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 |
|---|---|---|---|---|---|---|---|---|
110 | boat_1 | bird:test.json:870 | What are the ids of sailors who have not reserved a boat? | SELECT sid FROM Sailors EXCEPT SELECT sid FROM Reserves | [
"What",
"are",
"the",
"ids",
"of",
"sailors",
"who",
"have",
"not",
"reserved",
"a",
"boat",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "reserves"
},
{
"id": 0,
"type": "table",
"value": "sailors"
},
{
"id": 2,
"type": "column",
"value": "sid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
111 | advertising_agencies | bird:test.json:2067 | What is the agency id and details with most number of clients? | SELECT T1.agency_id , T1.agency_details FROM Agencies AS T1 JOIN Clients AS T2 ON T1.agency_id = T2.agency_id GROUP BY T1.agency_id ORDER BY count(*) DESC LIMIT 1 | [
"What",
"is",
"the",
"agency",
"i",
"d",
"and",
"details",
"with",
"most",
"number",
"of",
"clients",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "agency_details"
},
{
"id": 0,
"type": "column",
"value": "agency_id"
},
{
"id": 2,
"type": "table",
"value": "agencies"
},
{
"id": 3,
"type": "table",
"value": "clients"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
3,
4
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
112 | donor | bird:train.json:3223 | Is the donor who donated to school "d4af834b1d3fc8061e1ee1b3f1a77b85" a teacher? | SELECT T2.is_teacher_acct FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.schoolid = 'd4af834b1d3fc8061e1ee1b3f1a77b85' | [
"Is",
"the",
"donor",
"who",
"donated",
"to",
"school",
"\"",
"d4af834b1d3fc8061e1ee1b3f1a77b85",
"\"",
"a",
"teacher",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "d4af834b1d3fc8061e1ee1b3f1a77b85"
},
{
"id": 0,
"type": "column",
"value": "is_teacher_acct"
},
{
"id": 2,
"type": "table",
"value": "donations"
},
{
"id": 5,
"type": "column",
"value": "projectid"
},
{
"id": 1... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O"
] |
113 | performance_attendance | spider:train_spider.json:1321 | List the names of members who did not attend any performance. | SELECT Name FROM member WHERE Member_ID NOT IN (SELECT Member_ID FROM member_attendance) | [
"List",
"the",
"names",
"of",
"members",
"who",
"did",
"not",
"attend",
"any",
"performance",
"."
] | [
{
"id": 3,
"type": "table",
"value": "member_attendance"
},
{
"id": 2,
"type": "column",
"value": "member_id"
},
{
"id": 0,
"type": "table",
"value": "member"
},
{
"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": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O"
] |
114 | book_review | bird:test.json:594 | List the titles of books in ascending alphabetical order. | SELECT Title FROM book ORDER BY Title ASC | [
"List",
"the",
"titles",
"of",
"books",
"in",
"ascending",
"alphabetical",
"order",
"."
] | [
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "table",
"value": "book"
}
] | [
{
"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"
] |
115 | baseball_1 | spider:train_spider.json:3678 | What is the total salary expenses of team Boston Red Stockings in 2010? | SELECT sum(T1.salary) FROM salary AS T1 JOIN team AS T2 ON T1.team_id = T2.team_id_br WHERE T2.name = 'Boston Red Stockings' AND T1.year = 2010 | [
"What",
"is",
"the",
"total",
"salary",
"expenses",
"of",
"team",
"Boston",
"Red",
"Stockings",
"in",
"2010",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "Boston Red Stockings"
},
{
"id": 4,
"type": "column",
"value": "team_id_br"
},
{
"id": 3,
"type": "column",
"value": "team_id"
},
{
"id": 0,
"type": "table",
"value": "salary"
},
{
"id": 2,
"type": "column"... | [
{
"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-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O"
] |
116 | movies_4 | bird:train.json:449 | List the names of the production companies that made at least 200 movies. | SELECT T1.company_name FROM production_company AS T1 INNER JOIN movie_company AS T2 ON T1.company_id = T2.company_id GROUP BY T1.company_id HAVING COUNT(T2.movie_id) > 200 | [
"List",
"the",
"names",
"of",
"the",
"production",
"companies",
"that",
"made",
"at",
"least",
"200",
"movies",
"."
] | [
{
"id": 2,
"type": "table",
"value": "production_company"
},
{
"id": 3,
"type": "table",
"value": "movie_company"
},
{
"id": 1,
"type": "column",
"value": "company_name"
},
{
"id": 0,
"type": "column",
"value": "company_id"
},
{
"id": 5,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
118 | aircraft | spider:train_spider.json:4823 | List the name of the aircraft that has been named winning aircraft the most number of times. | SELECT T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID = T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft ORDER BY COUNT(*) DESC LIMIT 1 | [
"List",
"the",
"name",
"of",
"the",
"aircraft",
"that",
"has",
"been",
"named",
"winning",
"aircraft",
"the",
"most",
"number",
"of",
"times",
"."
] | [
{
"id": 0,
"type": "column",
"value": "winning_aircraft"
},
{
"id": 4,
"type": "column",
"value": "aircraft_id"
},
{
"id": 1,
"type": "column",
"value": "aircraft"
},
{
"id": 2,
"type": "table",
"value": "aircraft"
},
{
"id": 3,
"type": "table"... | [
{
"entity_id": 0,
"token_idxs": [
10,
11
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
119 | store_1 | spider:train_spider.json:582 | In which country does Roberto Almeida? | SELECT country FROM customers WHERE first_name = "Roberto" AND last_name = "Almeida"; | [
"In",
"which",
"country",
"does",
"Roberto",
"Almeida",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 4,
"type": "column",
"value": "last_name"
},
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 3,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
120 | theme_gallery | spider:train_spider.json:1672 | What are the names of artists that have not had any exhibitions? | SELECT name FROM artist WHERE artist_id NOT IN (SELECT artist_id FROM exhibition) | [
"What",
"are",
"the",
"names",
"of",
"artists",
"that",
"have",
"not",
"had",
"any",
"exhibitions",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "exhibition"
},
{
"id": 2,
"type": "column",
"value": "artist_id"
},
{
"id": 0,
"type": "table",
"value": "artist"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
121 | works_cycles | bird:train.json:7304 | Among the salable products from the mountain product line, how many of them have the most reviews? | SELECT SUM(CASE WHEN T2.ProductLine = 'M' THEN 1 ELSE 0 END) FROM ProductReview AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID WHERE T2.FinishedGoodsFlag = 1 GROUP BY T1.ProductID ORDER BY COUNT(T1.ProductReviewID) DESC LIMIT 1 | [
"Among",
"the",
"salable",
"products",
"from",
"the",
"mountain",
"product",
"line",
",",
"how",
"many",
"of",
"them",
"have",
"the",
"most",
"reviews",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "finishedgoodsflag"
},
{
"id": 5,
"type": "column",
"value": "productreviewid"
},
{
"id": 1,
"type": "table",
"value": "productreview"
},
{
"id": 7,
"type": "column",
"value": "productline"
},
{
"id": 0,
"t... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
122 | thrombosis_prediction | bird:dev.json:1235 | What are the patient's diagnosis for those who has lower red blood blood cell? State their ID and age. | SELECT DISTINCT T1.Diagnosis, T1.ID , STRFTIME('%Y', CURRENT_TIMESTAMP) -STRFTIME('%Y', T1.Birthday) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.RBC < 3.5 | [
"What",
"are",
"the",
"patient",
"'s",
"diagnosis",
"for",
"those",
"who",
"has",
"lower",
"red",
"blood",
"blood",
"cell",
"?",
"State",
"their",
"ID",
"and",
"age",
"."
