question_id int64 0 16.1k | db_id stringclasses 259
values | dber_id stringlengths 15 29 | question stringlengths 16 325 | SQL stringlengths 18 1.25k | tokens listlengths 4 62 | entities listlengths 0 21 | entity_to_token listlengths 20 20 | dber_tags listlengths 4 62 |
|---|---|---|---|---|---|---|---|---|
10,801 | headphone_store | bird:test.json:953 | Find the name of stores which have no headphone in stock. | SELECT name FROM store WHERE store_id NOT IN (SELECT store_id FROM stock) | [
"Find",
"the",
"name",
"of",
"stores",
"which",
"have",
"no",
"headphone",
"in",
"stock",
"."
] | [
{
"id": 2,
"type": "column",
"value": "store_id"
},
{
"id": 0,
"type": "table",
"value": "store"
},
{
"id": 3,
"type": "table",
"value": "stock"
},
{
"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": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,802 | movie_3 | bird:train.json:9241 | List the store ID of the films with a rental rate greater than the 60% of average rental rate of all listed films. | SELECT T2.store_id FROM film AS T1 INNER JOIN inventory AS T2 ON T1.film_id = T2.film_id WHERE T1.rental_rate > ( SELECT AVG(T1.rental_rate) * 0.6 FROM film AS T1 ) | [
"List",
"the",
"store",
"ID",
"of",
"the",
"films",
"with",
"a",
"rental",
"rate",
"greater",
"than",
"the",
"60",
"%",
"of",
"average",
"rental",
"rate",
"of",
"all",
"listed",
"films",
"."
] | [
{
"id": 3,
"type": "column",
"value": "rental_rate"
},
{
"id": 2,
"type": "table",
"value": "inventory"
},
{
"id": 0,
"type": "column",
"value": "store_id"
},
{
"id": 4,
"type": "column",
"value": "film_id"
},
{
"id": 1,
"type": "table",
"v... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
23
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,803 | retails | bird:train.json:6770 | What percentage of customers from France is in the automobile segment? | SELECT CAST(SUM(IIF(T1.c_mktsegment = 'AUTOMOBILE', 1, 0)) AS REAL) * 100 / COUNT(T1.c_name) FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T2.n_name = 'FRANCE' | [
"What",
"percentage",
"of",
"customers",
"from",
"France",
"is",
"in",
"the",
"automobile",
"segment",
"?"
] | [
{
"id": 10,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 4,
"type": "column",
"value": "c_nationkey"
},
{
"id": 5,
"type": "column",
"value": "n_nationkey"
},
{
"id": 11,
"type": "value",
"value": "AUTOMOBILE"
},
{
"id": 0,
"type": "tab... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,804 | public_review_platform | bird:train.json:3823 | What is the number of useful votes that the user 52592 received when reviewed for business number 2? | SELECT review_votes_useful FROM Reviews WHERE user_id = 52592 AND business_id = 2 | [
"What",
"is",
"the",
"number",
"of",
"useful",
"votes",
"that",
"the",
"user",
"52592",
"received",
"when",
"reviewed",
"for",
"business",
"number",
"2",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "review_votes_useful"
},
{
"id": 4,
"type": "column",
"value": "business_id"
},
{
"id": 0,
"type": "table",
"value": "reviews"
},
{
"id": 2,
"type": "column",
"value": "user_id"
},
{
"id": 3,
"type": "value... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,805 | voter_2 | spider:train_spider.json:5455 | What are the first names of all the students aged above 22? | SELECT Fname FROM STUDENT WHERE Age > 22 | [
"What",
"are",
"the",
"first",
"names",
"of",
"all",
"the",
"students",
"aged",
"above",
"22",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "fname"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "22"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,806 | music_platform_2 | bird:train.json:7977 | What is the average rating for the "crime-junkie" podcast? | SELECT AVG(T2.rating) FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T1.title = 'Crime Junkie' | [
"What",
"is",
"the",
"average",
"rating",
"for",
"the",
"\"",
"crime",
"-",
"junkie",
"\"",
"podcast",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Crime Junkie"
},
{
"id": 5,
"type": "column",
"value": "podcast_id"
},
{
"id": 0,
"type": "table",
"value": "podcasts"
},
{
"id": 1,
"type": "table",
"value": "reviews"
},
{
"id": 4,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-TABLE",
"O"
] |
10,807 | allergy_1 | spider:train_spider.json:501 | Which advisor has most number of students? | SELECT advisor FROM Student GROUP BY advisor ORDER BY count(*) DESC LIMIT 1 | [
"Which",
"advisor",
"has",
"most",
"number",
"of",
"students",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "advisor"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,808 | soccer_2016 | bird:train.json:2035 | What is the percentage of matches that are won by runs? | SELECT CAST(SUM(CASE WHEN T1.win_type = 1 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.Win_Type) FROM Match AS T1 INNER JOIN Win_By AS T2 ON T1.Win_Type = T2.Win_Id | [
"What",
"is",
"the",
"percentage",
"of",
"matches",
"that",
"are",
"won",
"by",
"runs",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "win_type"
},
{
"id": 1,
"type": "table",
"value": "win_by"
},
{
"id": 3,
"type": "column",
"value": "win_id"
},
{
"id": 0,
"type": "table",
"value": "match"
},
{
"id": 4,
"type": "value",
"value": "100... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
8,
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O"
] |
10,810 | european_football_2 | bird:dev.json:1022 | What is the preferred foot when attacking of the player with the lowest potential? | SELECT preferred_foot FROM Player_Attributes WHERE potential IS NOT NULL ORDER BY potential ASC LIMIT 1 | [
"What",
"is",
"the",
"preferred",
"foot",
"when",
"attacking",
"of",
"the",
"player",
"with",
"the",
"lowest",
"potential",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "player_attributes"
},
{
"id": 1,
"type": "column",
"value": "preferred_foot"
},
{
"id": 2,
"type": "column",
"value": "potential"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"tok... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,811 | music_2 | spider:train_spider.json:5255 | What are the types of vocals that the band member with the first name "Solveig" played the most? | SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate = T2.id WHERE firstname = "Solveig" GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1 | [
"What",
"are",
"the",
"types",
"of",
"vocals",
"that",
"the",
"band",
"member",
"with",
"the",
"first",
"name",
"\"",
"Solveig",
"\"",
"played",
"the",
"most",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "firstname"
},
{
"id": 5,
"type": "column",
"value": "bandmate"
},
{
"id": 4,
"type": "column",
"value": "Solveig"
},
{
"id": 1,
"type": "table",
"value": "vocals"
},
{
"id": 0,
"type": "column",
"value... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
15
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
10,812 | body_builder | spider:train_spider.json:1155 | What are the birthdays of people in ascending order of height? | SELECT Birth_Date FROM People ORDER BY Height ASC | [
"What",
"are",
"the",
"birthdays",
"of",
"people",
"in",
"ascending",
"order",
"of",
"height",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "birth_date"
},
{
"id": 0,
"type": "table",
"value": "people"
},
{
"id": 2,
"type": "column",
"value": "height"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,814 | wine_1 | spider:train_spider.json:6545 | Find the maximum price of wins from the appelations in Central Coast area and produced before the year of 2005. | SELECT max(T2.Price) FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation = T2.Appelation WHERE T1.Area = "Central Coast" AND T2.year < 2005 | [
"Find",
"the",
"maximum",
"price",
"of",
"wins",
"from",
"the",
"appelations",
"in",
"Central",
"Coast",
"area",
"and",
"produced",
"before",
"the",
"year",
"of",
"2005",
"."
] | [
{
"id": 5,
"type": "column",
"value": "Central Coast"
},
{
"id": 0,
"type": "table",
"value": "appellations"
},
{
"id": 3,
"type": "column",
"value": "appelation"
},
{
"id": 2,
"type": "column",
"value": "price"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,815 | formula_1 | bird:dev.json:863 | Show me the season page of year when the race No. 901 took place. | SELECT T2.url FROM races AS T1 INNER JOIN seasons AS T2 ON T2.year = T1.year WHERE T1.raceId = 901 | [
"Show",
"me",
"the",
"season",
"page",
"of",
"year",
"when",
"the",
"race",
"No",
".",
"901",
"took",
"place",
"."
