.review_length) FROM Business AS T1 INNER JOIN Reviews AS T2 ON T1.business_id = T2.business_id WHERE T1.city = 'Tempe' AND T1.stars = '3.5' AND T1.review_count = 'Uber' AND T2.review_length = 'Long' | [
"For",
"the",
"Yelp",
"business",
"in",
"\"",
"Tempe",
"\"",
"city",
"which",
"got",
"\"",
"3.5",
"\"",
"stars",
"and",
"review",
"count",
"as",
"\"",
"Uber",
"\"",
",",
"how",
"many",
"\"",
"long",
"\"",
"reviews",
"did",
"it",
"get",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "review_length"
},
{
"id": 8,
"type": "column",
"value": "review_count"
},
{
"id": 3,
"type": "column",
"value": "business_id"
},
{
"id": 0,
"type": "table",
"value": "business"
},
{
"id": 1,
"type": "table... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
28
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
10,765 | railway | spider:train_spider.json:5641 | Show the builder of railways associated with the trains named "Andaman Exp". | SELECT T1.Builder FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID = T2.Railway_ID WHERE T2.Name = "Andaman Exp" | [
"Show",
"the",
"builder",
"of",
"railways",
"associated",
"with",
"the",
"trains",
"named",
"\"",
"Andaman",
"Exp",
"\"",
"."
] | [
{
"id": 4,
"type": "column",
"value": "Andaman Exp"
},
{
"id": 5,
"type": "column",
"value": "railway_id"
},
{
"id": 0,
"type": "column",
"value": "builder"
},
{
"id": 1,
"type": "table",
"value": "railway"
},
{
"id": 2,
"type": "table",
"v... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
11,
12
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
10,766 | synthea | bird:train.json:1466 | Give me the immunization codes and dates on which Ms. Jacquelyn Shanahan was immunized with influenza seasonal injectable preservative-free vaccine. | SELECT DISTINCT T2.CODE, T2.DATE FROM patients AS T1 INNER JOIN immunizations AS T2 ON T1.patient = T2.PATIENT WHERE T1.prefix = 'Ms.' AND T1.first = 'Jacquelyn' AND T1.last = 'Shanahan' AND T2.DESCRIPTION = 'Influenza seasonal injectable preservative free' | [
"Give",
"me",
"the",
"immunization",
"codes",
"and",
"dates",
"on",
"which",
"Ms.",
"Jacquelyn",
"Shanahan",
"was",
"immunized",
"with",
"influenza",
"seasonal",
"injectable",
"preservative",
"-",
"free",
"vaccine",
"."
] | [
{
"id": 12,
"type": "value",
"value": "Influenza seasonal injectable preservative free"
},
{
"id": 3,
"type": "table",
"value": "immunizations"
},
{
"id": 11,
"type": "column",
"value": "description"
},
{
"id": 8,
"type": "value",
"value": "Jacquelyn"
... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"B-VALUE",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
10,767 | theme_gallery | spider:train_spider.json:1653 | What are all distinct country for artists? | SELECT DISTINCT country FROM artist | [
"What",
"are",
"all",
"distinct",
"country",
"for",
"artists",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 0,
"type": "table",
"value": "artist"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O"
] |
10,768 | inn_1 | spider:train_spider.json:2581 | Find the number of times ROY SWEAZY has reserved a room. | SELECT count(*) FROM Reservations WHERE FirstName = "ROY" AND LastName = "SWEAZY"; | [
"Find",
"the",
"number",
"of",
"times",
"ROY",
"SWEAZY",
"has",
"reserved",
"a",
"room",
"."
] | [
{
"id": 0,
"type": "table",
"value": "reservations"
},
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 3,
"type": "column",
"value": "lastname"
},
{
"id": 4,
"type": "column",
"value": "SWEAZY"
},
{
"id": 2,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": [
8,
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O"
] |
10,769 | language_corpus | bird:train.json:5696 | In the Catalan language, which biwords pair appeared the most in this language/page? | SELECT w1st, w2nd FROM biwords WHERE occurrences = ( SELECT MAX(occurrences) FROM biwords ) | [
"In",
"the",
"Catalan",
"language",
",",
"which",
"biwords",
"pair",
"appeared",
"the",
"most",
"in",
"this",
"language",
"/",
"page",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "occurrences"
},
{
"id": 0,
"type": "table",
"value": "biwords"
},
{
"id": 1,
"type": "column",
"value": "w1st"
},
{
"id": 2,
"type": "column",
"value": "w2nd"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,771 | university | bird:train.json:8105 | How many times more was the number of students of University of Ottawa than Joseph Fourier University in 2013? | SELECT CAST(SUM(CASE WHEN T2.university_name = 'University of Ottawa' THEN T1.num_students ELSE 0 END) AS REAL) / SUM(CASE WHEN T2.university_name = 'Joseph Fourier University' THEN T1.num_students ELSE 0 END) FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id WHERE T1.year = 2013 | [
"How",
"many",
"times",
"more",
"was",
"the",
"number",
"of",
"students",
"of",
"University",
"of",
"Ottawa",
"than",
"Joseph",
"Fourier",
"University",
"in",
"2013",
"?"