] | [
{
"id": 3,
"type": "table",
"value": "laboratory"
},
{
"id": 0,
"type": "column",
"value": "diagnosis"
},
{
"id": 7,
"type": "column",
"value": "birthday"
},
{
"id": 2,
"type": "table",
"value": "patient"
},
{
"id": 4,
"type": "column",
"va... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
123 | books | bird:train.json:5952 | What is the name of the first book written by J.K Rowling? | SELECT T1.title FROM book AS T1 INNER JOIN book_author AS T2 ON T1.book_id = T2.book_id INNER JOIN author AS T3 ON T3.author_id = T2.author_id WHERE T3.author_name = 'J.K. Rowling' ORDER BY T1.publication_date ASC LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"first",
"book",
"written",
"by",
"J.K",
"Rowling",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "publication_date"
},
{
"id": 3,
"type": "value",
"value": "J.K. Rowling"
},
{
"id": 2,
"type": "column",
"value": "author_name"
},
{
"id": 6,
"type": "table",
"value": "book_author"
},
{
"id": 7,
"type": "... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2,
3
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
124 | movies_4 | bird:train.json:535 | What percentage of films are made in the US? | SELECT CAST(COUNT(CASE WHEN T3.COUNTry_iso_code = 'US' THEN T1.movie_id ELSE NULL END) AS REAL) * 100 / COUNT(T1.movie_id) FROM movie AS T1 INNER JOIN production_COUNTry AS T2 ON T1.movie_id = T2.movie_id INNER JOIN COUNTry AS T3 ON T2.COUNTry_id = T3.COUNTry_id | [
"What",
"percentage",
"of",
"films",
"are",
"made",
"in",
"the",
"US",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "production_country"
},
{
"id": 6,
"type": "column",
"value": "country_iso_code"
},
{
"id": 3,
"type": "column",
"value": "country_id"
},
{
"id": 5,
"type": "column",
"value": "movie_id"
},
{
"id": 0,
"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": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
125 | aan_1 | bird:test.json:1023 | What are the names of the top 3 affiliations that have the most papers in year 2009? | SELECT T3.name FROM Paper AS T1 JOIN Author_list AS T2 ON T1.paper_id = T2.paper_id JOIN Affiliation AS T3 ON T2.affiliation_id = T3.affiliation_id WHERE T1.year = 2009 GROUP BY T2.affiliation_id ORDER BY count(*) DESC LIMIT 3 | [
"What",
"are",
"the",
"names",
"of",
"the",
"top",
"3",
"affiliations",
"that",
"have",
"the",
"most",
"papers",
"in",
"year",
"2009",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "affiliation_id"
},
{
"id": 2,
"type": "table",
"value": "affiliation"
},
{
"id": 6,
"type": "table",
"value": "author_list"
},
{
"id": 7,
"type": "column",
"value": "paper_id"
},
{
"id": 5,
"type": "table"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O"
] |
126 | financial | bird:dev.json:157 | What is the number of committed crimes in 1995 in the district of the account with the id 532? | SELECT T1.A15 FROM district AS T1 INNER JOIN `account` AS T2 ON T1.district_id = T2.district_id WHERE T2.account_id = 532 | [
"What",
"is",
"the",
"number",
"of",
"committed",
"crimes",
"in",
"1995",
"in",
"the",
"district",
"of",
"the",
"account",
"with",
"the",
"i",
"d",
"532",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "district_id"
},
{
"id": 3,
"type": "column",
"value": "account_id"
},
{
"id": 1,
"type": "table",
"value": "district"
},
{
"id": 2,
"type": "table",
"value": "account"
},
{
"id": 0,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
127 | soccer_2016 | bird:train.json:1836 | How many players with left-hand batting style are from India? | SELECT SUM(CASE WHEN T1.Batting_hand = 'Left-hand bat' THEN 1 ELSE 0 END) AS cnt FROM Batting_Style AS T1 INNER JOIN Player AS T2 ON T1.Batting_Id = T2.Batting_hand INNER JOIN Country AS T3 ON T2.Country_Name = T3.Country_Id WHERE T3.Country_Name = 'India' | [
"How",
"many",
"players",
"with",
"left",
"-",
"hand",
"batting",
"style",
"are",
"from",
"India",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "batting_style"
},
{
"id": 10,
"type": "value",
"value": "Left-hand bat"
},
{
"id": 1,
"type": "column",
"value": "country_name"
},
{
"id": 8,
"type": "column",
"value": "batting_hand"
},
{
"id": 5,
"type": ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
129 | car_road_race | bird:test.json:1343 | How many drivers use each constructor? | SELECT CONSTRUCTOR , COUNT(*) FROM driver GROUP BY CONSTRUCTOR | [
"How",
"many",
"drivers",
"use",
"each",
"constructor",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "constructor"
},
{
"id": 0,
"type": "table",
"value": "driver"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
130 | mondial_geo | bird:train.json:8242 | Which country has the biggest percentage of the albanian ethnic group? | SELECT T1.Name FROM country AS T1 INNER JOIN ethnicGroup AS T2 ON T1.Code = T2.Country WHERE T2.Name = 'Albanian' ORDER BY T2.Percentage DESC LIMIT 1 | [
"Which",
"country",
"has",
"the",
"biggest",
"percentage",
"of",
"the",
"albanian",
"ethnic",
"group",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "ethnicgroup"
},
{
"id": 4,
"type": "column",
"value": "percentage"
},
{
"id": 3,
"type": "value",
"value": "Albanian"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 6,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id"... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"B-TABLE",
"I-TABLE",
"O"
] |
131 | products_gen_characteristics | spider:train_spider.json:5581 | What is the description of the product category with the code 'Spices'? | SELECT product_category_description FROM ref_product_categories WHERE product_category_code = "Spices" | [
"What",
"is",
"the",
"description",
"of",
"the",
"product",
"category",
"with",
"the",
"code",
"'",
"Spices",
"'",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "product_category_description"
},
{
"id": 0,
"type": "table",
"value": "ref_product_categories"
},
{
"id": 2,
"type": "column",
"value": "product_category_code"
},
{
"id": 3,
"type": "column",
"value": "Spices"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
6,
7
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
132 | movie_2 | bird:test.json:1831 | Find the titles of all movies sorted by their ratings. | SELECT title FROM movies ORDER BY rating | [
"Find",
"the",
"titles",
"of",
"all",
"movies",
"sorted",
"by",
"their",
"ratings",
"."
] | [
{
"id": 0,
"type": "table",
"value": "movies"
},
{
"id": 2,
"type": "column",
"value": "rating"
},
{
"id": 1,
"type": "column",
"value": "title"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
133 | soccer_2016 | bird:train.json:1890 | Give the player id of the player who was at the non-striker end for the most number of balls in the match 501219. | SELECT Ball_Id FROM Ball_by_Ball WHERE Non_Striker = Ball_Id ORDER BY Ball_Id DESC LIMIT 1 | [
"Give",
"the",
"player",
"i",
"d",
"of",
"the",
"player",
"who",
"was",
"at",
"the",
"non",
"-",
"striker",
"end",
"for",
"the",
"most",
"number",
"of",
"balls",
"in",
"the",
"match",
"501219",
"."
] | [
{
"id": 0,
"type": "table",
"value": "ball_by_ball"
},
{
"id": 2,
"type": "column",
"value": "non_striker"
},
{
"id": 1,
"type": "column",
"value": "ball_id"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
21,
22
]
},
{
"entity_id": 2,
"token_idxs": [
12,
13,
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"e... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
134 | warehouse_1 | bird:test.json:1736 | For each content, what is the total value and number of boxes? | SELECT sum(value) , count(*) , CONTENTS FROM boxes GROUP BY CONTENTS | [
"For",
"each",
"content",
",",
"what",
"is",
"the",
"total",
"value",
"and",
"number",
"of",
"boxes",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "contents"
},
{
"id": 0,
"type": "table",
"value": "boxes"
},
{
"id": 2,
"type": "column",
"value": "value"
}
] | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"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",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
135 | allergy_1 | spider:train_spider.json:504 | How many students are affected by cat allergies? | SELECT count(*) FROM Has_allergy WHERE Allergy = "Cat" | [
"How",
"many",
"students",
"are",
"affected",
"by",
"cat",
"allergies",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "has_allergy"
},
{
"id": 1,
"type": "column",
"value": "allergy"
},
{
"id": 2,
"type": "column",
"value": "Cat"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
136 | restaurant | bird:train.json:1688 | Identify all restaurants in Contra Costa County by id. | SELECT T1.id_restaurant FROM location AS T1 INNER JOIN geographic AS T2 ON T1.city = T2.city WHERE T2.county = 'contra costa county' | [
"Identify",
"all",
"restaurants",
"in",
"Contra",
"Costa",
"County",
"by",
"i",
"d."
] | [
{
"id": 4,
"type": "value",
"value": "contra costa county"
},
{
"id": 0,
"type": "column",
"value": "id_restaurant"
},
{
"id": 2,
"type": "table",
"value": "geographic"
},
{
"id": 1,
"type": "table",
"value": "location"
},
{
"id": 3,
"type": "c... | [
{
"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": [
4,
5
]
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"O",
"O"
] |
137 | election | spider:train_spider.json:2777 | For each party, return the name of the party and the number of delegates from that party. | SELECT T2.Party , COUNT(*) FROM election AS T1 JOIN party AS T2 ON T1.Party = T2.Party_ID GROUP BY T1.Party | [
"For",
"each",
"party",
",",
"return",
"the",
"name",
"of",
"the",
"party",
"and",
"the",
"number",
"of",
"delegates",
"from",
"that",
"party",
"."