] | [
{
"id": 2,
"type": "table",
"value": "seasons"
},
{
"id": 3,
"type": "column",
"value": "raceid"
},
{
"id": 1,
"type": "table",
"value": "races"
},
{
"id": 5,
"type": "column",
"value": "year"
},
{
"id": 0,
"type": "column",
"value": "url"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
10,816 | synthea | bird:train.json:1499 | How many patients sought medical attention due to a second-degree burn? Describe the care plan recommended to them. | SELECT COUNT(DISTINCT T2.PATIENT), T2.DESCRIPTION FROM encounters AS T1 INNER JOIN careplans AS T2 ON T1.PATIENT = T2.PATIENT WHERE T2.REASONDESCRIPTION = 'Second degree burn' | [
"How",
"many",
"patients",
"sought",
"medical",
"attention",
"due",
"to",
"a",
"second",
"-",
"degree",
"burn",
"?",
"Describe",
"the",
"care",
"plan",
"recommended",
"to",
"them",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Second degree burn"
},
{
"id": 3,
"type": "column",
"value": "reasondescription"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 1,
"type": "table",
"value": "encounters"
},
{
"id": 2,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
16,
17
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9,
10,
11,
1... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O"
] |
10,817 | movie_1 | spider:train_spider.json:2435 | What are the names of all movies directed by Steven Spielberg? | SELECT title FROM Movie WHERE director = 'Steven Spielberg' | [
"What",
"are",
"the",
"names",
"of",
"all",
"movies",
"directed",
"by",
"Steven",
"Spielberg",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Steven Spielberg"
},
{
"id": 2,
"type": "column",
"value": "director"
},
{
"id": 0,
"type": "table",
"value": "movie"
},
{
"id": 1,
"type": "column",
"value": "title"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
10,818 | authors | bird:train.json:3512 | What is the url of the journal in which the paper "Area Effects in Cepaea" was published? | SELECT T1.HomePage FROM Journal AS T1 INNER JOIN Paper AS T2 ON T1.Id = T2.JournalId WHERE T2.Title = 'Area Effects in Cepaea' | [
"What",
"is",
"the",
"url",
"of",
"the",
"journal",
"in",
"which",
"the",
"paper",
"\"",
"Area",
"Effects",
"in",
"Cepaea",
"\"",
"was",
"published",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Area Effects in Cepaea"
},
{
"id": 6,
"type": "column",
"value": "journalid"
},
{
"id": 0,
"type": "column",
"value": "homepage"
},
{
"id": 1,
"type": "table",
"value": "journal"
},
{
"id": 2,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12,
13,
14,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O"
] |
10,819 | advertising_agencies | bird:test.json:2074 | What are the client details for each client and the corresponding details of their agencies? | SELECT T1.client_details , T2.agency_details FROM Clients AS T1 JOIN Agencies AS T2 ON T1.agency_id = T2.agency_id | [
"What",
"are",
"the",
"client",
"details",
"for",
"each",
"client",
"and",
"the",
"corresponding",
"details",
"of",
"their",
"agencies",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "client_details"
},
{
"id": 1,
"type": "column",
"value": "agency_details"
},
{
"id": 4,
"type": "column",
"value": "agency_id"
},
{
"id": 3,
"type": "table",
"value": "agencies"
},
{
"id": 2,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
10,820 | college_1 | spider:train_spider.json:3258 | What are the codes of all the courses that are located in room KLR209? | SELECT class_code FROM CLASS WHERE class_room = 'KLR209' | [
"What",
"are",
"the",
"codes",
"of",
"all",
"the",
"courses",
"that",
"are",
"located",
"in",
"room",
"KLR209",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "class_code"
},
{
"id": 2,
"type": "column",
"value": "class_room"
},
{
"id": 3,
"type": "value",
"value": "KLR209"
},
{
"id": 0,
"type": "table",
"value": "class"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
10,821 | food_inspection | bird:train.json:8844 | Among the businesses within the postal code 94117, what is total number of businesses with a high risk category? | SELECT COUNT(DISTINCT T2.business_id) FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T2.postal_code = 94117 AND T1.risk_category = 'High Risk' | [
"Among",
"the",
"businesses",
"within",
"the",
"postal",
"code",
"94117",
",",
"what",
"is",
"total",
"number",
"of",
"businesses",
"with",
"a",
"high",
"risk",
"category",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "risk_category"
},
{
"id": 2,
"type": "column",
"value": "business_id"
},
{
"id": 3,
"type": "column",
"value": "postal_code"
},
{
"id": 0,
"type": "table",
"value": "violations"
},
{
"id": 1,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
5,
6
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
10,822 | mondial_geo | bird:train.json:8362 | What is the smallest border's length, and what form of government do the two nations bordering it have? | SELECT T1.Government, T3.Government FROM politics AS T1 INNER JOIN borders AS T2 ON T1.Country = T2.Country1 INNER JOIN politics AS T3 ON T3.Country = T2.Country2 ORDER BY T2.Length ASC LIMIT 1 | [
"What",
"is",
"the",
"smallest",
"border",
"'s",
"length",
",",
"and",
"what",
"form",
"of",
"government",
"do",
"the",
"two",
"nations",
"bordering",
"it",
"have",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "government"
},
{
"id": 1,
"type": "table",
"value": "politics"
},
{
"id": 5,
"type": "column",
"value": "country2"
},
{
"id": 6,
"type": "column",
"value": "country1"
},
{
"id": 3,
"type": "table",
"va... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
4,
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,823 | retails | bird:train.json:6828 | What are the shipping methods for the orders on 12/31/1994? | SELECT DISTINCT T2.l_shipmode FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T1.o_orderdate = '1994-12-31' | [
"What",
"are",
"the",
"shipping",
"methods",
"for",
"the",
"orders",
"on",
"12/31/1994",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "o_orderdate"
},
{
"id": 0,
"type": "column",
"value": "l_shipmode"
},
{
"id": 4,
"type": "value",
"value": "1994-12-31"
},
{
"id": 5,
"type": "column",
"value": "o_orderkey"
},
{
"id": 6,
"type": "column",... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,824 | works_cycles | bird:train.json:7130 | Which role has the most common contact among businesses? | SELECT T1.Name FROM ContactType AS T1 INNER JOIN BusinessEntityContact AS T2 ON T1.ContactTypeID = T2.ContactTypeID GROUP BY T1.Name ORDER BY COUNT(T1.Name) DESC LIMIT 1 | [
"Which",
"role",
"has",
"the",
"most",
"common",
"contact",
"among",
"businesses",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "businessentitycontact"
},
{
"id": 3,
"type": "column",
"value": "contacttypeid"
},
{
"id": 1,
"type": "table",
"value": "contacttype"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O"
] |
10,825 | e_government | spider:train_spider.json:6328 | Find name of the services that has never been used. | SELECT service_name FROM services EXCEPT SELECT t1.service_name FROM services AS t1 JOIN party_services AS t2 ON t1.service_id = t2.service_id | [
"Find",
"name",
"of",
"the",
"services",
"that",
"has",
"never",
"been",
"used",
"."
] | [
{
"id": 2,
"type": "table",
"value": "party_services"
},
{
"id": 1,
"type": "column",
"value": "service_name"
},
{
"id": 3,
"type": "column",
"value": "service_id"
},
{
"id": 0,
"type": "table",
"value": "services"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,826 | wine_1 | spider:train_spider.json:6549 | What are the wines that have prices higher than 50 and made of Red color grapes? | SELECT T2.Name FROM Grapes AS T1 JOIN WINE AS T2 ON T1.Grape = T2.Grape WHERE T1.Color = "Red" AND T2.price > 50 | [
"What",
"are",
"the",
"wines",
"that",
"have",
"prices",
"higher",
"than",
"50",
"and",
"made",
"of",
"Red",
"color",
"grapes",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "grapes"
},
{
"id": 3,
"type": "column",
"value": "grape"
},
{
"id": 4,
"type": "column",
"value": "color"
},
{
"id": 6,
"type": "column",
"value": "price"
},
{
"id": 0,
"type": "column",
"value": "name"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-COLUMN",
"O"
] |
10,827 | public_review_platform | bird:train.json:4085 | Within the user who joined Yelp in 2004, explore the user ID with average star of 5 and it's review length on the business. | SELECT T2.user_id, T2.review_length FROM Users AS T1 INNER JOIN Reviews AS T2 ON T1.user_id = T2.user_id WHERE T1.user_yelping_since_year = 2004 AND T1.user_average_stars = 5 | [
"Within",
"the",
"user",
"who",
"joined",
"Yelp",
"in",
"2004",
",",
"explore",
"the",
"user",
"ID",
"with",
"average",
"star",
"of",
"5",
"and",
"it",
"'s",
"review",
"length",
"on",
"the",
"business",
"."
