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 |
|---|---|---|---|---|---|---|---|---|
4,627 | image_and_language | bird:train.json:7496 | Give the number of samples in image no.2377985 whose attribute is electrical. | SELECT SUM(CASE WHEN T2.ATT_CLASS = 'white' THEN 1 ELSE 0 END) FROM IMG_OBJ_ATT AS T1 INNER JOIN ATT_CLASSES AS T2 ON T1.ATT_CLASS_ID = T2.ATT_CLASS_ID WHERE T1.IMG_ID = 2347915 | [
"Give",
"the",
"number",
"of",
"samples",
"in",
"image",
"no.2377985",
"whose",
"attribute",
"is",
"electrical",
"."
] | [
{
"id": 4,
"type": "column",
"value": "att_class_id"
},
{
"id": 0,
"type": "table",
"value": "img_obj_att"
},
{
"id": 1,
"type": "table",
"value": "att_classes"
},
{
"id": 7,
"type": "column",
"value": "att_class"
},
{
"id": 3,
"type": "value",... | [
{
"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": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
4,628 | book_press | bird:test.json:2010 | Who wrote the best selling book? Give me the author name. | SELECT t1.name FROM author AS t1 JOIN book AS t2 ON t1.author_id = t2.author_id ORDER BY t2.sale_amount DESC LIMIT 1 | [
"Who",
"wrote",
"the",
"best",
"selling",
"book",
"?",
"Give",
"me",
"the",
"author",
"name",
"."
] | [
{
"id": 3,
"type": "column",
"value": "sale_amount"
},
{
"id": 4,
"type": "column",
"value": "author_id"
},
{
"id": 1,
"type": "table",
"value": "author"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "table",
"value"... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
4,629 | video_games | bird:train.json:3464 | Calculate the difference between sales of games from region ID 2 and region ID 3. | SELECT SUM(CASE WHEN T.region_id = 2 THEN T.num_sales ELSE 0 END) - SUM(CASE WHEN T.region_id = 3 THEN T.num_sales ELSE 0 END) FROM region_sales t | [
"Calculate",
"the",
"difference",
"between",
"sales",
"of",
"games",
"from",
"region",
"ID",
"2",
"and",
"region",
"ID",
"3",
"."
] | [
{
"id": 0,
"type": "table",
"value": "region_sales"
},
{
"id": 2,
"type": "column",
"value": "num_sales"
},
{
"id": 3,
"type": "column",
"value": "region_id"
},
{
"id": 1,
"type": "value",
"value": "0"
},
{
"id": 4,
"type": "value",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_i... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
4,630 | customers_and_invoices | spider:train_spider.json:1604 | Count the number of invoices. | SELECT count(*) FROM Invoices | [
"Count",
"the",
"number",
"of",
"invoices",
"."
] | [
{
"id": 0,
"type": "table",
"value": "invoices"
}
] | [
{
"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"
] |
4,631 | retails | bird:train.json:6900 | How many customers in the machinery segment are in debt? | SELECT COUNT(c_custkey) FROM customer WHERE c_acctbal < 0 AND c_mktsegment = 'MACHINERY' | [
"How",
"many",
"customers",
"in",
"the",
"machinery",
"segment",
"are",
"in",
"debt",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 1,
"type": "column",
"value": "c_custkey"
},
{
"id": 2,
"type": "column",
"value": "c_acctbal"
},
{
"id": 5,
"type": "value",
"value": "MACHINERY"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
4,632 | world | bird:train.json:7874 | Write down the name of the largest population country. | SELECT Name FROM Country ORDER BY Population DESC LIMIT 1 | [
"Write",
"down",
"the",
"name",
"of",
"the",
"largest",
"population",
"country",
"."
] | [
{
"id": 2,
"type": "column",
"value": "population"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"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",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
4,633 | food_inspection_2 | bird:train.json:6125 | Which employee was responsible for inspection no.48224? Give the full name. | SELECT T2.first_name, T2.last_name FROM inspection AS T1 INNER JOIN employee AS T2 ON T1.employee_id = T2.employee_id WHERE T1.inspection_id = 48224 | [
"Which",
"employee",
"was",
"responsible",
"for",
"inspection",
"no.48224",
"?",
"Give",
"the",
"full",
"name",
"."