] | [
{
"id": 1,
"type": "table",
"value": "election"
},
{
"id": 3,
"type": "column",
"value": "party_id"
},
{
"id": 0,
"type": "column",
"value": "party"
},
{
"id": 2,
"type": "table",
"value": "party"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
138 | wedding | spider:train_spider.json:1634 | Show all opening years and the number of churches that opened in that year. | SELECT open_date , count(*) FROM church GROUP BY open_date | [
"Show",
"all",
"opening",
"years",
"and",
"the",
"number",
"of",
"churches",
"that",
"opened",
"in",
"that",
"year",
"."
] | [
{
"id": 1,
"type": "column",
"value": "open_date"
},
{
"id": 0,
"type": "table",
"value": "church"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
139 | flight_company | spider:train_spider.json:6382 | how many airports are there in each country? | SELECT count(*) , country FROM airport GROUP BY country | [
"how",
"many",
"airports",
"are",
"there",
"in",
"each",
"country",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "airport"
},
{
"id": 1,
"type": "column",
"value": "country"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
140 | game_1 | spider:train_spider.json:5999 | How many sports do we have? | SELECT count(DISTINCT sportname) FROM Sportsinfo | [
"How",
"many",
"sports",
"do",
"we",
"have",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "sportsinfo"
},
{
"id": 1,
"type": "column",
"value": "sportname"
}
] | [
{
"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",
"O",
"O",
"O"
] |
141 | beer_factory | bird:train.json:5297 | Among the customers not subscribed to the mailing list, what percentage has given three or more stars in a review? | SELECT CAST(COUNT(CASE WHEN T2.StarRating > 3 THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T2.CustomerID) FROM customers AS T1 INNER JOIN rootbeerreview AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.SubscribedToEmailList = 'FALSE' | [
"Among",
"the",
"customers",
"not",
"subscribed",
"to",
"the",
"mailing",
"list",
",",
"what",
"percentage",
"has",
"given",
"three",
"or",
"more",
"stars",
"in",
"a",
"review",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "subscribedtoemaillist"
},
{
"id": 1,
"type": "table",
"value": "rootbeerreview"
},
{
"id": 4,
"type": "column",
"value": "customerid"
},
{
"id": 7,
"type": "column",
"value": "starrating"
},
{
"id": 0,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
20
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5,
6,
7,
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O"
] |
142 | hockey | bird:train.json:7782 | What is the average winning rate of the Montreal Canadiens in the Stanley Cup finals? | SELECT SUM(T2.W / T2.G) / SUM(T2.G + T2.W) FROM Teams AS T1 INNER JOIN TeamsSC AS T2 ON T1.tmID = T2.tmID AND T1.year = T2.year WHERE T1.name = 'Montreal Canadiens' | [
"What",
"is",
"the",
"average",
"winning",
"rate",
"of",
"the",
"Montreal",
"Canadiens",
"in",
"the",
"Stanley",
"Cup",
"finals",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Montreal Canadiens"
},
{
"id": 1,
"type": "table",
"value": "teamssc"
},
{
"id": 0,
"type": "table",
"value": "teams"
},
{
"id": 2,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
143 | icfp_1 | spider:train_spider.json:2864 | What are the titles of papers published by "Jeremy Gibbons"? | SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid = t2.authid JOIN papers AS t3 ON t2.paperid = t3.paperid WHERE t1.fname = "Jeremy" AND t1.lname = "Gibbons" | [
"What",
"are",
"the",
"titles",
"of",
"papers",
"published",
"by",
"\"",
"Jeremy",
"Gibbons",
"\"",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "authorship"
},
{
"id": 2,
"type": "table",
"value": "authors"
},
{
"id": 4,
"type": "column",
"value": "paperid"
},
{
"id": 8,
"type": "column",
"value": "Gibbons"
},
{
"id": 1,
"type": "table",
"value"... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O"
] |
144 | movies_4 | bird:train.json:454 | How many horror movies are there? | SELECT COUNT(T1.movie_id) FROM movie_genres AS T1 INNER JOIN genre AS T2 ON T1.genre_id = T2.genre_id WHERE T2.genre_name = 'Horror' | [
"How",
"many",
"horror",
"movies",
"are",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "movie_genres"
},
{
"id": 2,
"type": "column",
"value": "genre_name"
},
{
"id": 4,
"type": "column",
"value": "movie_id"
},
{
"id": 5,
"type": "column",
"value": "genre_id"
},
{
"id": 3,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"B-TABLE",
"O"
] |
145 | architecture | spider:train_spider.json:6960 | How many architects haven't built a mill before year 1850? | SELECT count(*) FROM architect WHERE id NOT IN ( SELECT architect_id FROM mill WHERE built_year < 1850 ); | [
"How",
"many",
"architects",
"have",
"n't",
"built",
"a",
"mill",
"before",
"year",
"1850",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "architect_id"
},
{
"id": 4,
"type": "column",
"value": "built_year"
},
{
"id": 0,
"type": "table",
"value": "architect"
},
{
"id": 2,
"type": "table",
"value": "mill"
},
{
"id": 5,
"type": "value",
"va... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5,
6
]
},
{
"entity_id":... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
146 | government_shift | bird:test.json:355 | List details of all the channel in alphabetical order . | select channel_details from channels order by channel_details | [
"List",
"details",
"of",
"all",
"the",
"channel",
"in",
"alphabetical",
"order",
"."
] | [
{
"id": 1,
"type": "column",
"value": "channel_details"
},
{
"id": 0,
"type": "table",
"value": "channels"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
147 | program_share | spider:train_spider.json:3766 | Find the name of the program that is broadcast most frequently. | SELECT t1.name FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id = t2.program_id GROUP BY t2.program_id ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"name",
"of",
"the",
"program",
"that",
"is",
"broadcast",
"most",
"frequently",
"."
] | [
{
"id": 0,
"type": "column",
"value": "program_id"
},
{
"id": 3,
"type": "table",
"value": "broadcast"
},
{
"id": 2,
"type": "table",
"value": "program"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
148 | public_review_platform | bird:train.json:3852 | Among the Yelp_Businesses in Arizona, how many of them do not provide alcohol? | SELECT COUNT(T2.business_id) FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T1.attribute_name LIKE 'Alcohol' AND T2.attribute_value LIKE 'none' AND T3.state LIKE 'AZ' | [
"Among",
"the",
"Yelp_Businesses",
"in",
"Arizona",
",",
"how",
"many",
"of",
"them",
"do",
"not",
"provide",
"alcohol",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "business_attributes"
},
{
"id": 6,
"type": "column",
"value": "attribute_value"
},
{
"id": 4,
"type": "column",
"value": "attribute_name"
},
{
"id": 10,
"type": "column",
"value": "attribute_id"
},
{
"id": 1,
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
149 | book_publishing_company | bird:train.json:182 | Which employee has the lowest job level. State the first name, last name and when he /she was hired. | SELECT fname, lname, hire_date FROM employee ORDER BY job_lvl LIMIT 1 | [
"Which",
"employee",
"has",
"the",
"lowest",
"job",
"level",
".",
"State",
"the",
"first",
"name",
",",
"last",
"name",
"and",
"when",
"he",
"/she",
"was",
"hired",
"."
] | [
{
"id": 3,
"type": "column",
"value": "hire_date"
},
{
"id": 0,
"type": "table",
"value": "employee"
},
{
"id": 4,
"type": "column",
"value": "job_lvl"
},
{
"id": 1,
"type": "column",
"value": "fname"
},
{
"id": 2,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
20
]
},
{
"entity_id": 4,
"token_idxs": [
5,
6
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
150 | student_loan | bird:train.json:4564 | List the names of the disabled students who were absent from school for more than 5 months. | SELECT T1.name FROM longest_absense_from_school AS T1 INNER JOIN disabled AS T2 ON T1.name = T2.name WHERE T1.month > 5 | [
"List",
"the",
"names",
"of",
"the",
"disabled",
"students",
"who",
"were",
"absent",
"from",
"school",
"for",
"more",
"than",
"5",
"months",
"."