] | [
{
"id": 4,
"type": "column",
"value": "user_yelping_since_year"
},
{
"id": 6,
"type": "column",
"value": "user_average_stars"
},
{
"id": 1,
"type": "column",
"value": "review_length"
},
{
"id": 0,
"type": "column",
"value": "user_id"
},
{
"id": 3,
... | [
{
"entity_id": 0,
"token_idxs": [
11,
12
]
},
{
"entity_id": 1,
"token_idxs": [
22
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": [
21
]
},
{
"entity_id": 4,
"token_idxs": []
},
{... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
10,828 | retail_complains | bird:train.json:242 | Which complaint is more urgent, complaint ID CR2400594 or ID CR2405641? | SELECT CASE WHEN SUM(CASE WHEN `Complaint ID` = 'CR2400594' THEN priority END) > SUM(CASE WHEN `Complaint ID` = 'CR2405641' THEN priority END) THEN 'CR2400594' ELSE 'CR2405641' END FROM callcenterlogs | [
"Which",
"complaint",
"is",
"more",
"urgent",
",",
"complaint",
"ID",
"CR2400594",
"or",
"ID",
"CR2405641",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "callcenterlogs"
},
{
"id": 4,
"type": "column",
"value": "Complaint ID"
},
{
"id": 1,
"type": "value",
"value": "CR2405641"
},
{
"id": 2,
"type": "value",
"value": "CR2400594"
},
{
"id": 3,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6,
7
]
},
{
"entity_id"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"B-VALUE",
"O"
] |
10,829 | vehicle_driver | bird:test.json:154 | Return the names of drivers with citizenship from the United States. | SELECT name FROM driver WHERE citizenship = 'United States' | [
"Return",
"the",
"names",
"of",
"drivers",
"with",
"citizenship",
"from",
"the",
"United",
"States",
"."
] | [
{
"id": 3,
"type": "value",
"value": "United States"
},
{
"id": 2,
"type": "column",
"value": "citizenship"
},
{
"id": 0,
"type": "table",
"value": "driver"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
10,830 | loan_1 | spider:train_spider.json:3028 | Find the number of different states which banks are located at. | SELECT count(DISTINCT state) FROM bank | [
"Find",
"the",
"number",
"of",
"different",
"states",
"which",
"banks",
"are",
"located",
"at",
"."
] | [
{
"id": 1,
"type": "column",
"value": "state"
},
{
"id": 0,
"type": "table",
"value": "bank"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
10,831 | cars | bird:train.json:3144 | How many cars with horsepower greater than 200 were produced in 1975? | SELECT COUNT(T2.model_year) FROM data AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID WHERE T1.horsepower > 200 AND T2.model_year = 1975 | [
"How",
"many",
"cars",
"with",
"horsepower",
"greater",
"than",
"200",
"were",
"produced",
"in",
"1975",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "production"
},
{
"id": 2,
"type": "column",
"value": "model_year"
},
{
"id": 4,
"type": "column",
"value": "horsepower"
},
{
"id": 0,
"type": "table",
"value": "data"
},
{
"id": 6,
"type": "value",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9,
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_id": 5,
"tok... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"I-TABLE",
"B-VALUE",
"O"
] |
10,832 | customers_and_invoices | spider:train_spider.json:1601 | What is the invoice number and invoice date for the invoice with most number of transactions? | SELECT T2.invoice_number , T2.invoice_date FROM Financial_transactions AS T1 JOIN Invoices AS T2 ON T1.invoice_number = T2.invoice_number GROUP BY T1.invoice_number ORDER BY count(*) DESC LIMIT 1 | [
"What",
"is",
"the",
"invoice",
"number",
"and",
"invoice",
"date",
"for",
"the",
"invoice",
"with",
"most",
"number",
"of",
"transactions",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "financial_transactions"
},
{
"id": 0,
"type": "column",
"value": "invoice_number"
},
{
"id": 1,
"type": "column",
"value": "invoice_date"
},
{
"id": 3,
"type": "table",
"value": "invoices"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,833 | car_retails | bird:train.json:1644 | List all customer names with orders that are disputed. | SELECT t3.firstName, t3.lastName FROM orders AS t1 INNER JOIN customers AS t2 ON t1.customerNumber = t2.customerNumber INNER JOIN employees AS t3 ON t2.salesRepEmployeeNumber = t3.employeeNumber WHERE t1.status = 'Disputed' | [
"List",
"all",
"customer",
"names",
"with",
"orders",
"that",
"are",
"disputed",
"."
] | [
{
"id": 7,
"type": "column",
"value": "salesrepemployeenumber"
},
{
"id": 8,
"type": "column",
"value": "employeenumber"
},
{
"id": 9,
"type": "column",
"value": "customernumber"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 2,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,835 | works_cycles | bird:train.json:7371 | What time does the company's night shift begin? Indicate the answer in regular form. | SELECT StartTime FROM Shift WHERE Name = 'Night' | [
"What",
"time",
"does",
"the",
"company",
"'s",
"night",
"shift",
"begin",
"?",
"Indicate",
"the",
"answer",
"in",
"regular",
"form",
"."
] | [
{
"id": 1,
"type": "column",
"value": "starttime"
},
{
"id": 0,
"type": "table",
"value": "shift"
},
{
"id": 3,
"type": "value",
"value": "Night"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
0,
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,836 | college_3 | spider:train_spider.json:4671 | Find the name of the department that has the biggest number of students minored in? | SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MINOR_IN AS T2 ON T1.DNO = T2.DNO GROUP BY T2.DNO ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"name",
"of",
"the",
"department",
"that",
"has",
"the",
"biggest",
"number",
"of",
"students",
"minored",
"in",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "department"
},
{
"id": 3,
"type": "table",
"value": "minor_in"
},
{
"id": 1,
"type": "column",
"value": "dname"
},
{
"id": 0,
"type": "column",
"value": "dno"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
13,
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
10,837 | shipping | bird:train.json:5594 | Who was the customer of shipment no.1275? Give the customer's name. | SELECT T1.cust_name FROM customer AS T1 INNER JOIN shipment AS T2 ON T1.cust_id = T2.cust_id WHERE T2.ship_id = '1275' | [
"Who",
"was",
"the",
"customer",
"of",
"shipment",
"no.1275",
"?",
"Give",
"the",
"customer",
"'s",
"name",
"."
] | [
{
"id": 0,
"type": "column",
"value": "cust_name"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 2,
"type": "table",
"value": "shipment"
},
{
"id": 3,
"type": "column",
"value": "ship_id"
},
{
"id": 5,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,838 | debit_card_specializing | bird:dev.json:1520 | For the customer who paid 124.05 in 2012/8/24, how much did he/she spend during the January of 2012? And what is the date and expenses exactly? | SELECT T1.CustomerID, T2.Date, T2.Consumption FROM transactions_1k AS T1 INNER JOIN yearmonth AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.Date = '2012-08-24' AND T1.Price = 124.05 AND T2.Date = '201201' | [
"For",
"the",
"customer",
"who",
"paid",
"124.05",
"in",
"2012/8/24",
",",
"how",
"much",
"did",
"he",
"/",
"she",
"spend",
"during",
"the",
"January",
"of",
"2012",
"?",
"And",
"what",
"is",
"the",
"date",
"and",
"expenses",
"exactly",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "transactions_1k"
},
{
"id": 2,
"type": "column",
"value": "consumption"
},
{
"id": 0,
"type": "column",
"value": "customerid"
},
{
"id": 5,
"type": "value",
"value": "2012-08-24"
},
{
"id": 4,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
26
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
10,839 | customer_complaints | spider:train_spider.json:5768 | How many customers are there? | SELECT count(*) FROM customers | [
"How",
"many",
"customers",
"are",
"there",
"?"
] | [
{
"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"
] |
10,840 | soccer_2 | spider:train_spider.json:5016 | Which colleges do the tryout players whose name starts with letter D go to? | SELECT T1.cName FROM tryout AS T1 JOIN player AS T2 ON T1.pID = T2.pID WHERE T2.pName LIKE 'D%' | [
"Which",
"colleges",
"do",
"the",
"tryout",
"players",
"whose",
"name",
"starts",
"with",
"letter",
"D",
"go",
"to",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "tryout"
},
{
"id": 2,
"type": "table",
"value": "player"
},
{
"id": 0,
"type": "column",
"value": "cname"
},
{
"id": 3,
"type": "column",
"value": "pname"
},
{
"id": 5,
"type": "column",
"value": "pid"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
10,841 | hr_1 | spider:train_spider.json:3504 | What are the ids and full names for employees who work in a department that has someone with a first name that contains the letter T? | SELECT employee_id , first_name , last_name FROM employees WHERE department_id IN ( SELECT department_id FROM employees WHERE first_name LIKE '%T%' ) | [
"What",
"are",
"the",
"ids",
"and",
"full",
"names",
"for",
"employees",
"who",
"work",
"in",
"a",
"department",
"that",
"has",
"someone",
"with",
"a",
"first",
"name",
"that",
"contains",
"the",
"letter",
"T",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "department_id"
},
{
"id": 1,
"type": "column",
"value": "employee_id"
},
{
"id": 2,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 3,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
18,
19,
20
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,842 | scientist_1 | spider:train_spider.json:6504 | What are the names of scientists who are assigned to any project? | SELECT T2.name FROM assignedto AS T1 JOIN scientists AS T2 ON T1.scientist = T2.ssn | [
"What",
"are",
"the",
"names",
"of",
"scientists",
"who",
"are",
"assigned",
"to",
"any",
"project",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "assignedto"
},
{
"id": 2,
"type": "table",
"value": "scientists"
},
{
"id": 3,
"type": "column",
"value": "scientist"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
8,
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O"
] |
10,843 | dorm_1 | spider:train_spider.json:5746 | Find the total number of students living in the male dorm (with gender M). | SELECT count(*) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid = T2.stuid JOIN dorm AS T3 ON T3.dormid = T2.dormid WHERE T3.gender = 'M' | [
"Find",
"the",
"total",
"number",
"of",
"students",
"living",
"in",
"the",
"male",
"dorm",
"(",
"with",
"gender",
"M",
")",
"."