] | [
{
"id": 4,
"type": "column",
"value": "inspection_id"
},
{
"id": 6,
"type": "column",
"value": "employee_id"
},
{
"id": 0,
"type": "column",
"value": "first_name"
},
{
"id": 2,
"type": "table",
"value": "inspection"
},
{
"id": 1,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
4,634 | law_episode | bird:train.json:1278 | What are the keywords of the "Shield" episode? | SELECT T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Shield' | [
"What",
"are",
"the",
"keywords",
"of",
"the",
"\"",
"Shield",
"\"",
"episode",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "episode_id"
},
{
"id": 0,
"type": "column",
"value": "keyword"
},
{
"id": 1,
"type": "table",
"value": "episode"
},
{
"id": 2,
"type": "table",
"value": "keyword"
},
{
"id": 4,
"type": "value",
"value"... | [
{
"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": [
7
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"O"
] |
4,635 | bike_racing | bird:test.json:1470 | How many bikes are heavier than 780 grams? | SELECT count(*) FROM bike WHERE weight > 780 | [
"How",
"many",
"bikes",
"are",
"heavier",
"than",
"780",
"grams",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "weight"
},
{
"id": 0,
"type": "table",
"value": "bike"
},
{
"id": 2,
"type": "value",
"value": "780"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
4,636 | roller_coaster | spider:train_spider.json:6216 | Show the name and population of the country that has the highest roller coaster. | SELECT T1.Name , T1.population FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID = T2.Country_ID ORDER BY T2.Height DESC LIMIT 1 | [
"Show",
"the",
"name",
"and",
"population",
"of",
"the",
"country",
"that",
"has",
"the",
"highest",
"roller",
"coaster",
"."
] | [
{
"id": 3,
"type": "table",
"value": "roller_coaster"
},
{
"id": 1,
"type": "column",
"value": "population"
},
{
"id": 5,
"type": "column",
"value": "country_id"
},
{
"id": 2,
"type": "table",
"value": "country"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
11
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"I-TABLE",
"O"
] |
4,637 | codebase_community | bird:dev.json:664 | What is the sum of score of the post on 2010-07-19? | SELECT SUM(Score) FROM posts WHERE LasActivityDate LIKE '2010-07-19%' | [
"What",
"is",
"the",
"sum",
"of",
"score",
"of",
"the",
"post",
"on",
"2010",
"-",
"07",
"-",
"19",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "lasactivitydate"
},
{
"id": 2,
"type": "value",
"value": "2010-07-19%"
},
{
"id": 0,
"type": "table",
"value": "posts"
},
{
"id": 3,
"type": "column",
"value": "score"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10,
11,
12,
13,
14
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idx... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
4,638 | climbing | spider:train_spider.json:1122 | What are the countries of mountains with height bigger than 5000? | SELECT Country FROM mountain WHERE Height > 5000 | [
"What",
"are",
"the",
"countries",
"of",
"mountains",
"with",
"height",
"bigger",
"than",
"5000",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "mountain"
},
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 2,
"type": "column",
"value": "height"
},
{
"id": 3,
"type": "value",
"value": "5000"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
4,639 | books | bird:train.json:6057 | Provide the International Standard Book Number of the book The Mystery in the Rocky Mountains. | SELECT isbn13 FROM book WHERE title = 'The Mystery in the Rocky Mountains' | [
"Provide",
"the",
"International",
"Standard",
"Book",
"Number",
"of",
"the",
"book",
"The",
"Mystery",
"in",
"the",
"Rocky",
"Mountains",
"."
] | [
{
"id": 3,
"type": "value",
"value": "The Mystery in the Rocky Mountains"
},
{
"id": 1,
"type": "column",
"value": "isbn13"
},
{
"id": 2,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "table",
"value": "book"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10,
11,
12,
13,
14
]
},
{
"entity_id": 4,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
4,640 | baseball_1 | spider:train_spider.json:3629 | what is the full name and id of the college with the largest number of baseball players? | SELECT T1.name_full , T1.college_id FROM college AS T1 JOIN player_college AS T2 ON T1.college_id = T2.college_id GROUP BY T1.college_id ORDER BY count(*) DESC LIMIT 1; | [
"what",
"is",
"the",
"full",
"name",
"and",
"i",
"d",
"of",
"the",
"college",
"with",
"the",
"largest",
"number",
"of",
"baseball",
"players",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "player_college"
},
{
"id": 0,
"type": "column",
"value": "college_id"
},
{
"id": 1,
"type": "column",
"value": "name_full"
},
{
"id": 2,
"type": "table",
"value": "college"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"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,
"token_idxs":... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,641 | customers_and_invoices | spider:train_spider.json:1563 | Show customer ids who don't have an account. | SELECT customer_id FROM Customers EXCEPT SELECT customer_id FROM Accounts | [
"Show",
"customer",
"ids",
"who",
"do",
"n't",
"have",
"an",
"account",
"."
] | [
{
"id": 2,
"type": "column",
"value": "customer_id"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 1,
"type": "table",
"value": "accounts"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,642 | works_cycles | bird:train.json:7018 | List all product names and its product line for all purchase order with order quantity of 5000 or more. | SELECT T1.Name, T1.ProductLine FROM Product AS T1 INNER JOIN PurchaseOrderDetail AS T2 ON T1.ProductID = T2.ProductID WHERE T2.OrderQty > 4999 | [
"List",
"all",
"product",
"names",
"and",
"its",
"product",
"line",
"for",
"all",
"purchase",
"order",
"with",
"order",
"quantity",
"of",
"5000",
"or",
"more",
"."