] | [
{
"id": 1,
"type": "table",
"value": "longest_absense_from_school"
},
{
"id": 2,
"type": "table",
"value": "disabled"
},
{
"id": 3,
"type": "column",
"value": "month"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "value"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
9,
10,
11
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
151 | student_loan | bird:train.json:4519 | State the number of male students who do not have payment due. | SELECT COUNT(T1.name) FROM no_payment_due AS T1 INNER JOIN male AS T2 ON T2.name = T1.name WHERE T1.bool = 'pos' | [
"State",
"the",
"number",
"of",
"male",
"students",
"who",
"do",
"not",
"have",
"payment",
"due",
"."
] | [
{
"id": 0,
"type": "table",
"value": "no_payment_due"
},
{
"id": 1,
"type": "table",
"value": "male"
},
{
"id": 2,
"type": "column",
"value": "bool"
},
{
"id": 4,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"value": "po... | [
{
"entity_id": 0,
"token_idxs": [
10,
11
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
153 | thrombosis_prediction | bird:dev.json:1251 | How many patients with an Ig G higher than normal? | SELECT COUNT(DISTINCT T1.ID) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID INNER JOIN Examination AS T3 ON T3.ID = T2.ID WHERE T2.IGG >= 2000 | [
"How",
"many",
"patients",
"with",
"an",
"Ig",
"G",
"higher",
"than",
"normal",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "examination"
},
{
"id": 5,
"type": "table",
"value": "laboratory"
},
{
"id": 4,
"type": "table",
"value": "patient"
},
{
"id": 2,
"type": "value",
"value": "2000"
},
{
"id": 1,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
154 | bakery_1 | bird:test.json:1588 | What are the ids of customers who spend more than 5 on average for each good? | SELECT T3.CustomerId FROM goods AS T1 JOIN items AS T2 ON T1.id = T2.item JOIN receipts AS T3 ON T2.receipt = T3.ReceiptNumber GROUP BY T3.CustomerId HAVING avg(T1.price) > 5 | [
"What",
"are",
"the",
"ids",
"of",
"customers",
"who",
"spend",
"more",
"than",
"5",
"on",
"average",
"for",
"each",
"good",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "receiptnumber"
},
{
"id": 0,
"type": "column",
"value": "customerid"
},
{
"id": 1,
"type": "table",
"value": "receipts"
},
{
"id": 5,
"type": "column",
"value": "receipt"
},
{
"id": 3,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
155 | movie_platform | bird:train.json:99 | Please list the names of the movies that user 94978 scored as 5. | SELECT T2.movie_title FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T1.rating_score = 5 AND T1.user_id = 94978 | [
"Please",
"list",
"the",
"names",
"of",
"the",
"movies",
"that",
"user",
"94978",
"scored",
"as",
"5",
"."
] | [
{
"id": 4,
"type": "column",
"value": "rating_score"
},
{
"id": 0,
"type": "column",
"value": "movie_title"
},
{
"id": 3,
"type": "column",
"value": "movie_id"
},
{
"id": 1,
"type": "table",
"value": "ratings"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"B-VALUE",
"O"
] |
156 | works_cycles | bird:train.json:7324 | Among the employees in Adventure Works, calculate the percentage of them working as sales representatives. | SELECT CAST(SUM(CASE WHEN JobTitle = 'Sales Representative' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(BusinessEntityID) FROM Employee | [
"Among",
"the",
"employees",
"in",
"Adventure",
"Works",
",",
"calculate",
"the",
"percentage",
"of",
"them",
"working",
"as",
"sales",
"representatives",
"."
] | [
{
"id": 6,
"type": "value",
"value": "Sales Representative"
},
{
"id": 2,
"type": "column",
"value": "businessentityid"
},
{
"id": 0,
"type": "table",
"value": "employee"
},
{
"id": 5,
"type": "column",
"value": "jobtitle"
},
{
"id": 1,
"type":... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
157 | world | bird:train.json:7895 | How many countries have no GNP? | SELECT COUNT(*) FROM Country WHERE GNP = 0 | [
"How",
"many",
"countries",
"have",
"no",
"GNP",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 1,
"type": "column",
"value": "gnp"
},
{
"id": 2,
"type": "value",
"value": "0"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O"
] |
159 | airline | bird:train.json:5849 | Tell the number of flights that landed at Lake Charles Regional Airport on 2018/8/15. | SELECT COUNT(T1.Code) FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/15' AND T1.Description = 'Lake Charles, LA: Lake Charles Regional' | [
"Tell",
"the",
"number",
"of",
"flights",
"that",
"landed",
"at",
"Lake",
"Charles",
"Regional",
"Airport",
"on",
"2018/8/15",
"."
] | [
{
"id": 7,
"type": "value",
"value": "Lake Charles, LA: Lake Charles Regional"
},
{
"id": 6,
"type": "column",
"value": "description"
},
{
"id": 5,
"type": "value",
"value": "2018/8/15"
},
{
"id": 0,
"type": "table",
"value": "airports"
},
{
"id": ... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"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-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"B-VALUE",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
160 | world | bird:train.json:7840 | Among the countries that officially use the English language, what country has the highest capital? | SELECT T1.Code FROM Country AS T1 INNER JOIN CountryLanguage AS T2 ON T1.Code = T2.CountryCode WHERE T2.Language = 'English' AND T2.IsOfficial = 'T' ORDER BY T1.Capital DESC LIMIT 1 | [
"Among",
"the",
"countries",
"that",
"officially",
"use",
"the",
"English",
"language",
",",
"what",
"country",
"has",
"the",
"highest",
"capital",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "countrylanguage"
},
{
"id": 4,
"type": "column",
"value": "countrycode"
},
{
"id": 7,
"type": "column",
"value": "isofficial"
},
{
"id": 5,
"type": "column",
"value": "language"
},
{
"id": 1,
"type": "table... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs"... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
161 | music_platform_2 | bird:train.json:7985 | How many ratings of 5 have been given to the podcast "Please Excuse My Dead Aunt Sally"? | SELECT COUNT(T2.rating) FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.title = 'Please Excuse My Dead Aunt Sally' AND T2.rating = 5 | [
"How",
"many",
"ratings",
"of",
"5",
"have",
"been",
"given",
"to",
"the",
"podcast",
"\"",
"Please",
"Excuse",
"My",
"Dead",
"Aunt",
"Sally",
"\"",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Please Excuse My Dead Aunt Sally"
},
{
"id": 3,
"type": "column",
"value": "podcast_id"
},
{
"id": 0,
"type": "table",
"value": "podcasts"
},
{
"id": 1,
"type": "table",
"value": "reviews"
},
{
"id": 2,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
162 | restaurant | bird:train.json:1707 | How many American food restaurants are unpopular in Carmel? | SELECT COUNT(id_restaurant) FROM generalinfo WHERE food_type = 'american' AND city = 'carmel' AND review = ( SELECT MIN(review) FROM generalinfo WHERE food_type = 'american' AND city = 'carmel' ) | [
"How",
"many",
"American",
"food",
"restaurants",
"are",
"unpopular",
"in",
"Carmel",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "id_restaurant"
},
{
"id": 0,
"type": "table",
"value": "generalinfo"
},
{
"id": 2,
"type": "column",
"value": "food_type"
},
{
"id": 3,
"type": "value",
"value": "american"
},
{
"id": 5,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-VALUE",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
163 | retail_complains | bird:train.json:254 | Which division is Diesel Galloway in? | SELECT T2.division FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T1.first = 'Diesel' AND T1.last = 'Galloway' | [
"Which",
"division",
"is",
"Diesel",
"Galloway",
"in",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "district_id"
},
{
"id": 0,
"type": "column",
"value": "division"
},
{
"id": 2,
"type": "table",
"value": "district"
},
{
"id": 7,
"type": "value",
"value": "Galloway"
},
{
"id": 1,
"type": "table",
"va... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"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": [
3
... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O"
] |
164 | wedding | spider:train_spider.json:1648 | How many churches have a wedding in year 2016? | SELECT COUNT (DISTINCT church_id) FROM wedding WHERE YEAR = 2016 | [
"How",
"many",
"churches",
"have",
"a",
"wedding",
"in",
"year",
"2016",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "church_id"
},
{
"id": 0,
"type": "table",
"value": "wedding"
},
{
"id": 1,
"type": "column",
"value": "year"
},
{
"id": 2,
"type": "value",
"value": "2016"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
165 | card_games | bird:dev.json:495 | What was the release date for the set which card "Evacuation" in it? | SELECT T2.releaseDate FROM cards AS T1 INNER JOIN sets AS T2 ON T2.code = T1.setCode WHERE T1.name = 'Evacuation' | [
"What",
"was",
"the",
"release",
"date",
"for",
"the",
"set",
"which",
"card",
"\"",
"Evacuation",
"\"",
"in",
"it",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "releasedate"
},
{
"id": 4,
"type": "value",
"value": "Evacuation"
},
{
"id": 6,
"type": "column",
"value": "setcode"
},
{
"id": 1,
"type": "table",
"value": "cards"
},
{
"id": 2,
"type": "table",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
166 | law_episode | bird:train.json:1302 | What is the name of the actors born in the USA? | SELECT name FROM Person WHERE birth_country = 'USA' | [
"What",
"is",
"the",
"name",
"of",
"the",
"actors",
"born",
"in",
"the",
"USA",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "birth_country"
},
{
"id": 0,
"type": "table",
"value": "person"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"value": "USA"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
167 | csu_1 | spider:train_spider.json:2355 | What is the average fee for a CSU campus in the year of 2005? | SELECT avg(campusfee) FROM csu_fees WHERE YEAR = 2005 | [
"What",
"is",
"the",
"average",
"fee",
"for",
"a",
"CSU",
"campus",
"in",
"the",
"year",
"of",
"2005",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "campusfee"
},
{
"id": 0,
"type": "table",
"value": "csu_fees"
},
{
"id": 1,
"type": "column",
"value": "year"
},
{
"id": 2,
"type": "value",
"value": "2005"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
168 | codebase_community | bird:dev.json:691 | Identify the number of adult users who have cast over 5000 upvotes. | SELECT COUNT(Id) FROM users WHERE Age BETWEEN 19 AND 65 AND UpVotes > 5000 | [
"Identify",
"the",
"number",
"of",
"adult",
"users",
"who",
"have",
"cast",
"over",
"5000",
"upvotes",
"."