] | [
{
"id": 4,
"type": "table",
"value": "lives_in"
},
{
"id": 3,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "gender"
},
{
"id": 5,
"type": "column",
"value": "dormid"
},
{
"id": 6,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"enti... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O"
] |
10,845 | music_tracker | bird:train.json:2076 | List the group name has the most downloaded that have released jazz genres from 1982 or later. | SELECT T1.groupName FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T2.tag = 'jazz' AND T1.groupYear >= 1982 ORDER BY T1.totalSnatched DESC LIMIT 1 | [
"List",
"the",
"group",
"name",
"has",
"the",
"most",
"downloaded",
"that",
"have",
"released",
"jazz",
"genres",
"from",
"1982",
"or",
"later",
"."
] | [
{
"id": 3,
"type": "column",
"value": "totalsnatched"
},
{
"id": 0,
"type": "column",
"value": "groupname"
},
{
"id": 7,
"type": "column",
"value": "groupyear"
},
{
"id": 1,
"type": "table",
"value": "torrents"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
10,846 | public_review_platform | bird:train.json:4103 | How many businesses have more than 1 attribute? | SELECT COUNT(business_id) FROM Business_Attributes WHERE attribute_value > 1 | [
"How",
"many",
"businesses",
"have",
"more",
"than",
"1",
"attribute",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "business_attributes"
},
{
"id": 1,
"type": "column",
"value": "attribute_value"
},
{
"id": 3,
"type": "column",
"value": "business_id"
},
{
"id": 2,
"type": "value",
"value": "1"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,847 | image_and_language | bird:train.json:7493 | Provide the number of predicted classes. | SELECT COUNT(PRED_CLASS_ID) FROM PRED_CLASSES | [
"Provide",
"the",
"number",
"of",
"predicted",
"classes",
"."
] | [
{
"id": 1,
"type": "column",
"value": "pred_class_id"
},
{
"id": 0,
"type": "table",
"value": "pred_classes"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4,
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",
"B-TABLE",
"I-TABLE",
"O"
] |
10,848 | bike_share_1 | bird:train.json:9043 | Please list bikes id were used in trips which start station were installed in 2013. | SELECT DISTINCT T1.bike_id FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.installation_date LIKE '%2013' | [
"Please",
"list",
"bikes",
"i",
"d",
"were",
"used",
"in",
"trips",
"which",
"start",
"station",
"were",
"installed",
"in",
"2013",
"."
] | [
{
"id": 6,
"type": "column",
"value": "start_station_name"
},
{
"id": 3,
"type": "column",
"value": "installation_date"
},
{
"id": 0,
"type": "column",
"value": "bike_id"
},
{
"id": 2,
"type": "table",
"value": "station"
},
{
"id": 4,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,849 | apartment_rentals | spider:train_spider.json:1226 | What is the booking status code of the apartment with apartment number "Suite 634"? | SELECT T1.booking_status_code FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id = T2.apt_id WHERE T2.apt_number = "Suite 634" | [
"What",
"is",
"the",
"booking",
"status",
"code",
"of",
"the",
"apartment",
"with",
"apartment",
"number",
"\"",
"Suite",
"634",
"\"",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "booking_status_code"
},
{
"id": 1,
"type": "table",
"value": "apartment_bookings"
},
{
"id": 2,
"type": "table",
"value": "apartments"
},
{
"id": 3,
"type": "column",
"value": "apt_number"
},
{
"id": 4,
"t... | [
{
"entity_id": 0,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
10,850 | gas_company | spider:train_spider.json:2031 | Show the manager name with most number of gas stations opened after 2000. | SELECT manager_name FROM gas_station WHERE open_year > 2000 GROUP BY manager_name ORDER BY count(*) DESC LIMIT 1 | [
"Show",
"the",
"manager",
"name",
"with",
"most",
"number",
"of",
"gas",
"stations",
"opened",
"after",
"2000",
"."
] | [
{
"id": 1,
"type": "column",
"value": "manager_name"
},
{
"id": 0,
"type": "table",
"value": "gas_station"
},
{
"id": 2,
"type": "column",
"value": "open_year"
},
{
"id": 3,
"type": "value",
"value": "2000"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8,
9
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,851 | books | bird:train.json:5968 | Who authored the book with greatest number of pages? | SELECT T3.author_name 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 ORDER BY T1.num_pages DESC LIMIT 1 | [
"Who",
"authored",
"the",
"book",
"with",
"greatest",
"number",
"of",
"pages",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "author_name"
},
{
"id": 4,
"type": "table",
"value": "book_author"
},
{
"id": 2,
"type": "column",
"value": "num_pages"
},
{
"id": 5,
"type": "column",
"value": "author_id"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,852 | mondial_geo | bird:train.json:8320 | List all the cities in Sumatra and state the population of each city. | SELECT T1.Name, T1.Population FROM city AS T1 INNER JOIN locatedOn AS T2 ON T1.Name = T2.City INNER JOIN island AS T3 ON T3.Name = T2.Island WHERE T3.Name = 'Sumatra' | [
"List",
"all",
"the",
"cities",
"in",
"Sumatra",
"and",
"state",
"the",
"population",
"of",
"each",
"city",
"."
] | [
{
"id": 1,
"type": "column",
"value": "population"
},
{
"id": 5,
"type": "table",
"value": "locatedon"
},
{
"id": 3,
"type": "value",
"value": "Sumatra"
},
{
"id": 2,
"type": "table",
"value": "island"
},
{
"id": 6,
"type": "column",
"value... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
10,853 | culture_company | spider:train_spider.json:6979 | What are the publishers who have published a book in both 1989 and 1990? | SELECT publisher FROM book_club WHERE YEAR = 1989 INTERSECT SELECT publisher FROM book_club WHERE YEAR = 1990 | [
"What",
"are",
"the",
"publishers",
"who",
"have",
"published",
"a",
"book",
"in",
"both",
"1989",
"and",
"1990",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "book_club"
},
{
"id": 1,
"type": "column",
"value": "publisher"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "1989"
},
{
"id": 4,
"type": "value",
"value": "19... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
10,854 | formula_1 | bird:dev.json:869 | For the constructor which got the highest point in the race No. 9 , what is its introduction website? | SELECT T2.url FROM constructorResults AS T1 INNER JOIN constructors AS T2 ON T2.constructorId = T1.constructorId WHERE T1.raceId = 9 ORDER BY T1.points DESC LIMIT 1 | [
"For",
"the",
"constructor",
"which",
"got",
"the",
"highest",
"point",
"in",
"the",
"race",
"No",
".",
"9",
",",
"what",
"is",
"its",
"introduction",
"website",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "constructorresults"
},
{
"id": 6,
"type": "column",
"value": "constructorid"
},
{
"id": 2,
"type": "table",
"value": "constructors"
},
{
"id": 3,
"type": "column",
"value": "raceid"
},
{
"id": 5,
"type": "c... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,855 | public_review_platform | bird:train.json:3966 | Is the payment in mastercard possible for the Yelp business No."12476"? | SELECT T1.attribute_value FROM Business_Attributes AS T1 INNER JOIN Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T1.business_id = 12476 AND T2.attribute_name = 'payment_types_mastercard' | [
"Is",
"the",
"payment",
"in",
"mastercard",
"possible",
"for",
"the",
"Yelp",
"business",
"No",
".",
"\"12476",
"\"",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "payment_types_mastercard"
},
{
"id": 1,
"type": "table",
"value": "business_attributes"
},
{
"id": 0,
"type": "column",
"value": "attribute_value"
},
{
"id": 6,
"type": "column",
"value": "attribute_name"
},
{
... | [
{
"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": [
9
]
},
{
"entity_id": 5,
"token_idxs": [
12
... | [
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
10,856 | world_development_indicators | bird:train.json:2240 | Which countries have notes on the indicator BX.KLT.DINV.CD.WD? | SELECT T1.ShortName FROM Country AS T1 INNER JOIN CountryNotes AS T2 ON T1.CountryCode = T2.Countrycode INNER JOIN Series AS T3 ON T2.Seriescode = T3.SeriesCode WHERE T3.Seriescode = 'BX.KLT.DINV.CD.WD' | [
"Which",
"countries",
"have",
"notes",
"on",
"the",
"indicator",
"BX.KLT.DINV.CD.WD",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "BX.KLT.DINV.CD.WD"
},
{
"id": 5,
"type": "table",
"value": "countrynotes"
},
{
"id": 6,
"type": "column",
"value": "countrycode"
},
{
"id": 2,
"type": "column",
"value": "seriescode"
},
{
"id": 0,
"type": "... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
1
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
10,857 | video_game | bird:test.json:1954 | Return the average number of units sold in millions among games played by players who have the position Guard. | SELECT avg(Units_sold_Millions) FROM game AS T1 JOIN game_player AS T2 ON T1.Game_ID = T2.Game_ID JOIN player AS T3 ON T2.Player_ID = T3.Player_ID WHERE T3.Position = "Guard" | [
"Return",
"the",
"average",
"number",
"of",
"units",
"sold",
"in",
"millions",
"among",
"games",
"played",
"by",
"players",
"who",
"have",
"the",
"position",
"Guard",
"."