] | [
{
"id": 9,
"type": "value",
"value": "Joseph Fourier University"
},
{
"id": 10,
"type": "value",
"value": "University of Ottawa"
},
{
"id": 0,
"type": "table",
"value": "university_year"
},
{
"id": 8,
"type": "column",
"value": "university_name"
},
{
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
18
]
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,772 | voter_2 | spider:train_spider.json:5451 | What is the oldest age among the students? | SELECT max(Age) FROM STUDENT | [
"What",
"is",
"the",
"oldest",
"age",
"among",
"the",
"students",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "age"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
10,773 | restaurant_bills | bird:test.json:642 | List the names of customers that do not have any order. | SELECT name FROM customer WHERE Customer_ID NOT IN (SELECT Customer_ID FROM customer_order) | [
"List",
"the",
"names",
"of",
"customers",
"that",
"do",
"not",
"have",
"any",
"order",
"."
] | [
{
"id": 3,
"type": "table",
"value": "customer_order"
},
{
"id": 2,
"type": "column",
"value": "customer_id"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,774 | film_rank | spider:train_spider.json:4154 | List the studios which average gross is above 4500000. | SELECT Studio FROM film GROUP BY Studio HAVING avg(Gross_in_dollar) >= 4500000 | [
"List",
"the",
"studios",
"which",
"average",
"gross",
"is",
"above",
"4500000",
"."
] | [
{
"id": 3,
"type": "column",
"value": "gross_in_dollar"
},
{
"id": 2,
"type": "value",
"value": "4500000"
},
{
"id": 1,
"type": "column",
"value": "studio"
},
{
"id": 0,
"type": "table",
"value": "film"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
10,775 | cs_semester | bird:train.json:941 | Among the professors with a teachability of 3 and below, what is the percentage of their student advisees with a low salary? | SELECT CAST(SUM(CASE WHEN T1.salary = 'low' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.salary) FROM RA AS T1 INNER JOIN prof AS T2 ON T1.prof_id = T2.prof_id WHERE T2.teachingability < 3 | [
"Among",
"the",
"professors",
"with",
"a",
"teachability",
"of",
"3",
"and",
"below",
",",
"what",
"is",
"the",
"percentage",
"of",
"their",
"student",
"advisees",
"with",
"a",
"low",
"salary",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "teachingability"
},
{
"id": 4,
"type": "column",
"value": "prof_id"
},
{
"id": 6,
"type": "column",
"value": "salary"
},
{
"id": 1,
"type": "table",
"value": "prof"
},
{
"id": 5,
"type": "value",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,776 | professional_basketball | bird:train.json:2949 | Which team had the most same starting players througout the season? Give the full name of the team. | SELECT DISTINCT T1.tmID FROM teams AS T1 INNER JOIN players_teams AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T2.GP = T2.GS | [
"Which",
"team",
"had",
"the",
"most",
"same",
"starting",
"players",
"througout",
"the",
"season",
"?",
"Give",
"the",
"full",
"name",
"of",
"the",
"team",
"."