] | [
{
"id": 5,
"type": "column",
"value": "upvotes"
},
{
"id": 0,
"type": "table",
"value": "users"
},
{
"id": 6,
"type": "value",
"value": "5000"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 1,
"type": "column",
"value": "id"
},... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
11
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
169 | student_loan | bird:train.json:4387 | Which school is student829 enrolled in? | SELECT school FROM enrolled WHERE name = 'student829' | [
"Which",
"school",
"is",
"student829",
"enrolled",
"in",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "student829"
},
{
"id": 0,
"type": "table",
"value": "enrolled"
},
{
"id": 1,
"type": "column",
"value": "school"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O"
] |
170 | formula_1 | bird:dev.json:924 | Please list the exact dates on which a Formula_1 race took place on the Barcelona-Catalunya circuit. | SELECT T2.date FROM circuits AS T1 INNER JOIN races AS T2 ON T2.circuitID = T1.circuitId WHERE T1.name = 'Circuit de Barcelona-Catalunya' | [
"Please",
"list",
"the",
"exact",
"dates",
"on",
"which",
"a",
"Formula_1",
"race",
"took",
"place",
"on",
"the",
"Barcelona",
"-",
"Catalunya",
"circuit",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Circuit de Barcelona-Catalunya"
},
{
"id": 5,
"type": "column",
"value": "circuitid"
},
{
"id": 1,
"type": "table",
"value": "circuits"
},
{
"id": 2,
"type": "table",
"value": "races"
},
{
"id": 0,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13,
14,
15,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"O"
] |
171 | public_review_platform | bird:train.json:3930 | Find the location of businesses that has business hours from 9 am to 9 pm every Saturday. | SELECT T1.city FROM Business AS T1 INNER JOIN Business_Hours AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T2.closing_time LIKE '9PM' AND T2.opening_time LIKE '9AM' AND T3.day_of_week LIKE 'Saturday' GROUP BY T1.city | [
"Find",
"the",
"location",
"of",
"businesses",
"that",
"has",
"business",
"hours",
"from",
"9",
"am",
"to",
"9",
"pm",
"every",
"Saturday",
"."
] | [
{
"id": 3,
"type": "table",
"value": "business_hours"
},
{
"id": 5,
"type": "column",
"value": "closing_time"
},
{
"id": 7,
"type": "column",
"value": "opening_time"
},
{
"id": 9,
"type": "column",
"value": "day_of_week"
},
{
"id": 11,
"type": ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O"
] |
173 | cs_semester | bird:train.json:933 | In students with a grade of B, how many of them have an intellegence level of 3? | SELECT COUNT(T1.student_id) FROM registration AS T1 INNER JOIN student AS T2 ON T1.student_id = T2.student_id WHERE T1.grade = 'B' AND T2.intelligence = 3 | [
"In",
"students",
"with",
"a",
"grade",
"of",
"B",
",",
"how",
"many",
"of",
"them",
"have",
"an",
"intellegence",
"level",
"of",
"3",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "registration"
},
{
"id": 5,
"type": "column",
"value": "intelligence"
},
{
"id": 2,
"type": "column",
"value": "student_id"
},
{
"id": 1,
"type": "table",
"value": "student"
},
{
"id": 3,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"... | [
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
174 | airline | bird:train.json:5859 | How many flights depart to Hartsfield-Jackson Atlanta International from Chicago O'Hare International? | SELECT COUNT(FL_DATE) FROM Airlines WHERE ORIGIN = ( SELECT T2.ORIGIN FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Chicago, IL: Chicago O''Hare International' ) AND DEST = ( SELECT T4.DEST FROM Airports AS T3 INNER JOIN Airlines AS T4 ON T3.Code = T4.DEST WHERE T3.Description = 'Atlanta, GA: Hartsfield-Jackson Atlanta International' ) | [
"How",
"many",
"flights",
"depart",
"to",
"Hartsfield",
"-",
"Jackson",
"Atlanta",
"International",
"from",
"Chicago",
"O'Hare",
"International",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "Atlanta, GA: Hartsfield-Jackson Atlanta International"
},
{
"id": 6,
"type": "value",
"value": "Chicago, IL: Chicago O'Hare International"
},
{
"id": 5,
"type": "column",
"value": "description"
},
{
"id": 0,
"type": "table... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"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-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
175 | car_road_race | bird:test.json:1344 | List the most common type of engine used by drivers. | SELECT Engine FROM driver GROUP BY Engine ORDER BY COUNT(*) DESC LIMIT 1 | [
"List",
"the",
"most",
"common",
"type",
"of",
"engine",
"used",
"by",
"drivers",
"."
] | [
{
"id": 0,
"type": "table",
"value": "driver"
},
{
"id": 1,
"type": "column",
"value": "engine"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
176 | retail_world | bird:train.json:6407 | What territories is the Inside Sales Coordinator in charge of? | SELECT T3.TerritoryDescription FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID WHERE T1.Title = 'Inside Sales Coordinator' | [
"What",
"territories",
"is",
"the",
"Inside",
"Sales",
"Coordinator",
"in",
"charge",
"of",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Inside Sales Coordinator"
},
{
"id": 0,
"type": "column",
"value": "territorydescription"
},
{
"id": 5,
"type": "table",
"value": "employeeterritories"
},
{
"id": 1,
"type": "table",
"value": "territories"
},
{
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O"
] |
177 | soccer_2016 | bird:train.json:2009 | How many players are Indians? | SELECT COUNT(T1.Player_Id) FROM Player AS T1 INNER JOIN Country AS T2 ON T1.Country_Name = T2.Country_ID WHERE T2.Country_Name = 'India' | [
"How",
"many",
"players",
"are",
"Indians",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "country_name"
},
{
"id": 5,
"type": "column",
"value": "country_id"
},
{
"id": 4,
"type": "column",
"value": "player_id"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
178 | cre_Doc_Tracking_DB | spider:train_spider.json:4232 | Show the location name and code with the least documents. | SELECT T2.location_name , T1.location_code FROM Document_locations AS T1 JOIN Ref_locations AS T2 ON T1.location_code = T2.location_code GROUP BY T1.location_code ORDER BY count(*) ASC LIMIT 1 | [
"Show",
"the",
"location",
"name",
"and",
"code",
"with",
"the",
"least",
"documents",
"."