] | [
{
"id": 3,
"type": "column",
"value": "units_sold_millions"
},
{
"id": 5,
"type": "table",
"value": "game_player"
},
{
"id": 6,
"type": "column",
"value": "player_id"
},
{
"id": 1,
"type": "column",
"value": "position"
},
{
"id": 7,
"type": "co... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
18
]
},
{
"entity_id": 3,
"token_idxs": [
5,
6,
7,
8
]
},
{
"entity_id": 4,
"token_i... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
10,858 | bakery_1 | bird:test.json:1552 | What is the full name of the customer who visited on the earliest date? | SELECT T1.FirstName , T1.LastName FROM customers AS T1 JOIN receipts AS T2 ON T1.id = T2.CustomerId ORDER BY T2.date LIMIT 1 | [
"What",
"is",
"the",
"full",
"name",
"of",
"the",
"customer",
"who",
"visited",
"on",
"the",
"earliest",
"date",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "customerid"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 2,
"type": "table",
"value": "customers"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 3,
"type": "table",
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,859 | document_management | spider:train_spider.json:4534 | What is the description of the most popular role among users that have logged in? | SELECT role_description FROM ROLES WHERE role_code = (SELECT role_code FROM users WHERE user_login = 1 GROUP BY role_code ORDER BY count(*) DESC LIMIT 1) | [
"What",
"is",
"the",
"description",
"of",
"the",
"most",
"popular",
"role",
"among",
"users",
"that",
"have",
"logged",
"in",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "role_description"
},
{
"id": 4,
"type": "column",
"value": "user_login"
},
{
"id": 2,
"type": "column",
"value": "role_code"
},
{
"id": 0,
"type": "table",
"value": "roles"
},
{
"id": 3,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"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,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
10,860 | retails | bird:train.json:6754 | Provide the phone number of the customer with the highest total price in an order. | SELECT T2.c_phone FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey ORDER BY T1.o_totalprice DESC LIMIT 1 | [
"Provide",
"the",
"phone",
"number",
"of",
"the",
"customer",
"with",
"the",
"highest",
"total",
"price",
"in",
"an",
"order",
"."
] | [
{
"id": 3,
"type": "column",
"value": "o_totalprice"
},
{
"id": 4,
"type": "column",
"value": "o_custkey"
},
{
"id": 5,
"type": "column",
"value": "c_custkey"
},
{
"id": 2,
"type": "table",
"value": "customer"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
10,861 | regional_sales | bird:train.json:2575 | Which region has the most number of sales team? | SELECT Region FROM `Sales Team` GROUP BY Region ORDER BY COUNT(DISTINCT `Sales Team`) DESC LIMIT 1 | [
"Which",
"region",
"has",
"the",
"most",
"number",
"of",
"sales",
"team",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "Sales Team"
},
{
"id": 2,
"type": "column",
"value": "Sales Team"
},
{
"id": 1,
"type": "column",
"value": "region"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
10,863 | hr_1 | spider:train_spider.json:3427 | Display the first and last name, and salary for those employees whose first name is ending with the letter m. | SELECT first_name , last_name , salary FROM employees WHERE first_name LIKE '%m' | [
"Display",
"the",
"first",
"and",
"last",
"name",
",",
"and",
"salary",
"for",
"those",
"employees",
"whose",
"first",
"name",
"is",
"ending",
"with",
"the",
"letter",
"m."
] | [
{
"id": 1,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type": "table",
"value": "employees"
},
{
"id": 2,
"type": "column",
"value": "last_name"
},
{
"id": 3,
"type": "column",
"value": "salary"
},
{
"id": 4,
"type": "value",
"va... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
13,
14
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,864 | film_rank | spider:train_spider.json:4113 | Count the number of films. | SELECT count(*) FROM film | [
"Count",
"the",
"number",
"of",
"films",
"."
] | [
{
"id": 0,
"type": "table",
"value": "film"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,865 | art_1 | bird:test.json:1295 | When did each artist who created less than 4 paintings die ? | select t1.deathyear from artists as t1 join paintings as t2 on t1.artistid = t2.painterid group by t2.painterid having count(*) < 4 | [
"When",
"did",
"each",
"artist",
"who",
"created",
"less",
"than",
"4",
"paintings",
"die",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "painterid"
},
{
"id": 1,
"type": "column",
"value": "deathyear"
},
{
"id": 3,
"type": "table",
"value": "paintings"
},
{
"id": 5,
"type": "column",
"value": "artistid"
},
{
"id": 2,
"type": "table",
"v... | [
{
"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": [
8
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O"
] |
10,866 | school_finance | spider:train_spider.json:1891 | Show the average, maximum, minimum enrollment of all schools. | SELECT avg(enrollment) , max(enrollment) , min(enrollment) FROM school | [
"Show",
"the",
"average",
",",
"maximum",
",",
"minimum",
"enrollment",
"of",
"all",
"schools",
"."
] | [
{
"id": 1,
"type": "column",
"value": "enrollment"
},
{
"id": 0,
"type": "table",
"value": "school"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"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",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
10,867 | public_review_platform | bird:train.json:4078 | Describe ID and active status of the business under category of "Diagnostic Imaging". | SELECT T2.business_id, T3.active FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T1.category_name = 'Diagnostic Imaging' | [
"Describe",
"ID",
"and",
"active",
"status",
"of",
"the",
"business",
"under",
"category",
"of",
"\"",
"Diagnostic",
"Imaging",
"\"",
"."
] | [
{
"id": 6,
"type": "table",
"value": "business_categories"
},
{
"id": 4,
"type": "value",
"value": "Diagnostic Imaging"
},
{
"id": 3,
"type": "column",
"value": "category_name"
},
{
"id": 0,
"type": "column",
"value": "business_id"
},
{
"id": 7,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12,
13
]
},
{
"entity_id... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
10,869 | hockey | bird:train.json:7754 | Among the teams that had more wins than loses in the year 2006, how many of them have over 100 points? | SELECT COUNT(tmID) FROM Teams WHERE year = 2006 AND W > L AND Pts > 100 | [
"Among",
"the",
"teams",
"that",
"had",
"more",
"wins",
"than",
"loses",
"in",
"the",
"year",
"2006",
",",
"how",
"many",
"of",
"them",
"have",
"over",
"100",
"points",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "teams"
},
{
"id": 1,
"type": "column",
"value": "tmid"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 3,
"type": "value",
"value": "2006"
},
{
"id": 6,
"type": "column",
"value": "pts"
},
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,870 | cinema | spider:train_spider.json:1933 | Count the number of cinemas. | SELECT count(*) FROM cinema | [
"Count",
"the",
"number",
"of",
"cinemas",
"."
] | [
{
"id": 0,
"type": "table",
"value": "cinema"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,871 | college_1 | spider:train_spider.json:3204 | For each classroom with at least 2 classes, how many classes are offered? | SELECT count(*) , class_room FROM CLASS GROUP BY class_room HAVING count(*) >= 2 | [
"For",
"each",
"classroom",
"with",
"at",
"least",
"2",
"classes",
",",
"how",
"many",
"classes",
"are",
"offered",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "class_room"
},
{
"id": 0,
"type": "table",
"value": "class"
},
{
"id": 2,
"type": "value",
"value": "2"
}
] | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
10,872 | food_inspection | bird:train.json:8832 | Provide the names, risk categories and descriptions for the eateries with violation type ID of 103111. | SELECT T2.name, T1.risk_category, T1.description FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.violation_type_id = '103111' | [
"Provide",
"the",
"names",
",",
"risk",
"categories",
"and",
"descriptions",
"for",
"the",
"eateries",
"with",
"violation",
"type",
"ID",
"of",
"103111",
"."