] | [
{
"id": 2,
"type": "table",
"value": "players_teams"
},
{
"id": 1,
"type": "table",
"value": "teams"
},
{
"id": 0,
"type": "column",
"value": "tmid"
},
{
"id": 5,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "column",
"value": "g... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,777 | toxicology | bird:dev.json:235 | How many molecules are carcinogenic and have the bromine element? | SELECT COUNT(DISTINCT T2.molecule_id) FROM atom AS T1 INNER JOIN molecule AS T2 ON T1.molecule_id = T2.molecule_id WHERE T2.label = '+' AND T1.element = 'br' | [
"How",
"many",
"molecules",
"are",
"carcinogenic",
"and",
"have",
"the",
"bromine",
"element",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "molecule_id"
},
{
"id": 1,
"type": "table",
"value": "molecule"
},
{
"id": 5,
"type": "column",
"value": "element"
},
{
"id": 3,
"type": "column",
"value": "label"
},
{
"id": 0,
"type": "table",
"value... | [
{
"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": [
9
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,778 | video_games | bird:train.json:3346 | Which publisher published the most games? | SELECT T.publisher_name FROM ( SELECT T2.publisher_name, COUNT(DISTINCT T1.game_id) FROM game_publisher AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id GROUP BY T2.publisher_name ORDER BY COUNT(DISTINCT T1.game_id) DESC LIMIT 1 ) t | [
"Which",
"publisher",
"published",
"the",
"most",
"games",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "publisher_name"
},
{
"id": 1,
"type": "table",
"value": "game_publisher"
},
{
"id": 4,
"type": "column",
"value": "publisher_id"
},
{
"id": 2,
"type": "table",
"value": "publisher"
},
{
"id": 3,
"type": "c... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
"... | [
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
10,779 | gymnast | spider:train_spider.json:1752 | List the names of the top 5 oldest people. | SELECT Name FROM People ORDER BY Age DESC LIMIT 5 | [
"List",
"the",
"names",
"of",
"the",
"top",
"5",
"oldest",
"people",
"."
] | [
{
"id": 0,
"type": "table",
"value": "people"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "age"
}
] | [
{
"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": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,780 | regional_sales | bird:train.json:2657 | State all the order numbers for sales team of Samuel Fowler. | SELECT T FROM ( SELECT DISTINCT CASE WHEN T2.`Sales Team` = 'Samuel Fowler' THEN T1.OrderNumber ELSE NULL END AS T FROM `Sales Orders` T1 INNER JOIN `Sales Team` T2 ON T2.SalesTeamID = T1._SalesTeamID ) WHERE T IS NOT NULL | [
"State",
"all",
"the",
"order",
"numbers",
"for",
"sales",
"team",
"of",
"Samuel",
"Fowler",
"."
] | [
{
"id": 7,
"type": "value",
"value": "Samuel Fowler"
},
{
"id": 1,
"type": "table",
"value": "Sales Orders"
},
{
"id": 4,
"type": "column",
"value": "_salesteamid"
},
{
"id": 3,
"type": "column",
"value": "salesteamid"
},
{
"id": 5,
"type": "co... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1,
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
... | [
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
10,781 | student_assessment | spider:train_spider.json:83 | Find id of candidates whose assessment code is "Pass"? | SELECT candidate_id FROM candidate_assessments WHERE asessment_outcome_code = "Pass" | [
"Find",
"i",
"d",
"of",
"candidates",
"whose",
"assessment",
"code",
"is",
"\"",
"Pass",
"\"",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "asessment_outcome_code"
},
{
"id": 0,
"type": "table",
"value": "candidate_assessments"
},
{
"id": 1,
"type": "column",
"value": "candidate_id"
},
{
"id": 3,
"type": "column",
"value": "Pass"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5,
6
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
10,782 | gymnast | spider:train_spider.json:1736 | How many gymnasts are there? | SELECT count(*) FROM gymnast | [
"How",
"many",
"gymnasts",
"are",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "gymnast"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,783 | csu_1 | spider:train_spider.json:2391 | What is the campus fee in the year 2000 for San Jose State University? | SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus = t2.id WHERE t2.campus = "San Jose State University" AND t1.year = 2000 | [
"What",
"is",
"the",
"campus",
"fee",
"in",
"the",
"year",
"2000",
"for",
"San",
"Jose",
"State",
"University",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "San Jose State University"
},
{
"id": 0,
"type": "column",
"value": "campusfee"
},
{
"id": 1,
"type": "table",
"value": "csu_fees"
},
{
"id": 2,
"type": "table",
"value": "campuses"
},
{
"id": 3,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
10,784 | debit_card_specializing | bird:dev.json:1477 | Which year recorded the most gas use paid in EUR? | SELECT SUBSTRING(T2.Date, 1, 4) FROM customers AS T1 INNER JOIN yearmonth AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.Currency = 'EUR' GROUP BY SUBSTRING(T2.Date, 1, 4) ORDER BY SUM(T2.Consumption) DESC LIMIT 1 | [
"Which",
"year",
"recorded",
"the",
"most",
"gas",
"use",
"paid",
"in",
"EUR",
"?"