] | [
{
"id": 3,
"type": "table",
"value": "purchaseorderdetail"
},
{
"id": 1,
"type": "column",
"value": "productline"
},
{
"id": 6,
"type": "column",
"value": "productid"
},
{
"id": 4,
"type": "column",
"value": "orderqty"
},
{
"id": 2,
"type": "ta... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
13
... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,643 | language_corpus | bird:train.json:5688 | List the titles for all Catalan language wikipedia page from revision page id 106600 to 106700. | SELECT title FROM pages WHERE revision BETWEEN 106600 AND 106700 | [
"List",
"the",
"titles",
"for",
"all",
"Catalan",
"language",
"wikipedia",
"page",
"from",
"revision",
"page",
"i",
"d",
"106600",
"to",
"106700",
"."
] | [
{
"id": 2,
"type": "column",
"value": "revision"
},
{
"id": 3,
"type": "value",
"value": "106600"
},
{
"id": 4,
"type": "value",
"value": "106700"
},
{
"id": 0,
"type": "table",
"value": "pages"
},
{
"id": 1,
"type": "column",
"value": "tit... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
4,645 | movie_platform | bird:train.json:51 | What's the description for the movie list "Short and pretty damn sweet"? | SELECT list_description FROM lists WHERE list_title = 'Short and pretty damn sweet' | [
"What",
"'s",
"the",
"description",
"for",
"the",
"movie",
"list",
"\"",
"Short",
"and",
"pretty",
"damn",
"sweet",
"\"",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Short and pretty damn sweet"
},
{
"id": 1,
"type": "column",
"value": "list_description"
},
{
"id": 2,
"type": "column",
"value": "list_title"
},
{
"id": 0,
"type": "table",
"value": "lists"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10,
11,
12,
13
]
},
{
"entity_id": 4,
"token_idxs... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
4,646 | mondial_geo | bird:train.json:8500 | Which Asian country gave its agricultural sector the largest share of its gross domestic product? | SELECT T2.Country FROM continent AS T1 INNER JOIN encompasses AS T2 ON T1.Name = T2.Continent INNER JOIN country AS T3 ON T2.Country = T3.Code INNER JOIN economy AS T4 ON T4.Country = T3.Code WHERE T1.Name = 'Asia' ORDER BY T4.Agriculture DESC LIMIT 1 | [
"Which",
"Asian",
"country",
"gave",
"its",
"agricultural",
"sector",
"the",
"largest",
"share",
"of",
"its",
"gross",
"domestic",
"product",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "agriculture"
},
{
"id": 8,
"type": "table",
"value": "encompasses"
},
{
"id": 7,
"type": "table",
"value": "continent"
},
{
"id": 9,
"type": "column",
"value": "continent"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,647 | movie_platform | bird:train.json:92 | Please provide the title of the list with the most comments on the list. | SELECT list_title FROM lists GROUP BY list_title ORDER BY COUNT(list_comments) DESC LIMIT 1 | [
"Please",
"provide",
"the",
"title",
"of",
"the",
"list",
"with",
"the",
"most",
"comments",
"on",
"the",
"list",
"."
] | [
{
"id": 2,
"type": "column",
"value": "list_comments"
},
{
"id": 1,
"type": "column",
"value": "list_title"
},
{
"id": 0,
"type": "table",
"value": "lists"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
4,648 | mondial_geo | bird:train.json:8218 | Which countries have more than 90% of African? List the name of the country in full. | SELECT T2.Name FROM ethnicGroup AS T1 INNER JOIN country AS T2 ON T1.Country = T2.Code WHERE T1.Name = 'African' AND T1.Percentage > 90 | [
"Which",
"countries",
"have",
"more",
"than",
"90",
"%",
"of",
"African",
"?",
"List",
"the",
"name",
"of",
"the",
"country",
"in",
"full",
"."