] | [
{
"id": 5,
"type": "column",
"value": "violation_type_id"
},
{
"id": 1,
"type": "column",
"value": "risk_category"
},
{
"id": 2,
"type": "column",
"value": "description"
},
{
"id": 7,
"type": "column",
"value": "business_id"
},
{
"id": 3,
"type... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,873 | california_schools | bird:dev.json:34 | What is the free rate for students between the ages of 5 and 17 at the school run by Kacey Gibson? | SELECT CAST(T2.`Free Meal Count (Ages 5-17)` AS REAL) / T2.`Enrollment (Ages 5-17)` FROM schools AS T1 INNER JOIN frpm AS T2 ON T1.CDSCode = T2.CDSCode WHERE T1.AdmFName1 = 'Kacey' AND T1.AdmLName1 = 'Gibson' | [
"What",
"is",
"the",
"free",
"rate",
"for",
"students",
"between",
"the",
"ages",
"of",
"5",
"and",
"17",
"at",
"the",
"school",
"run",
"by",
"Kacey",
"Gibson",
"?"
] | [
{
"id": 8,
"type": "column",
"value": "Free Meal Count (Ages 5-17)"
},
{
"id": 2,
"type": "column",
"value": "Enrollment (Ages 5-17)"
},
{
"id": 4,
"type": "column",
"value": "admfname1"
},
{
"id": 6,
"type": "column",
"value": "admlname1"
},
{
"id... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"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": [
19
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O"
] |
10,874 | club_1 | spider:train_spider.json:4283 | What is the description of the club "Pen and Paper Gaming"? | SELECT clubdesc FROM club WHERE clubname = "Pen and Paper Gaming" | [
"What",
"is",
"the",
"description",
"of",
"the",
"club",
"\"",
"Pen",
"and",
"Paper",
"Gaming",
"\"",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "Pen and Paper Gaming"
},
{
"id": 1,
"type": "column",
"value": "clubdesc"
},
{
"id": 2,
"type": "column",
"value": "clubname"
},
{
"id": 0,
"type": "table",
"value": "club"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10,
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"enti... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O"
] |
10,875 | european_football_1 | bird:train.json:2778 | List the number of games that ended up with 5-0 in Greece. | SELECT COUNT(T1.Div) FROM matchs AS T1 INNER JOIN divisions AS T2 ON T1.Div = T2.division WHERE T2.country = 'Greece' AND T1.FTHG = 5 AND T1.FTAG = 0 | [
"List",
"the",
"number",
"of",
"games",
"that",
"ended",
"up",
"with",
"5",
"-",
"0",
"in",
"Greece",
"."
] | [
{
"id": 1,
"type": "table",
"value": "divisions"
},
{
"id": 3,
"type": "column",
"value": "division"
},
{
"id": 4,
"type": "column",
"value": "country"
},
{
"id": 0,
"type": "table",
"value": "matchs"
},
{
"id": 5,
"type": "value",
"value":... | [
{
"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": [
13
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
10,877 | olympics | bird:train.json:4932 | Which game has Jessica Carolina Aguilera Aguilera participated in? Give the id of the game. | SELECT T2.games_id FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id WHERE T1.full_name = 'Jessica Carolina Aguilera Aguilera' | [
"Which",
"game",
"has",
"Jessica",
"Carolina",
"Aguilera",
"Aguilera",
"participated",
"in",
"?",
"Give",
"the",
"i",
"d",
"of",
"the",
"game",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Jessica Carolina Aguilera Aguilera"
},
{
"id": 2,
"type": "table",
"value": "games_competitor"
},
{
"id": 3,
"type": "column",
"value": "full_name"
},
{
"id": 6,
"type": "column",
"value": "person_id"
},
{
"id"... | [
{
"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": [
3,
4,
5,
6
]
},
{
"entity... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
10,878 | college_1 | spider:train_spider.json:3290 | What is the number of professors who are in the Accounting or Biology departments? | SELECT count(*) FROM professor AS T1 JOIN department AS T2 ON T1.dept_code = T2.dept_code WHERE T2.dept_name = 'Accounting' OR T2.dept_name = 'Biology' | [
"What",
"is",
"the",
"number",
"of",
"professors",
"who",
"are",
"in",
"the",
"Accounting",
"or",
"Biology",
"departments",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "department"
},
{
"id": 4,
"type": "value",
"value": "Accounting"
},
{
"id": 0,
"type": "table",
"value": "professor"
},
{
"id": 2,
"type": "column",
"value": "dept_code"
},
{
"id": 3,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,879 | soccer_2016 | bird:train.json:1985 | Count the matches with a total of two innings. | SELECT COUNT(Match_Id) FROM Wicket_Taken WHERE innings_no = 2 | [
"Count",
"the",
"matches",
"with",
"a",
"total",
"of",
"two",
"innings",
"."
] | [
{
"id": 0,
"type": "table",
"value": "wicket_taken"
},
{
"id": 1,
"type": "column",
"value": "innings_no"
},
{
"id": 3,
"type": "column",
"value": "match_id"
},
{
"id": 2,
"type": "value",
"value": "2"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,880 | public_review_platform | bird:train.json:3861 | Please list all the categories of the Yelp_Business in Arizona. | SELECT T1.category_name FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id WHERE T3.state LIKE 'AZ' GROUP BY T1.category_name | [
"Please",
"list",
"all",
"the",
"categories",
"of",
"the",
"Yelp_Business",
"in",
"Arizona",
"."
] | [
{
"id": 5,
"type": "table",
"value": "business_categories"
},
{
"id": 0,
"type": "column",
"value": "category_name"
},
{
"id": 6,
"type": "column",
"value": "business_id"
},
{
"id": 7,
"type": "column",
"value": "category_id"
},
{
"id": 4,
"typ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
1,
2,
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"en... | [
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O"
] |
10,881 | hockey | bird:train.json:7774 | In the year 2000, which team has played the most games against the Buffalo Sabres? | SELECT T3.name FROM TeamVsTeam AS T1 INNER JOIN Teams AS T2 ON T1.year = T2.year AND T1.oppID = T2.tmID INNER JOIN Teams AS T3 ON T1.year = T3.year AND T1.tmID = T3.tmID WHERE T1.year = 2000 AND T2.name = 'Buffalo Sabres' GROUP BY T3.name ORDER BY SUM(T2.G) DESC LIMIT 1 | [
"In",
"the",
"year",
"2000",
",",
"which",
"team",
"has",
"played",
"the",
"most",
"games",
"against",
"the",
"Buffalo",
"Sabres",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Buffalo Sabres"
},
{
"id": 2,
"type": "table",
"value": "teamvsteam"
},
{
"id": 1,
"type": "table",
"value": "teams"
},
{
"id": 8,
"type": "column",
"value": "oppid"
},
{
"id": 0,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
3
... | [
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
10,883 | toxicology | bird:dev.json:298 | Calculate the percentage of molecules containing carcinogenic compounds that element is hydrogen. | SELECT CAST(COUNT(CASE WHEN T1.element = 'h' AND T2.label = '+' THEN T2.molecule_id ELSE NULL END) AS REAL) * 100 / COUNT(T2.molecule_id) FROM atom AS T1 INNER JOIN molecule AS T2 ON T1.molecule_id = T2.molecule_id | [
"Calculate",
"the",
"percentage",
"of",
"molecules",
"containing",
"carcinogenic",
"compounds",
"that",
"element",
"is",
"hydrogen",
"."
] | [
{
"id": 2,
"type": "column",
"value": "molecule_id"
},
{
"id": 1,
"type": "table",
"value": "molecule"
},
{
"id": 4,
"type": "column",
"value": "element"
},
{
"id": 6,
"type": "column",
"value": "label"
},
{
"id": 0,
"type": "table",
"value... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
10,884 | retail_complains | bird:train.json:325 | Please list any two clients with their full names who have been tagged as "Older American" by the company without seeking their permission. | SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T2.Tags = 'Older American' AND T2.`Consumer consent provided?` IN (NULL, 'N/A', '') LIMIT 2 | [
"Please",
"list",
"any",
"two",
"clients",
"with",
"their",
"full",
"names",
"who",
"have",
"been",
"tagged",
"as",
"\"",
"Older",
"American",
"\"",
"by",
"the",
"company",
"without",
"seeking",
"their",
"permission",
"."