] | [
{
"id": 2,
"type": "table",
"value": "document_locations"
},
{
"id": 0,
"type": "column",
"value": "location_code"
},
{
"id": 1,
"type": "column",
"value": "location_name"
},
{
"id": 3,
"type": "table",
"value": "ref_locations"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
179 | movie_3 | bird:train.json:9389 | List at least 10 films that the customers can rent for more than 5 days. | SELECT T.title FROM ( SELECT T1.title, COUNT(T3.customer_id) AS num FROM film AS T1 INNER JOIN inventory AS T2 ON T1.film_id = T2.film_id INNER JOIN rental AS T3 ON T2.inventory_id = T3.inventory_id WHERE T1.rental_duration > 5 GROUP BY T1.title ) AS T WHERE T.num > 10 | [
"List",
"at",
"least",
"10",
"films",
"that",
"the",
"customers",
"can",
"rent",
"for",
"more",
"than",
"5",
"days",
"."
] | [
{
"id": 4,
"type": "column",
"value": "rental_duration"
},
{
"id": 9,
"type": "column",
"value": "inventory_id"
},
{
"id": 6,
"type": "column",
"value": "customer_id"
},
{
"id": 8,
"type": "table",
"value": "inventory"
},
{
"id": 10,
"type": "c... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
180 | professional_basketball | bird:train.json:2874 | Among the Most improved Players awarded from 1985-1990, how many player whose country is USA? | SELECT COUNT(DISTINCT T2.playerID) FROM awards_players AS T1 INNER JOIN players AS T2 ON T1.playerID = T2.playerID WHERE T1.award = 'Most Improved Player' AND T2.birthCountry = 'USA' AND T1.year BETWEEN 1985 AND 1990 | [
"Among",
"the",
"Most",
"improved",
"Players",
"awarded",
"from",
"1985",
"-",
"1990",
",",
"how",
"many",
"player",
"whose",
"country",
"is",
"USA",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Most Improved Player"
},
{
"id": 0,
"type": "table",
"value": "awards_players"
},
{
"id": 5,
"type": "column",
"value": "birthcountry"
},
{
"id": 2,
"type": "column",
"value": "playerid"
},
{
"id": 1,
"type... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
2,
3
]
},
{
"entity_id":... | [
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
181 | aircraft | spider:train_spider.json:4798 | What is the number of aircraft? | SELECT count(*) FROM aircraft | [
"What",
"is",
"the",
"number",
"of",
"aircraft",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "aircraft"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
182 | computer_student | bird:train.json:1037 | Which courses were taught by a professor who is not a faculty member? | SELECT DISTINCT T2.course_id FROM person AS T1 INNER JOIN taughtBy AS T2 ON T1.p_id = T2.p_id WHERE T1.professor = 1 AND T1.hasPosition = 0 | [
"Which",
"courses",
"were",
"taught",
"by",
"a",
"professor",
"who",
"is",
"not",
"a",
"faculty",
"member",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "hasposition"
},
{
"id": 0,
"type": "column",
"value": "course_id"
},
{
"id": 4,
"type": "column",
"value": "professor"
},
{
"id": 2,
"type": "table",
"value": "taughtby"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3,
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id":... | [
"O",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
183 | regional_sales | bird:train.json:2704 | Which regions have online sales channels that have the most discounts? | SELECT T2.Region FROM `Sales Orders` AS T1 INNER JOIN `Sales Team` AS T2 ON T2.SalesTeamID = T1._SalesTeamID WHERE T1.`Sales Channel` = 'Online' ORDER BY T1.`Discount Applied` DESC LIMIT 1 | [
"Which",
"regions",
"have",
"online",
"sales",
"channels",
"that",
"have",
"the",
"most",
"discounts",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "Discount Applied"
},
{
"id": 3,
"type": "column",
"value": "Sales Channel"
},
{
"id": 1,
"type": "table",
"value": "Sales Orders"
},
{
"id": 7,
"type": "column",
"value": "_salesteamid"
},
{
"id": 6,
"type... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-TABLE",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
184 | works_cycles | bird:train.json:7157 | For the document Control Assistant who was born on 1975/12/25, how many private documents did he/she have? | SELECT COUNT(T2.BusinessEntityID) FROM Document AS T1 INNER JOIN Employee AS T2 ON T1.Owner = T2.BusinessEntityID WHERE T2.JobTitle = 'Document Control Assistant' AND T2.BirthDate = '1975-12-25' AND T1.DocumentSummary IS NULL | [
"For",
"the",
"document",
"Control",
"Assistant",
"who",
"was",
"born",
"on",
"1975/12/25",
",",
"how",
"many",
"private",
"documents",
"did",
"he",
"/",
"she",
"have",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Document Control Assistant"
},
{
"id": 2,
"type": "column",
"value": "businessentityid"
},
{
"id": 8,
"type": "column",
"value": "documentsummary"
},
{
"id": 7,
"type": "value",
"value": "1975-12-25"
},
{
"id":... | [
{
"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": [
3,
... | [
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
185 | club_1 | spider:train_spider.json:4280 | What is the description of the club named "Tennis Club"? | SELECT clubdesc FROM club WHERE clubname = "Tennis Club" | [
"What",
"is",
"the",
"description",
"of",
"the",
"club",
"named",
"\"",
"Tennis",
"Club",
"\"",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "Tennis Club"
},
{
"id": 1,
"type": "column",
"value": "clubdesc"
},
{
"id": 2,
"type": "column",
"value": "clubname"
},
{
"id": 0,
"type": "table",
"value": "club"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6,
7
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O"
] |
186 | university_basketball | spider:train_spider.json:999 | What is the total and minimum enrollment of all schools? | SELECT sum(enrollment) , min(enrollment) FROM university | [
"What",
"is",
"the",
"total",
"and",
"minimum",
"enrollment",
"of",
"all",
"schools",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "university"
},
{
"id": 1,
"type": "column",
"value": "enrollment"
}
] | [
{
"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": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
187 | retails | bird:train.json:6834 | What are the top 5 nations of suppliers with the lowest account balance? | SELECT T2.n_name FROM supplier AS T1 INNER JOIN nation AS T2 ON T1.s_nationkey = T2.n_nationkey ORDER BY T1.s_acctbal LIMIT 1 | [
"What",
"are",
"the",
"top",
"5",
"nations",
"of",
"suppliers",
"with",
"the",
"lowest",
"account",
"balance",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "s_nationkey"
},
{
"id": 5,
"type": "column",
"value": "n_nationkey"
},
{
"id": 3,
"type": "column",
"value": "s_acctbal"
},
{
"id": 1,
"type": "table",
"value": "supplier"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
11,
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
189 | university_basketball | spider:train_spider.json:1012 | What are the names of teams from universities that have a below average enrollment? | SELECT t2.team_name FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id = t2.school_id WHERE enrollment < (SELECT avg(enrollment) FROM university) | [
"What",
"are",
"the",
"names",
"of",
"teams",
"from",
"universities",
"that",
"have",
"a",
"below",
"average",
"enrollment",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "basketball_match"
},
{
"id": 1,
"type": "table",
"value": "university"
},
{
"id": 3,
"type": "column",
"value": "enrollment"
},
{
"id": 0,
"type": "column",
"value": "team_name"
},
{
"id": 4,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
190 | club_1 | spider:train_spider.json:4297 | Find the first name and last name for the "CTO" of the club "Hopkins Student Enterprises"? | SELECT t3.fname , t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid = t2.clubid JOIN student AS t3 ON t2.stuid = t3.stuid WHERE t1.clubname = "Hopkins Student Enterprises" AND t2.position = "CTO" | [
"Find",
"the",
"first",
"name",
"and",
"last",
"name",
"for",
"the",
"\"",
"CTO",
"\"",
"of",
"the",
"club",
"\"",
"Hopkins",
"Student",
"Enterprises",
"\"",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "Hopkins Student Enterprises"
},
{
"id": 4,
"type": "table",
"value": "member_of_club"
},
{
"id": 6,
"type": "column",
"value": "clubname"
},
{
"id": 8,
"type": "column",
"value": "position"
},
{
"id": 2,
"... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-COLUMN",
"B-COLUMN",
"O",
"O"
] |
191 | toxicology | bird:dev.json:339 | List the atom ID of the carcinogenic molecule that contains oxygen? | SELECT T1.atom_id FROM atom AS T1 INNER JOIN molecule AS T2 ON T1.molecule_id = T2.molecule_id WHERE T1.element = 'o' AND T2.label = '+' | [
"List",
"the",
"atom",
"ID",
"of",
"the",
"carcinogenic",
"molecule",
"that",
"contains",
"oxygen",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "molecule_id"
},
{
"id": 2,
"type": "table",
"value": "molecule"
},
{
"id": 0,
"type": "column",
"value": "atom_id"
},
{
"id": 4,
"type": "column",
"value": "element"
},
{
"id": 6,
"type": "column",
"va... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
192 | college_1 | spider:train_spider.json:3277 | What is the last name and office of the professor from the history department? | SELECT T1.emp_lname , T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num = T2.emp_num JOIN department AS T3 ON T2.dept_code = T3.dept_code WHERE T3.dept_name = 'History' | [
"What",
"is",
"the",
"last",
"name",
"and",
"office",
"of",
"the",
"professor",
"from",
"the",
"history",
"department",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "prof_office"
},
{
"id": 2,
"type": "table",
"value": "department"
},
{
"id": 0,
"type": "column",
"value": "emp_lname"
},
{
"id": 3,
"type": "column",
"value": "dept_name"
},
{
"id": 6,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entit... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
193 | works_cycles | bird:train.json:7014 | List down the product name, reviewer name, rating and comments for product under the road line. | SELECT T1.Name, T2.ReviewerName, T2.Rating, T2.Comments FROM Product AS T1 INNER JOIN ProductReview AS T2 USING (productID) WHERE T1.ProductLine = 'R' | [
"List",
"down",
"the",
"product",
"name",
",",
"reviewer",
"name",
",",
"rating",
"and",
"comments",
"for",
"product",
"under",
"the",
"road",
"line",
"."