] | [
{
"id": 1,
"type": "table",
"value": "ethnicgroup"
},
{
"id": 6,
"type": "column",
"value": "percentage"
},
{
"id": 2,
"type": "table",
"value": "country"
},
{
"id": 3,
"type": "column",
"value": "country"
},
{
"id": 5,
"type": "value",
"va... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs"... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
4,650 | simpson_episodes | bird:train.json:4329 | How many episodes have the star score greater than 8? | SELECT COUNT(DISTINCT episode_id) FROM Vote WHERE stars > 8; | [
"How",
"many",
"episodes",
"have",
"the",
"star",
"score",
"greater",
"than",
"8",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "episode_id"
},
{
"id": 1,
"type": "column",
"value": "stars"
},
{
"id": 0,
"type": "table",
"value": "vote"
},
{
"id": 2,
"type": "value",
"value": "8"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
4,651 | chicago_crime | bird:train.json:8704 | In which district have there been more intimidation-type crimes? | SELECT T3.district_name FROM IUCR AS T1 INNER JOIN Crime AS T2 ON T2.iucr_no = T1.iucr_no INNER JOIN District AS T3 ON T3.district_no = T2.district_no WHERE T1.primary_description = 'INTIMIDATION' GROUP BY T3.district_name ORDER BY COUNT(T1.primary_description) DESC LIMIT 1 | [
"In",
"which",
"district",
"have",
"there",
"been",
"more",
"intimidation",
"-",
"type",
"crimes",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "primary_description"
},
{
"id": 0,
"type": "column",
"value": "district_name"
},
{
"id": 3,
"type": "value",
"value": "INTIMIDATION"
},
{
"id": 6,
"type": "column",
"value": "district_no"
},
{
"id": 1,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O"
] |
4,652 | apartment_rentals | spider:train_spider.json:1228 | Show the distinct apartment numbers of the apartments that have bookings with status code "Confirmed". | SELECT DISTINCT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id = T2.apt_id WHERE T1.booking_status_code = "Confirmed" | [
"Show",
"the",
"distinct",
"apartment",
"numbers",
"of",
"the",
"apartments",
"that",
"have",
"bookings",
"with",
"status",
"code",
"\"",
"Confirmed",
"\"",
"."
] | [
{
"id": 3,
"type": "column",
"value": "booking_status_code"
},
{
"id": 1,
"type": "table",
"value": "apartment_bookings"
},
{
"id": 0,
"type": "column",
"value": "apt_number"
},
{
"id": 2,
"type": "table",
"value": "apartments"
},
{
"id": 4,
"t... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
8,
9,
10
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
11,
12,
13
]
},
{
"entity_id": 4,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
4,653 | district_spokesman | bird:test.json:1191 | Which spokesman has lower points than the average? | SELECT name FROM spokesman WHERE points < (SELECT avg(points) FROM spokesman) | [
"Which",
"spokesman",
"has",
"lower",
"points",
"than",
"the",
"average",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "spokesman"
},
{
"id": 2,
"type": "column",
"value": "points"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
4,654 | works_cycles | bird:train.json:7444 | What is the name of the product stored in location 1 compartment L container 6? | SELECT T2.Name FROM ProductInventory AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID WHERE T1.LocationID = 1 AND T1.Shelf = 'L' AND T1.Bin = 6 | [
"What",
"is",
"the",
"name",
"of",
"the",
"product",
"stored",
"in",
"location",
"1",
"compartment",
"L",
"container",
"6",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "productinventory"
},
{
"id": 4,
"type": "column",
"value": "locationid"
},
{
"id": 3,
"type": "column",
"value": "productid"
},
{
"id": 2,
"type": "table",
"value": "product"
},
{
"id": 6,
"type": "column",... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
4,655 | tracking_share_transactions | spider:train_spider.json:5865 | What are the lot details of lots associated with transactions whose share count is bigger than 100 and whose type code is "PUR"? | SELECT T1.lot_details FROM LOTS AS T1 JOIN TRANSACTIONS_LOTS AS T2 ON T1.lot_id = T2.transaction_id JOIN TRANSACTIONS AS T3 ON T2.transaction_id = T3.transaction_id WHERE T3.share_count > 100 AND T3.transaction_type_code = "PUR" | [
"What",
"are",
"the",
"lot",
"details",
"of",
"lots",
"associated",
"with",
"transactions",
"whose",
"share",
"count",
"is",
"bigger",
"than",
"100",
"and",
"whose",
"type",
"code",
"is",
"\"",
"PUR",
"\"",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "transaction_type_code"
},
{
"id": 3,
"type": "table",
"value": "transactions_lots"
},
{
"id": 4,
"type": "column",
"value": "transaction_id"
},
{
"id": 1,
"type": "table",
"value": "transactions"
},
{
"id": 0,... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
4,656 | software_company | bird:train.json:8554 | Of customers who provide other services, how many are from places where inhabitants are more than 20000? | SELECT COUNT(T2.GEOID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.OCCUPATION = 'Other-service' AND T2.INHABITANTS_K > 20 | [
"Of",
"customers",
"who",
"provide",
"other",
"services",
",",
"how",
"many",
"are",
"from",
"places",
"where",
"inhabitants",
"are",
"more",
"than",
"20000",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Other-service"
},
{
"id": 5,
"type": "column",
"value": "inhabitants_k"
},
{
"id": 3,
"type": "column",
"value": "occupation"
},
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 1,
"type": "table... | [
{
"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": [
4,
5
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
4,657 | sakila_1 | spider:train_spider.json:2997 | Return the full name of the staff who provided a customer with the first name April and the last name Burns with a film rental. | SELECT DISTINCT T1.first_name , T1.last_name FROM staff AS T1 JOIN rental AS T2 ON T1.staff_id = T2.staff_id JOIN customer AS T3 ON T2.customer_id = T3.customer_id WHERE T3.first_name = 'APRIL' AND T3.last_name = 'BURNS' | [
"Return",
"the",
"full",
"name",
"of",
"the",
"staff",
"who",
"provided",
"a",
"customer",
"with",
"the",
"first",
"name",
"April",
"and",
"the",
"last",
"name",
"Burns",
"with",
"a",
"film",
"rental",
"."