] | [
{
"id": 8,
"type": "column",
"value": "consumption"
},
{
"id": 7,
"type": "column",
"value": "customerid"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 1,
"type": "table",
"value": "yearmonth"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
10,785 | video_game | bird:test.json:1941 | What are the maximum and minimum rank of the year of players. | SELECT max(Rank_of_the_year) , min(Rank_of_the_year) FROM player | [
"What",
"are",
"the",
"maximum",
"and",
"minimum",
"rank",
"of",
"the",
"year",
"of",
"players",
"."
] | [
{
"id": 1,
"type": "column",
"value": "rank_of_the_year"
},
{
"id": 0,
"type": "table",
"value": "player"
}
] | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7,
8,
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O"
] |
10,786 | roller_coaster | spider:train_spider.json:6214 | Show the names of roller coasters and names of country they are in. | SELECT T2.Name , T1.Name FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID = T2.Country_ID | [
"Show",
"the",
"names",
"of",
"roller",
"coasters",
"and",
"names",
"of",
"country",
"they",
"are",
"in",
"."
] | [
{
"id": 2,
"type": "table",
"value": "roller_coaster"
},
{
"id": 3,
"type": "column",
"value": "country_id"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
10,787 | music_2 | spider:train_spider.json:5216 | Find all the instruments ever used by the musician with last name "Heilo"? | SELECT instrument FROM instruments AS T1 JOIN Band AS T2 ON T1.bandmateid = T2.id WHERE T2.lastname = "Heilo" | [
"Find",
"all",
"the",
"instruments",
"ever",
"used",
"by",
"the",
"musician",
"with",
"last",
"name",
"\"",
"Heilo",
"\"",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "instruments"
},
{
"id": 0,
"type": "column",
"value": "instrument"
},
{
"id": 5,
"type": "column",
"value": "bandmateid"
},
{
"id": 3,
"type": "column",
"value": "lastname"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_i... | [
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
10,788 | aan_1 | bird:test.json:965 | How many papers do we have? | SELECT count(*) FROM Paper | [
"How",
"many",
"papers",
"do",
"we",
"have",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "paper"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
10,789 | beer_factory | bird:train.json:5326 | How many times did Anna Himes use her Mastercard when paying between 12/25/2014 and 5/20/2016 ? | SELECT COUNT(T2.CustomerID) FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.First = 'Anna' AND T1.Last = 'Himes' AND T2.CreditCardType = 'MasterCard' AND T2.TransactionDate BETWEEN '2014-12-25' AND '2016-05-20' | [
"How",
"many",
"times",
"did",
"Anna",
"Himes",
"use",
"her",
"Mastercard",
"when",
"paying",
"between",
"12/25/2014",
"and",
"5/20/2016",
"?"
] | [
{
"id": 9,
"type": "column",
"value": "transactiondate"
},
{
"id": 7,
"type": "column",
"value": "creditcardtype"
},
{
"id": 1,
"type": "table",
"value": "transaction"
},
{
"id": 2,
"type": "column",
"value": "customerid"
},
{
"id": 8,
"type": ... | [
{
"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": [
4
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,790 | music_2 | spider:train_spider.json:5253 | What are the different names of all songs without back vocals? | SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid = t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid = t2.songid WHERE TYPE = "back" | [
"What",
"are",
"the",
"different",
"names",
"of",
"all",
"songs",
"without",
"back",
"vocals",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "vocals"
},
{
"id": 5,
"type": "column",
"value": "songid"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 2,
"type": "table",
"value": "songs"
},
{
"id": 3,
"type": "column",
"value": "type"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
10,791 | retail_world | bird:train.json:6578 | How many boxes of 'Pavlova' did Northwind sell? | SELECT COUNT(T2.Quantity) FROM Products AS T1 INNER JOIN `Order Details` AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ProductName = 'Pavlova' | [
"How",
"many",
"boxes",
"of",
"'",
"Pavlova",
"'",
"did",
"Northwind",
"sell",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "Order Details"
},
{
"id": 2,
"type": "column",
"value": "productname"
},
{
"id": 5,
"type": "column",
"value": "productid"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O"
] |
10,792 | icfp_1 | spider:train_spider.json:2900 | Retrieve the country that has published the most papers. | SELECT t1.country FROM inst AS t1 JOIN authorship AS t2 ON t1.instid = t2.instid JOIN papers AS t3 ON t2.paperid = t3.paperid GROUP BY t1.country ORDER BY count(*) DESC LIMIT 1 | [
"Retrieve",
"the",
"country",
"that",
"has",
"published",
"the",
"most",
"papers",
"."