] | [
{
"id": 5,
"type": "table",
"value": "productreview"
},
{
"id": 1,
"type": "column",
"value": "reviewername"
},
{
"id": 6,
"type": "column",
"value": "productline"
},
{
"id": 3,
"type": "column",
"value": "comments"
},
{
"id": 4,
"type": "table... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
3
... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
194 | thrombosis_prediction | bird:dev.json:1304 | Among the patients with a normal blood glucose, how many of them don't have thrombosis? | SELECT COUNT(T1.ID) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID INNER JOIN Examination AS T3 ON T1.ID = T3.ID WHERE T2.GLU < 180 AND T3.Thrombosis = 0 | [
"Among",
"the",
"patients",
"with",
"a",
"normal",
"blood",
"glucose",
",",
"how",
"many",
"of",
"them",
"do",
"n't",
"have",
"thrombosis",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "examination"
},
{
"id": 3,
"type": "table",
"value": "laboratory"
},
{
"id": 6,
"type": "column",
"value": "thrombosis"
},
{
"id": 2,
"type": "table",
"value": "patient"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
195 | card_games | bird:dev.json:362 | What is the description about the ruling of card "Condemn"? | SELECT T2.text FROM cards AS T1 INNER JOIN rulings AS T2 ON T1.uuid = T2.uuid WHERE T1.name = 'Condemn' | [
"What",
"is",
"the",
"description",
"about",
"the",
"ruling",
"of",
"card",
"\"",
"Condemn",
"\"",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "rulings"
},
{
"id": 4,
"type": "value",
"value": "Condemn"
},
{
"id": 1,
"type": "table",
"value": "cards"
},
{
"id": 0,
"type": "column",
"value": "text"
},
{
"id": 3,
"type": "column",
"value": "name"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O"
] |
196 | hr_1 | spider:train_spider.json:3471 | return the smallest salary for every departments. | SELECT MIN(salary) , department_id FROM employees GROUP BY department_id | [
"return",
"the",
"smallest",
"salary",
"for",
"every",
"departments",
"."
] | [
{
"id": 1,
"type": "column",
"value": "department_id"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 2,
"type": "column",
"value": "salary"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
197 | college_3 | spider:train_spider.json:4669 | Find the first names of faculties of rank Professor in alphabetic order. | SELECT Fname FROM FACULTY WHERE Rank = "Professor" ORDER BY Fname | [
"Find",
"the",
"first",
"names",
"of",
"faculties",
"of",
"rank",
"Professor",
"in",
"alphabetic",
"order",
"."
] | [
{
"id": 3,
"type": "column",
"value": "Professor"
},
{
"id": 0,
"type": "table",
"value": "faculty"
},
{
"id": 1,
"type": "column",
"value": "fname"
},
{
"id": 2,
"type": "column",
"value": "rank"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
198 | retail_world | bird:train.json:6664 | When was the employee who handled order id 10281 hired? | SELECT T1.HireDate FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T2.OrderID = 10281 | [
"When",
"was",
"the",
"employee",
"who",
"handled",
"order",
"i",
"d",
"10281",
"hired",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "employeeid"
},
{
"id": 1,
"type": "table",
"value": "employees"
},
{
"id": 0,
"type": "column",
"value": "hiredate"
},
{
"id": 3,
"type": "column",
"value": "orderid"
},
{
"id": 2,
"type": "table",
"va... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
"entity_id": 4,
"token_idxs": [
9
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"B-COLUMN",
"O"
] |
199 | synthea | bird:train.json:1540 | Who is the patient with a body weight of 61.97 kg? | SELECT T2.first, T2.last FROM observations AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T1.DESCRIPTION = 'Body Weight' AND T1.UNITS = 'kg' AND T1.VALUE = 61.97 | [
"Who",
"is",
"the",
"patient",
"with",
"a",
"body",
"weight",
"of",
"61.97",
"kg",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "observations"
},
{
"id": 5,
"type": "column",
"value": "description"
},
{
"id": 6,
"type": "value",
"value": "Body Weight"
},
{
"id": 3,
"type": "table",
"value": "patients"
},
{
"id": 4,
"type": "column",
... | [
{
"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": [
3
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"B-VALUE",
"O"
] |
200 | mountain_photos | spider:train_spider.json:3723 | How many distinct kinds of camera lenses are used to take photos of mountains in the country 'Ethiopia'? | SELECT count(DISTINCT T2.camera_lens_id) FROM mountain AS T1 JOIN photos AS T2 ON T1.id = T2.mountain_id WHERE T1.country = 'Ethiopia' | [
"How",
"many",
"distinct",
"kinds",
"of",
"camera",
"lenses",
"are",
"used",
"to",
"take",
"photos",
"of",
"mountains",
"in",
"the",
"country",
"'",
"Ethiopia",
"'",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "camera_lens_id"
},
{
"id": 6,
"type": "column",
"value": "mountain_id"
},
{
"id": 0,
"type": "table",
"value": "mountain"
},
{
"id": 3,
"type": "value",
"value": "Ethiopia"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": [
18
]
},
{
"entity_id": 4,
"token_idxs": [
5,
6
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
201 | regional_sales | bird:train.json:2697 | What percentage of sell orders on 04/04/2020 were for the state of New York? | SELECT CAST(SUM(CASE WHEN T2.State = 'New York' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.OrderNumber) FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID WHERE T1.OrderDate = '4/4/20' | [
"What",
"percentage",
"of",
"sell",
"orders",
"on",
"04/04/2020",
"were",
"for",
"the",
"state",
"of",
"New",
"York",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "Store Locations"
},
{
"id": 0,
"type": "table",
"value": "Sales Orders"
},
{
"id": 7,
"type": "column",
"value": "ordernumber"
},
{
"id": 2,
"type": "column",
"value": "orderdate"
},
{
"id": 5,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
202 | movielens | bird:train.json:2286 | How many distinct movies in English stars a male actor who acts the best? | SELECT COUNT(DISTINCT T1.actorid) FROM actors AS T1 INNER JOIN movies2actors AS T2 ON T1.actorid = T2.actorid INNER JOIN movies AS T3 ON T2.movieid = T3.movieid WHERE T3.isEnglish = 'T' AND T1.a_gender = 'M' AND T1.a_quality = 5 | [
"How",
"many",
"distinct",
"movies",
"in",
"English",
"stars",
"a",
"male",
"actor",
"who",
"acts",
"the",
"best",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "movies2actors"
},
{
"id": 5,
"type": "column",
"value": "isenglish"
},
{
"id": 9,
"type": "column",
"value": "a_quality"
},
{
"id": 7,
"type": "column",
"value": "a_gender"
},
{
"id": 1,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
203 | mondial_geo | bird:train.json:8460 | What is the full name of ABEDA and when was it established? | SELECT Name, Established FROM organization WHERE Abbreviation = 'ABEDA' | [
"What",
"is",
"the",
"full",
"name",
"of",
"ABEDA",
"and",
"when",
"was",
"it",
"established",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "organization"
},
{
"id": 3,
"type": "column",
"value": "abbreviation"
},
{
"id": 2,
"type": "column",
"value": "established"
},
{
"id": 4,
"type": "value",
"value": "ABEDA"
},
{
"id": 1,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
204 | movie_3 | bird:train.json:9243 | List the address in Texas in the ascending order of city id. | SELECT address FROM address WHERE district = 'Texas' AND city_id = ( SELECT MIN(city_id) FROM address WHERE district = 'Texas' ) | [
"List",
"the",
"address",
"in",
"Texas",
"in",
"the",
"ascending",
"order",
"of",
"city",
"i",
"d."