] | [
{
"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",
"value": "customer"
},
{
"id": 8,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
13,
14
]
},
{
"entity_id": 1,
"token_idxs": [
18,
19
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,658 | formula_1 | spider:train_spider.json:2227 | Find the id, forename and number of races of all drivers who have at least participated in two races? | SELECT T1.driverid , T1.forename , count(*) FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid HAVING count(*) >= 2 | [
"Find",
"the",
"i",
"d",
",",
"forename",
"and",
"number",
"of",
"races",
"of",
"all",
"drivers",
"who",
"have",
"at",
"least",
"participated",
"in",
"two",
"races",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "driverid"
},
{
"id": 1,
"type": "column",
"value": "forename"
},
{
"id": 4,
"type": "table",
"value": "drivers"
},
{
"id": 5,
"type": "table",
"value": "results"
},
{
"id": 6,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
20
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,659 | beer_factory | bird:train.json:5340 | How many transactions have been made to purchase a root beer brand from California? | SELECT COUNT(T3.RootBeerID) FROM rootbeerbrand AS T1 INNER JOIN rootbeer AS T2 ON T1.BrandID = T2.BrandID INNER JOIN `transaction` AS T3 ON T2.RootBeerID = T3.RootBeerID WHERE T1.State = 'CA' | [
"How",
"many",
"transactions",
"have",
"been",
"made",
"to",
"purchase",
"a",
"root",
"beer",
"brand",
"from",
"California",
"?"
] | [
{
"id": 4,
"type": "table",
"value": "rootbeerbrand"
},
{
"id": 0,
"type": "table",
"value": "transaction"
},
{
"id": 3,
"type": "column",
"value": "rootbeerid"
},
{
"id": 5,
"type": "table",
"value": "rootbeer"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
4,660 | toxicology | bird:dev.json:300 | What atoms comprise TR186? | SELECT T.atom_id FROM atom AS T WHERE T.molecule_id = 'TR186' | [
"What",
"atoms",
"comprise",
"TR186",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "molecule_id"
},
{
"id": 1,
"type": "column",
"value": "atom_id"
},
{
"id": 3,
"type": "value",
"value": "TR186"
},
{
"id": 0,
"type": "table",
"value": "atom"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
4,661 | college_1 | spider:train_spider.json:3185 | How many credits does course CIS-220 have, and what its description? | SELECT crs_credit , crs_description FROM course WHERE crs_code = 'CIS-220' | [
"How",
"many",
"credits",
"does",
"course",
"CIS-220",
"have",
",",
"and",
"what",
"its",
"description",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "crs_description"
},
{
"id": 1,
"type": "column",
"value": "crs_credit"
},
{
"id": 3,
"type": "column",
"value": "crs_code"
},
{
"id": 4,
"type": "value",
"value": "CIS-220"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
4,662 | world | bird:train.json:7822 | List the languages used in the USA. | SELECT Language FROM CountryLanguage WHERE CountryCode = 'USA' | [
"List",
"the",
"languages",
"used",
"in",
"the",
"USA",
"."
] | [
{
"id": 0,
"type": "table",
"value": "countrylanguage"
},
{
"id": 2,
"type": "column",
"value": "countrycode"
},
{
"id": 1,
"type": "column",
"value": "language"
},
{
"id": 3,
"type": "value",
"value": "USA"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
4,663 | music_platform_2 | bird:train.json:7971 | List all the podcasts reviewed by a reviewer who has a review titled "Inspired & On Fire!". | SELECT T1.title FROM podcasts AS T1 INNER JOIN reviews AS T2 ON T2.podcast_id = T1.podcast_id WHERE T2.title = 'Inspired & On Fire!' | [
"List",
"all",
"the",
"podcasts",
"reviewed",
"by",
"a",
"reviewer",
"who",
"has",
"a",
"review",
"titled",
"\"",
"Inspired",
"&",
"On",
"Fire",
"!",
"\"",
"."
] | [
{
"id": 3,
"type": "value",
"value": "Inspired & On Fire!"