] | [
{
"id": 8,
"type": "column",
"value": "Consumer consent provided?"
},
{
"id": 7,
"type": "value",
"value": "Older American"
},
{
"id": 5,
"type": "column",
"value": "client_id"
},
{
"id": 1,
"type": "column",
"value": "middle"
},
{
"id": 3,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,885 | sakila_1 | spider:train_spider.json:2994 | What is the first name and the last name of the customer who made the earliest rental? | SELECT T1.first_name , T1.last_name FROM customer AS T1 JOIN rental AS T2 ON T1.customer_id = T2.customer_id ORDER BY T2.rental_date ASC LIMIT 1 | [
"What",
"is",
"the",
"first",
"name",
"and",
"the",
"last",
"name",
"of",
"the",
"customer",
"who",
"made",
"the",
"earliest",
"rental",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "rental_date"
},
{
"id": 5,
"type": "column",
"value": "customer_id"
},
{
"id": 0,
"type": "column",
"value": "first_name"
},
{
"id": 1,
"type": "column",
"value": "last_name"
},
{
"id": 2,
"type": "table",... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
7,
8
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,886 | european_football_2 | bird:dev.json:1102 | For the players who had a 77 points overall rating on 2016/6/23, who was the oldest? Give the name of the player. | SELECT t1.player_name FROM Player AS t1 INNER JOIN Player_Attributes AS t2 ON t1.player_api_id = t2.player_api_id WHERE SUBSTR(t2.`date`, 1, 10) = '2016-06-23' AND t2.overall_rating = 77 ORDER BY t1.birthday ASC LIMIT 1 | [
"For",
"the",
"players",
"who",
"had",
"a",
"77",
"points",
"overall",
"rating",
"on",
"2016/6/23",
",",
"who",
"was",
"the",
"oldest",
"?",
"Give",
"the",
"name",
"of",
"the",
"player",
"."
] | [
{
"id": 2,
"type": "table",
"value": "player_attributes"
},
{
"id": 6,
"type": "column",
"value": "overall_rating"
},
{
"id": 4,
"type": "column",
"value": "player_api_id"
},
{
"id": 0,
"type": "column",
"value": "player_name"
},
{
"id": 5,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
23
]
},
{
"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",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,887 | world | bird:train.json:7891 | Provide the district of the city with a population of 201843. | SELECT District FROM City WHERE population = 201843 | [
"Provide",
"the",
"district",
"of",
"the",
"city",
"with",
"a",
"population",
"of",
"201843",
"."
] | [
{
"id": 2,
"type": "column",
"value": "population"
},
{
"id": 1,
"type": "column",
"value": "district"
},
{
"id": 3,
"type": "value",
"value": "201843"
},
{
"id": 0,
"type": "table",
"value": "city"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,888 | retail_world | bird:train.json:6394 | Of the customers who are from Canada, how many used Federal Shipping? | SELECT COUNT(T3.CustomerID) FROM Shippers AS T1 INNER JOIN Orders AS T2 ON T1.ShipperID = T2.ShipVia INNER JOIN Customers AS T3 ON T2.CustomerID = T3.CustomerID WHERE T2.ShipName = 'Federal Shipping' | [
"Of",
"the",
"customers",
"who",
"are",
"from",
"Canada",
",",
"how",
"many",
"used",
"Federal",
"Shipping",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Federal Shipping"
},
{
"id": 3,
"type": "column",
"value": "customerid"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 6,
"type": "column",
"value": "shipperid"
},
{
"id": 1,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
10,889 | apartment_rentals | spider:train_spider.json:1210 | Return the apartment number and the number of rooms for each apartment. | SELECT apt_number , room_count FROM Apartments | [
"Return",
"the",
"apartment",
"number",
"and",
"the",
"number",
"of",
"rooms",
"for",
"each",
"apartment",
"."
] | [
{
"id": 0,
"type": "table",
"value": "apartments"
},
{
"id": 1,
"type": "column",
"value": "apt_number"
},
{
"id": 2,
"type": "column",
"value": "room_count"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"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",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,890 | scientist_1 | spider:train_spider.json:6500 | What are the names of the scientists, and how many projects are each of them working on? | SELECT count(*) , T1.name FROM scientists AS T1 JOIN assignedto AS T2 ON T1.ssn = T2.scientist GROUP BY T1.name | [
"What",
"are",
"the",
"names",
"of",
"the",
"scientists",
",",
"and",
"how",
"many",
"projects",
"are",
"each",
"of",
"them",
"working",
"on",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "scientists"
},
{
"id": 2,
"type": "table",
"value": "assignedto"
},
{
"id": 4,
"type": "column",
"value": "scientist"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
10,891 | pilot_1 | bird:test.json:1108 | Count the number of different locations of hangars. | SELECT count(DISTINCT LOCATION) FROM hangar | [
"Count",
"the",
"number",
"of",
"different",
"locations",
"of",
"hangars",
"."
] | [
{
"id": 1,
"type": "column",
"value": "location"
},
{
"id": 0,
"type": "table",
"value": "hangar"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O"
] |
10,892 | public_review_platform | bird:train.json:4054 | Provide business ids with opening hours 10AM on Saturday. | SELECT DISTINCT business_id FROM Business_Hours WHERE day_id = 6 AND opening_time = '10AM' | [
"Provide",
"business",
"ids",
"with",
"opening",
"hours",
"10AM",
"on",
"Saturday",
"."
] | [
{
"id": 0,
"type": "table",
"value": "business_hours"
},
{
"id": 4,
"type": "column",
"value": "opening_time"
},
{
"id": 1,
"type": "column",
"value": "business_id"
},
{
"id": 2,
"type": "column",
"value": "day_id"
},
{
"id": 5,
"type": "value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1,
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
10,893 | world_development_indicators | bird:train.json:2237 | By how much did the indicator on Adolescent fertility rate increase from 1960 to 1961 in the country whose Alpha2Code is 1A? | SELECT ( SELECT T2.Value FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.Alpha2Code = '1A' AND T2.IndicatorName = 'Adolescent fertility rate (births per 1,000 women ages 15-19)' AND T2.Year = 1961 ) - ( SELECT T2.Value FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.Count... | [
"By",
"how",
"much",
"did",
"the",
"indicator",
"on",
"Adolescent",
"fertility",
"rate",
"increase",
"from",
"1960",
"to",
"1961",
"in",
"the",
"country",
"whose",
"Alpha2Code",
"is",
"1A",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "Adolescent fertility rate (births per 1,000 women ages 15-19)"
},
{
"id": 6,
"type": "column",
"value": "indicatorname"
},
{
"id": 3,
"type": "column",
"value": "countrycode"
},
{
"id": 2,
"type": "table",
"value": "in... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,895 | college_2 | spider:train_spider.json:1408 | Find the name of department has the highest amount of students? | SELECT dept_name FROM student GROUP BY dept_name ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"name",
"of",
"department",
"has",
"the",
"highest",
"amount",
"of",
"students",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "dept_name"
},
{
"id": 0,
"type": "table",
"value": "student"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
10,896 | mental_health_survey | bird:train.json:4571 | How many users answered "No" to question 19? | SELECT COUNT(QuestionID) FROM Answer WHERE QuestionID = 19 AND AnswerText LIKE 'No' | [
"How",
"many",
"users",
"answered",
"\"",
"No",
"\"",
"to",
"question",
"19",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "questionid"
},
{
"id": 3,
"type": "column",
"value": "answertext"
},
{
"id": 0,
"type": "table",
"value": "answer"
},
{
"id": 2,
"type": "value",
"value": "19"
},
{
"id": 4,
"type": "value",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
10,897 | human_resources | bird:train.json:8976 | List the location cities in the Western states. | SELECT locationcity FROM location WHERE state IN ('CO', 'UT', 'CA') | [
"List",
"the",
"location",
"cities",
"in",
"the",
"Western",
"states",
"."