] | [
{
"id": 3,
"type": "table",
"value": "authorship"
},
{
"id": 0,
"type": "column",
"value": "country"
},
{
"id": 4,
"type": "column",
"value": "paperid"
},
{
"id": 1,
"type": "table",
"value": "papers"
},
{
"id": 5,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,793 | professional_basketball | bird:train.json:2799 | For team who has more home won than home lost more than 80%, list the team name and the offense points. | SELECT name, o_pts FROM teams WHERE CAST((homeWon - homeLost) AS REAL) * 100 / games > 80 | [
"For",
"team",
"who",
"has",
"more",
"home",
"won",
"than",
"home",
"lost",
"more",
"than",
"80",
"%",
",",
"list",
"the",
"team",
"name",
"and",
"the",
"offense",
"points",
"."
] | [
{
"id": 7,
"type": "column",
"value": "homelost"
},
{
"id": 6,
"type": "column",
"value": "homewon"
},
{
"id": 0,
"type": "table",
"value": "teams"
},
{
"id": 2,
"type": "column",
"value": "o_pts"
},
{
"id": 4,
"type": "column",
"value": "g... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
10,795 | e_learning | spider:train_spider.json:3813 | What are the names and descriptions of the all courses under the "Computer Science" subject? | SELECT T1.course_name , T1.course_description FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id = T2.subject_id WHERE T2.subject_name = "Computer Science" | [
"What",
"are",
"the",
"names",
"and",
"descriptions",
"of",
"the",
"all",
"courses",
"under",
"the",
"\"",
"Computer",
"Science",
"\"",
"subject",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "course_description"
},
{
"id": 5,
"type": "column",
"value": "Computer Science"
},
{
"id": 4,
"type": "column",
"value": "subject_name"
},
{
"id": 0,
"type": "column",
"value": "course_name"
},
{
"id": 6,
... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O"
] |
10,797 | headphone_store | bird:test.json:948 | Count the number of distinct neighborhoods stores are located. | SELECT count(DISTINCT neighborhood) FROM store | [
"Count",
"the",
"number",
"of",
"distinct",
"neighborhoods",
"stores",
"are",
"located",
"."
] | [
{
"id": 1,
"type": "column",
"value": "neighborhood"
},
{
"id": 0,
"type": "table",
"value": "store"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O"
] |
10,798 | activity_1 | spider:train_spider.json:6732 | Count the number of Professors who have office in building NEB. | SELECT count(*) FROM Faculty WHERE Rank = "Professor" AND building = "NEB" | [
"Count",
"the",
"number",
"of",
"Professors",
"who",
"have",
"office",
"in",
"building",
"NEB",
"."
] | [
{
"id": 2,
"type": "column",
"value": "Professor"
},
{
"id": 3,
"type": "column",
"value": "building"
},
{
"id": 0,
"type": "table",
"value": "faculty"
},
{
"id": 1,
"type": "column",
"value": "rank"
},
{
"id": 4,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
10,799 | works_cycles | bird:train.json:7244 | What is the description of the discount for the product with the id "762"? | SELECT T2.Description FROM SpecialOfferProduct AS T1 INNER JOIN SpecialOffer AS T2 ON T1.SpecialOfferID = T2.SpecialOfferID WHERE T1.ProductID = 762 | [
"What",
"is",
"the",
"description",
"of",
"the",
"discount",
"for",
"the",
"product",
"with",
"the",
"i",
"d",
"\"",
"762",
"\"",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "specialofferproduct"
},
{
"id": 5,
"type": "column",
"value": "specialofferid"
},
{
"id": 2,
"type": "table",
"value": "specialoffer"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 3,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
10,800 | retail_complains | bird:train.json:298 | How many cases of billing dispute issues occurred in the Mountain division? | SELECT COUNT(T1.Issue) FROM events AS T1 INNER JOIN client AS T2 ON T1.Client_ID = T2.client_id INNER JOIN district AS T3 ON T2.district_id = T3.district_id WHERE T1.Issue = 'Billing disputes' AND T3.division = 'Mountain' | [
"How",
"many",
"cases",
"of",
"billing",
"dispute",
"issues",
"occurred",
"in",
"the",
"Mountain",
"division",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Billing disputes"
},
{
"id": 4,
"type": "column",
"value": "district_id"
},
{
"id": 8,
"type": "column",
"value": "client_id"
},
{
"id": 0,
"type": "table",
"value": "district"
},
{
"id": 6,
"type": "column... | [
{
"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": [
4,
... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.