},
{
"id": 4,
"type": "column",
"value": "podcast_id"
},
{
"id": 1,
"type": "table",
"value": "podcasts"
},
{
"id": 2,
"type": "table",
"value": "reviews"
},
{
"id": 0,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
14,
15,
16,
17,
18
]
},
{
"entity_id": 4,... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
4,664 | public_review_platform | bird:train.json:3869 | How many users became an elite user the same year they joined Yelp? | SELECT COUNT(T1.user_id) FROM Users AS T1 INNER JOIN Elite AS T2 ON T1.user_id = T2.user_id WHERE T1.user_yelping_since_year = T2.year_id | [
"How",
"many",
"users",
"became",
"an",
"elite",
"user",
"the",
"same",
"year",
"they",
"joined",
"Yelp",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "user_yelping_since_year"
},
{
"id": 3,
"type": "column",
"value": "year_id"
},
{
"id": 4,
"type": "column",
"value": "user_id"
},
{
"id": 0,
"type": "table",
"value": "users"
},
{
"id": 1,
"type": "table",... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
4,665 | movie_3 | bird:train.json:9129 | Please list the titles of all the films that the customer RUTH MARTINEZ has rented. | SELECT T4.title FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id INNER JOIN inventory AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN film AS T4 ON T3.film_id = T4.film_id WHERE T1.first_name = 'RUTH' AND T1.last_name = 'MARTINEZ' | [
"Please",
"list",
"the",
"titles",
"of",
"all",
"the",
"films",
"that",
"the",
"customer",
"RUTH",
"MARTINEZ",
"has",
"rented",
"."
] | [
{
"id": 10,
"type": "column",
"value": "inventory_id"
},
{
"id": 11,
"type": "column",
"value": "customer_id"
},
{
"id": 4,
"type": "column",
"value": "first_name"
},
{
"id": 2,
"type": "table",
"value": "inventory"
},
{
"id": 6,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-VALUE",
"B-VALUE",
"O",
"B-TABLE",
"O"
] |
4,666 | train_station | spider:train_spider.json:6615 | Show the station name with greatest number of trains. | SELECT T2.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id = T2.station_id GROUP BY T1.station_id ORDER BY count(*) DESC LIMIT 1 | [
"Show",
"the",
"station",
"name",
"with",
"greatest",
"number",
"of",
"trains",
"."
] | [
{
"id": 2,
"type": "table",
"value": "train_station"
},
{
"id": 0,
"type": "column",
"value": "station_id"
},
{
"id": 3,
"type": "table",
"value": "station"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,667 | regional_sales | bird:train.json:2593 | Mention the most populated city and median income of the store in Florida state. | SELECT `City Name`, `Median Income` FROM `Store Locations` WHERE State = 'Florida' ORDER BY Population DESC LIMIT 1 | [
"Mention",
"the",
"most",
"populated",
"city",
"and",
"median",
"income",
"of",
"the",
"store",
"in",
"Florida",
"state",
"."
] | [
{
"id": 0,
"type": "table",
"value": "Store Locations"
},
{
"id": 2,
"type": "column",
"value": "Median Income"
},
{
"id": 5,
"type": "column",
"value": "population"
},
{
"id": 1,
"type": "column",
"value": "City Name"
},
{
"id": 4,
"type": "va... | [
{
"entity_id": 0,
"token_idxs": [
10,
11
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
6,
7
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_i... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-VALUE",
"B-COLUMN",
"O"
] |
4,668 | scientist_1 | spider:train_spider.json:6506 | What are the names of projects that have not been assigned? | SELECT Name FROM Projects WHERE Code NOT IN (SELECT Project FROM AssignedTo) | [
"What",
"are",
"the",
"names",
"of",
"projects",
"that",
"have",
"not",
"been",
"assigned",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "assignedto"
},
{
"id": 0,
"type": "table",
"value": "projects"
},
{
"id": 4,
"type": "column",
"value": "project"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,670 | student_loan | bird:train.json:4526 | List out the number of students who have the longest duration of absense from school and enlisted in the peace corps. | SELECT COUNT(T1.NAME) FROM longest_absense_from_school AS T1 INNER JOIN enlist AS T2 ON T1.name = T2.name WHERE T2.organ = 'peace_corps' ORDER BY T1.month DESC LIMIT 1 | [
"List",
"out",
"the",
"number",
"of",
"students",
"who",
"have",
"the",
"longest",
"duration",
"of",
"absense",
"from",
"school",
"and",
"enlisted",
"in",
"the",
"peace",
"corps",
"."
] | [
{
"id": 0,
"type": "table",
"value": "longest_absense_from_school"
},
{
"id": 3,
"type": "value",
"value": "peace_corps"
},
{
"id": 1,
"type": "table",
"value": "enlist"
},
{
"id": 2,
"type": "column",
"value": "organ"
},
{
"id": 4,
"type": "co... | [
{
"entity_id": 0,
"token_idxs": [
12,
13,
14
]
},
{
"entity_id": 1,
"token_idxs": [
16
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
19,
20
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
4,671 | mondial_geo | bird:train.json:8484 | State the inflation rate of Greece. | SELECT T2.Inflation FROM country AS T1 INNER JOIN economy AS T2 ON T1.Code = T2.Country WHERE T1.Name = 'Greece' | [
"State",
"the",
"inflation",
"rate",
"of",
"Greece",
"."