] | [
{
"id": 2,
"type": "column",
"value": "district"
},
{
"id": 0,
"type": "table",
"value": "address"
},
{
"id": 1,
"type": "column",
"value": "address"
},
{
"id": 4,
"type": "column",
"value": "city_id"
},
{
"id": 3,
"type": "value",
"value":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
10,
11,
12
]
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN"
] |
205 | customers_card_transactions | spider:train_spider.json:739 | Show the card type codes and the number of transactions. | SELECT T2.card_type_code , count(*) FROM Financial_transactions AS T1 JOIN Customers_cards AS T2 ON T1.card_id = T2.card_id GROUP BY T2.card_type_code | [
"Show",
"the",
"card",
"type",
"codes",
"and",
"the",
"number",
"of",
"transactions",
"."
] | [
{
"id": 1,
"type": "table",
"value": "financial_transactions"
},
{
"id": 2,
"type": "table",
"value": "customers_cards"
},
{
"id": 0,
"type": "column",
"value": "card_type_code"
},
{
"id": 3,
"type": "column",
"value": "card_id"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
206 | icfp_1 | spider:train_spider.json:2874 | Who belong to the institution "University of Oxford"? Show the first names and last names. | SELECT DISTINCT t1.fname , t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid = t2.authid JOIN inst AS t3 ON t2.instid = t3.instid WHERE t3.name = "University of Oxford" | [
"Who",
"belong",
"to",
"the",
"institution",
"\"",
"University",
"of",
"Oxford",
"\"",
"?",
"Show",
"the",
"first",
"names",
"and",
"last",
"names",
"."
] | [
{
"id": 4,
"type": "column",
"value": "University of Oxford"
},
{
"id": 6,
"type": "table",
"value": "authorship"
},
{
"id": 5,
"type": "table",
"value": "authors"
},
{
"id": 7,
"type": "column",
"value": "instid"
},
{
"id": 8,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
17
]
},
{
"entity_id": 4,
"token_idxs": [
6,
7,
8
]
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
207 | cre_Doc_Tracking_DB | spider:train_spider.json:4242 | Show the ids of all employees who have authorized destruction. | SELECT DISTINCT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed | [
"Show",
"the",
"ids",
"of",
"all",
"employees",
"who",
"have",
"authorized",
"destruction",
"."
] | [
{
"id": 1,
"type": "column",
"value": "destruction_authorised_by_employee_id"
},
{
"id": 0,
"type": "table",
"value": "documents_to_be_destroyed"
}
] | [
{
"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",
"O",
"O",
"O",
"O"
] |
208 | image_and_language | bird:train.json:7583 | What is the image ID with a predicted class of "parked on"? | SELECT DISTINCT T1.IMG_ID FROM IMG_REL AS T1 INNER JOIN PRED_CLASSES AS T2 ON T1.PRED_CLASS_ID = T2.PRED_CLASS_ID WHERE T2.PRED_CLASS = 'parked on' | [
"What",
"is",
"the",
"image",
"ID",
"with",
"a",
"predicted",
"class",
"of",
"\"",
"parked",
"on",
"\"",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "pred_class_id"
},
{
"id": 2,
"type": "table",
"value": "pred_classes"
},
{
"id": 3,
"type": "column",
"value": "pred_class"
},
{
"id": 4,
"type": "value",
"value": "parked on"
},
{
"id": 1,
"type": "table"... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
"entity_id": 4,
"token_idxs": [
11,
12
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
209 | cookbook | bird:train.json:8891 | How many ingredients are there in Apricot Yogurt Parfaits? | SELECT COUNT(*) FROM Recipe AS T1 INNER JOIN Quantity AS T2 ON T1.recipe_id = T2.recipe_id WHERE T1.title = 'Apricot Yogurt Parfaits' | [
"How",
"many",
"ingredients",
"are",
"there",
"in",
"Apricot",
"Yogurt",
"Parfaits",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Apricot Yogurt Parfaits"
},
{
"id": 4,
"type": "column",
"value": "recipe_id"
},
{
"id": 1,
"type": "table",
"value": "quantity"
},
{
"id": 0,
"type": "table",
"value": "recipe"
},
{
"id": 2,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_id... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
210 | game_1 | spider:train_spider.json:6034 | How many different students play games? | SELECT count(DISTINCT StuID) FROM Plays_games | [
"How",
"many",
"different",
"students",
"play",
"games",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "plays_games"
},
{
"id": 1,
"type": "column",
"value": "stuid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"I-TABLE",
"O"
] |
211 | customer_complaints | spider:train_spider.json:5780 | What is the phone number of the customer who has filed the most recent complaint? | SELECT t1.phone_number FROM customers AS t1 JOIN complaints AS t2 ON t1.customer_id = t2.customer_id ORDER BY t2.date_complaint_raised DESC LIMIT 1 | [
"What",
"is",
"the",
"phone",
"number",
"of",
"the",
"customer",
"who",
"has",
"filed",
"the",
"most",
"recent",
"complaint",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "date_complaint_raised"
},
{
"id": 0,
"type": "column",
"value": "phone_number"
},
{
"id": 4,
"type": "column",
"value": "customer_id"
},
{
"id": 2,
"type": "table",
"value": "complaints"
},
{
"id": 1,
"typ... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
212 | retail_world | bird:train.json:6384 | List the company names of customers from the city with the most customers. | SELECT CompanyName FROM `Customer and Suppliers by City` WHERE CITY = ( SELECT City FROM `Customer and Suppliers by City` GROUP BY City ORDER BY COUNT(Relationship) DESC LIMIT 1 ) | [
"List",
"the",
"company",
"names",
"of",
"customers",
"from",
"the",
"city",
"with",
"the",
"most",
"customers",
"."
] | [
{
"id": 0,
"type": "table",
"value": "Customer and Suppliers by City"
},
{
"id": 3,
"type": "column",
"value": "relationship"
},
{
"id": 1,
"type": "column",
"value": "companyname"
},
{
"id": 2,
"type": "column",
"value": "city"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
213 | device | spider:train_spider.json:5079 | What are the different software platforms for devices, ordered by frequency descending? | SELECT Software_Platform FROM device GROUP BY Software_Platform ORDER BY COUNT(*) DESC | [
"What",
"are",
"the",
"different",
"software",
"platforms",
"for",
"devices",
",",
"ordered",
"by",
"frequency",
"descending",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "software_platform"
},
{
"id": 0,
"type": "table",
"value": "device"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
214 | works_cycles | bird:train.json:7182 | What are the total per assembly quantity for unit measure code EA, IN and OZ respectively? What are the name of these 3 code? | SELECT SUM(T1.PerAssemblyQty), T2.Name FROM BillOfMaterials AS T1 INNER JOIN UnitMeasure AS T2 ON T1.UnitMeasureCode = T2.UnitMeasureCode WHERE T1.UnitMeasureCode IN ('EA', 'IN', 'OZ') GROUP BY T2.Name | [
"What",
"are",
"the",
"total",
"per",
"assembly",
"quantity",
"for",
"unit",
"measure",
"code",
"EA",
",",
"IN",
"and",
"OZ",
"respectively",
"?",
"What",
"are",
"the",
"name",
"of",
"these",
"3",
"code",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "billofmaterials"
},
{
"id": 3,
"type": "column",
"value": "unitmeasurecode"
},
{
"id": 7,
"type": "column",
"value": "perassemblyqty"
},
{
"id": 2,
"type": "table",
"value": "unitmeasure"
},
{
"id": 0,
"typ... | [
{
"entity_id": 0,
"token_idxs": [
21
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8,
9
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
215 | insurance_and_eClaims | spider:train_spider.json:1508 | Sort the customer names in alphabetical order. | SELECT customer_details FROM customers ORDER BY customer_details | [
"Sort",
"the",
"customer",
"names",
"in",
"alphabetical",
"order",
"."
] | [
{
"id": 1,
"type": "column",
"value": "customer_details"
},
{
"id": 0,
"type": "table",
"value": "customers"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.