] | [
{
"id": 1,
"type": "column",
"value": "locationcity"
},
{
"id": 0,
"type": "table",
"value": "location"
},
{
"id": 2,
"type": "column",
"value": "state"
},
{
"id": 3,
"type": "value",
"value": "CO"
},
{
"id": 4,
"type": "value",
"value": "U... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"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",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
10,898 | storm_record | spider:train_spider.json:2704 | What is the total number of deaths and damage for all storms with a max speed greater than the average? | SELECT sum(number_deaths) , sum(damage_millions_USD) FROM storm WHERE max_speed > (SELECT avg(max_speed) FROM storm) | [
"What",
"is",
"the",
"total",
"number",
"of",
"deaths",
"and",
"damage",
"for",
"all",
"storms",
"with",
"a",
"max",
"speed",
"greater",
"than",
"the",
"average",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "damage_millions_usd"
},
{
"id": 2,
"type": "column",
"value": "number_deaths"
},
{
"id": 1,
"type": "column",
"value": "max_speed"
},
{
"id": 0,
"type": "table",
"value": "storm"
}
] | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
14,
15
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
10,899 | works_cycles | bird:train.json:7454 | How many products with a thumpnail photo? | SELECT COUNT(ProductID) FROM ProductProductPhoto WHERE ProductPhotoID != 1 | [
"How",
"many",
"products",
"with",
"a",
"thumpnail",
"photo",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "productproductphoto"
},
{
"id": 1,
"type": "column",
"value": "productphotoid"
},
{
"id": 3,
"type": "column",
"value": "productid"
},
{
"id": 2,
"type": "value",
"value": "1"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
10,900 | law_episode | bird:train.json:1268 | Display the number of 9-star votes the episode Sideshow received. | SELECT T2.votes FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T2.stars = 9 AND T1.title = 'Sideshow' | [
"Display",
"the",
"number",
"of",
"9",
"-",
"star",
"votes",
"the",
"episode",
"Sideshow",
"received",
"."
] | [
{
"id": 3,
"type": "column",
"value": "episode_id"
},
{
"id": 7,
"type": "value",
"value": "Sideshow"
},
{
"id": 1,
"type": "table",
"value": "episode"
},
{
"id": 0,
"type": "column",
"value": "votes"
},
{
"id": 4,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-TABLE",
"B-VALUE",
"O",
"O"
] |
10,901 | cs_semester | bird:train.json:864 | Please list the names of the courses taken by Laughton Antonio. | SELECT T3.name FROM student AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T1.f_name = 'Laughton' AND T1.l_name = 'Antonio' | [
"Please",
"list",
"the",
"names",
"of",
"the",
"courses",
"taken",
"by",
"Laughton",
"Antonio",
"."
] | [
{
"id": 3,
"type": "table",
"value": "registration"
},
{
"id": 9,
"type": "column",
"value": "student_id"
},
{
"id": 4,
"type": "column",
"value": "course_id"
},
{
"id": 6,
"type": "value",
"value": "Laughton"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O"
] |
10,902 | vehicle_driver | bird:test.json:172 | Return the names of drivers who have driven vehicles with power over 5000. | SELECT DISTINCT T1.Name FROM driver AS T1 JOIN vehicle_driver AS T2 ON T1.driver_id = T2.driver_id JOIN vehicle AS T3 ON T2.vehicle_id = T3.vehicle_id WHERE T3.power > 5000 | [
"Return",
"the",
"names",
"of",
"drivers",
"who",
"have",
"driven",
"vehicles",
"with",
"power",
"over",
"5000",
"."
] | [
{
"id": 5,
"type": "table",
"value": "vehicle_driver"
},
{
"id": 6,
"type": "column",
"value": "vehicle_id"
},
{
"id": 7,
"type": "column",
"value": "driver_id"
},
{
"id": 1,
"type": "table",
"value": "vehicle"
},
{
"id": 4,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
10,903 | olympics | bird:train.json:4942 | How many 24 years old competitors competed in Men's Basketball? | SELECT COUNT(T2.person_id) FROM competitor_event AS T1 INNER JOIN games_competitor AS T2 ON T1.competitor_id = T2.id INNER JOIN event AS T3 ON T1.event_id = T3.id WHERE T3.event_name LIKE 'Basketball Men%s Basketball' AND T2.age = 24 | [
"How",
"many",
"24",
"years",
"old",
"competitors",
"competed",
"in",
"Men",
"'s",
"Basketball",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "Basketball Men%s Basketball"
},
{
"id": 2,
"type": "table",
"value": "competitor_event"
},
{
"id": 3,
"type": "table",
"value": "games_competitor"
},
{
"id": 10,
"type": "column",
"value": "competitor_id"
},
{
... | [
{
"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",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
10,904 | movielens | bird:train.json:2253 | List the top 10 USA movies, by descending order, from the highest to the lowest, the user rating. | SELECT T1.movieid FROM u2base AS T1 INNER JOIN movies AS T2 ON T1.movieid = T2.movieid WHERE T2.country = 'USA' GROUP BY T1.movieid ORDER BY AVG(T1.rating) DESC LIMIT 10 | [
"List",
"the",
"top",
"10",
"USA",
"movies",
",",
"by",
"descending",
"order",
",",
"from",
"the",
"highest",
"to",
"the",
"lowest",
",",
"the",
"user",
"rating",
"."
] | [
{
"id": 0,
"type": "column",
"value": "movieid"
},
{
"id": 3,
"type": "column",
"value": "country"
},
{
"id": 1,
"type": "table",
"value": "u2base"
},
{
"id": 2,
"type": "table",
"value": "movies"
},
{
"id": 5,
"type": "column",
"value": "r... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
19
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
10,905 | books | bird:train.json:6010 | How many customers have an address that is located in the city of Villeneuve-la-Garenne? | SELECT COUNT(address_id) FROM address WHERE city = 'Villeneuve-la-Garenne' | [
"How",
"many",
"customers",
"have",
"an",
"address",
"that",
"is",
"located",
"in",
"the",
"city",
"of",
"Villeneuve",
"-",
"la",
"-",
"Garenne",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Villeneuve-la-Garenne"
},
{
"id": 3,
"type": "column",
"value": "address_id"
},
{
"id": 0,
"type": "table",
"value": "address"
},
{
"id": 1,
"type": "column",
"value": "city"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
13,
14,
15,
16,
17
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_id... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
10,906 | retails | bird:train.json:6911 | What are the total quantities of the items ordered by customer 101660 on 10/5/1995? | SELECT SUM(T2.l_quantity) FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T1.o_orderdate = '1995-10-05' AND T1.o_custkey = 101660 | [
"What",
"are",
"the",
"total",
"quantities",
"of",
"the",
"items",
"ordered",
"by",
"customer",
"101660",
"on",
"10/5/1995",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "o_orderdate"
},
{
"id": 2,
"type": "column",
"value": "l_quantity"
},
{
"id": 3,
"type": "column",
"value": "o_orderkey"
},
{
"id": 4,
"type": "column",
"value": "l_orderkey"
},
{
"id": 6,
"type": "value",... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"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,
"... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O"
] |
10,907 | music_2 | spider:train_spider.json:5271 | How many songs appear in studio albums? | SELECT count(DISTINCT T3.title) FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid = T2.albumid JOIN songs AS T3 ON T2.songid = T3.songid WHERE t1.type = "Studio" | [
"How",
"many",
"songs",
"appear",
"in",
"studio",
"albums",
"?"
] | [
{
"id": 5,
"type": "table",
"value": "tracklists"
},
{
"id": 8,
"type": "column",
"value": "albumid"
},
{
"id": 2,
"type": "column",
"value": "Studio"
},
{
"id": 4,
"type": "table",
"value": "albums"
},
{
"id": 6,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
10,908 | ice_hockey_draft | bird:train.json:6998 | Calculate the percentage of penalty minutes of Swedish players in OHL league among all players. | SELECT CAST(COUNT(CASE WHEN T1.nation = 'Sweden' THEN T2.PIM ELSE NULL END) AS REAL) * 100 / COUNT(*) FROM PlayerInfo AS T1 INNER JOIN SeasonStatus AS T2 ON T1.ELITEID = T2.ELITEID WHERE T2.LEAGUE = 'OHL' | [
"Calculate",
"the",
"percentage",
"of",
"penalty",
"minutes",
"of",
"Swedish",
"players",
"in",
"OHL",
"league",
"among",
"all",
"players",
"."
] | [
{
"id": 1,
"type": "table",
"value": "seasonstatus"
},
{
"id": 0,
"type": "table",
"value": "playerinfo"
},
{
"id": 4,
"type": "column",
"value": "eliteid"
},
{
"id": 2,
"type": "column",
"value": "league"
},
{
"id": 7,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": [
8,
9
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"I-TABLE",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
10,909 | retail_complains | bird:train.json:371 | What is the longest server time when the call is about the issue of arbitration? | SELECT MAX(T1.ser_time) FROM callcenterlogs AS T1 INNER JOIN events AS T2 ON T1.`Complaint ID` = T2.`Complaint ID` WHERE T2.issue = 'Arbitration' | [
"What",
"is",
"the",
"longest",
"server",
"time",
"when",
"the",
"call",
"is",
"about",
"the",
"issue",
"of",
"arbitration",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "callcenterlogs"
},
{
"id": 5,
"type": "column",
"value": "Complaint ID"
},
{
"id": 3,
"type": "value",
"value": "Arbitration"
},
{
"id": 4,
"type": "column",
"value": "ser_time"
},
{
"id": 1,
"type": "table... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": [
4,
5
]
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.