] | [
{
"id": 0,
"type": "column",
"value": "inflation"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 2,
"type": "table",
"value": "economy"
},
{
"id": 6,
"type": "column",
"value": "country"
},
{
"id": 4,
"type": "value",
"value":... | [
{
"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": [
5
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
4,672 | wine_1 | spider:train_spider.json:6551 | What are the wines that have prices lower than 50 and have appelations in Monterey county? | SELECT T2.Name FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation = T2.Appelation WHERE T1.County = "Monterey" AND T2.price < 50 | [
"What",
"are",
"the",
"wines",
"that",
"have",
"prices",
"lower",
"than",
"50",
"and",
"have",
"appelations",
"in",
"Monterey",
"county",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "appellations"
},
{
"id": 3,
"type": "column",
"value": "appelation"
},
{
"id": 5,
"type": "column",
"value": "Monterey"
},
{
"id": 4,
"type": "column",
"value": "county"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
4,674 | sales_in_weather | bird:train.json:8166 | Tell the wet-bulb temperature of the weather station which contained store no.6 on 2012/2/15. | SELECT T1.wetbulb FROM weather AS T1 INNER JOIN relation AS T2 ON T1.station_nbr = T2.station_nbr WHERE T2.store_nbr = 14 AND T1.`date` = '2012-02-15' | [
"Tell",
"the",
"wet",
"-",
"bulb",
"temperature",
"of",
"the",
"weather",
"station",
"which",
"contained",
"store",
"no.6",
"on",
"2012/2/15",
"."
] | [
{
"id": 3,
"type": "column",
"value": "station_nbr"
},
{
"id": 7,
"type": "value",
"value": "2012-02-15"
},
{
"id": 4,
"type": "column",
"value": "store_nbr"
},
{
"id": 2,
"type": "table",
"value": "relation"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12
]
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
4,675 | hospital_1 | spider:train_spider.json:3898 | Which department has the largest number of employees? | SELECT name FROM department GROUP BY departmentID ORDER BY count(departmentID) DESC LIMIT 1; | [
"Which",
"department",
"has",
"the",
"largest",
"number",
"of",
"employees",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "departmentid"
},
{
"id": 0,
"type": "table",
"value": "department"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
4,676 | retails | bird:train.json:6676 | Among all the orders made by a customer in the household segment, what is the highest total price? | SELECT MAX(T1.o_totalprice) FROM orders AS T1 INNER JOIN customer AS T2 ON T1.o_custkey = T2.c_custkey WHERE T2.c_mktsegment = 'HOUSEHOLD' | [
"Among",
"all",
"the",
"orders",
"made",
"by",
"a",
"customer",
"in",
"the",
"household",
"segment",
",",
"what",
"is",
"the",
"highest",
"total",
"price",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 4,
"type": "column",
"value": "o_totalprice"
},
{
"id": 3,
"type": "value",
"value": "HOUSEHOLD"
},
{
"id": 5,
"type": "column",
"value": "o_custkey"
},
{
"id": 6,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
17,
18
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
4,677 | chinook_1 | spider:train_spider.json:816 | Find the different billing countries for all invoices. | SELECT distinct(BillingCountry) FROM INVOICE | [
"Find",
"the",
"different",
"billing",
"countries",
"for",
"all",
"invoices",
"."
] | [
{
"id": 1,
"type": "column",
"value": "billingcountry"
},
{
"id": 0,
"type": "table",
"value": "invoice"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
4,678 | baseball_1 | spider:train_spider.json:3674 | How many times in total did the team Boston Red Stockings participate in postseason games? | SELECT count(*) FROM ( SELECT * FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner = T2.team_id_br WHERE T2.name = 'Boston Red Stockings' UNION SELECT * FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_loser = T2.team_id_br WHERE T2.name = 'Boston Red Stockings' ); | [
"How",
"many",
"times",
"in",
"total",
"did",
"the",
"team",
"Boston",
"Red",
"Stockings",
"participate",
"in",
"postseason",
"games",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Boston Red Stockings"
},
{
"id": 4,
"type": "column",
"value": "team_id_winner"
},
{
"id": 6,
"type": "column",
"value": "team_id_loser"
},
{
"id": 0,
"type": "table",
"value": "postseason"
},
{
"id": 5,
"t... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
4,679 | hockey | bird:train.json:7636 | Name the goalies and season they played when Boston Bruins won number 1 in rank. | SELECT T1.firstName, T1.lastName, T3.year FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID INNER JOIN Teams AS T3 ON T2.year = T3.year AND T2.tmID = T3.tmID WHERE T1.deathYear IS NOT NULL AND T3.name = 'Boston Bruins' AND T3.rank = 1 AND T1.pos = 'G' | [
"Name",
"the",
"goalies",
"and",
"season",
"they",
"played",
"when",
"Boston",
"Bruins",
"won",
"number",
"1",
"in",
"rank",
"."
] | [
{
"id": 8,
"type": "value",
"value": "Boston Bruins"
},
{
"id": 0,
"type": "column",
"value": "firstname"
},
{
"id": 6,
"type": "column",
"value": "deathyear"
},
{
"id": 1,
"type": "column",
"value": "lastname"
},
{
"id": 13,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
2
]
},
{
... | [
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
4,680 | customers_and_addresses | spider:train_spider.json:6118 | Return the number of customers who have at least one order with "Cancelled" status. | SELECT count(DISTINCT customer_id) FROM customer_orders WHERE order_status = "Cancelled" | [
"Return",
"the",
"number",
"of",
"customers",
"who",
"have",
"at",
"least",
"one",
"order",
"with",
"\"",
"Cancelled",
"\"",
"status",
"."
] | [
{
"id": 0,
"type": "table",
"value": "customer_orders"
},
{
"id": 1,
"type": "column",
"value": "order_status"
},
{
"id": 3,
"type": "column",
"value": "customer_id"
},
{
"id": 2,
"type": "column",
"value": "Cancelled"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O"
] |
4,682 | club_leader | bird:test.json:658 | Show the nations that have both members older than 22 and members younger than 19. | SELECT Nationality FROM member WHERE Age > 22 INTERSECT SELECT Nationality FROM member WHERE Age < 19 | [
"Show",
"the",
"nations",
"that",
"have",
"both",
"members",
"older",
"than",
"22",
"and",
"members",
"younger",
"than",
"19",
"."
] | [
{
"id": 1,
"type": "column",
"value": "nationality"
},
{
"id": 0,
"type": "table",
"value": "member"
},
{
"id": 2,
"type": "column",
"value": "age"
},
{
"id": 3,
"type": "value",
"value": "22"
},
{
"id": 4,
"type": "value",
"value": "19"
... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
4,683 | ice_hockey_draft | bird:train.json:6993 | Who had the most assists of team Plymouth Whalers in the 1999-2000 season? | SELECT T1.PlayerName FROM PlayerInfo AS T1 INNER JOIN SeasonStatus AS T2 ON T1.ELITEID = T2.ELITEID WHERE T2.TEAM = 'Plymouth Whalers' AND T2.SEASON = '1999-2000' ORDER BY T2.A DESC LIMIT 1 | [
"Who",
"had",
"the",
"most",
"assists",
"of",
"team",
"Plymouth",
"Whalers",
"in",
"the",
"1999",
"-",
"2000",
"season",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "Plymouth Whalers"
},
{
"id": 2,
"type": "table",
"value": "seasonstatus"
},
{
"id": 0,
"type": "column",
"value": "playername"
},
{
"id": 1,
"type": "table",
"value": "playerinfo"
},
{
"id": 8,
"type": "val... | [
{
"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": [
6
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
4,684 | sales | bird:train.json:5396 | What is the total sales amount for Reflector? | SELECT SUM(T1.Price * T2.quantity) FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Name = 'Reflector' | [
"What",
"is",
"the",
"total",
"sales",
"amount",
"for",
"Reflector",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Reflector"
},
{
"id": 4,
"type": "column",
"value": "productid"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 6,
"type": "column",
"value": "quantity"
},
{
"id": 1,
"type": "table",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O"
] |
4,685 | codebase_community | bird:dev.json:712 | What is the post ID and the comments commented in the post titled by "Group differences on a five point Likert item"? | SELECT T2.Id, T1.Text FROM comments AS T1 INNER JOIN posts AS T2 ON T1.PostId = T2.Id WHERE T2.Title = 'Group differences on a five point Likert item' | [
"What",
"is",
"the",
"post",
"ID",
"and",
"the",
"comments",
"commented",
"in",
"the",
"post",
"titled",
"by",
"\"",
"Group",
"differences",
"on",
"a",
"five",
"point",
"Likert",
"item",
"\"",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Group differences on a five point Likert item"
},
{
"id": 2,
"type": "table",
"value": "comments"
},
{
"id": 6,
"type": "column",
"value": "postid"
},
{
"id": 3,
"type": "table",
"value": "posts"
},
{
"id": 4,
... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
4,686 | advertising_agencies | bird:test.json:2112 | What are the distinct invoice ids and statuses for all payments? | SELECT DISTINCT T1.invoice_id , T1.invoice_status FROM Invoices AS T1 JOIN Payments AS T2 ON T1.invoice_id = T2.invoice_id | [
"What",
"are",
"the",
"distinct",
"invoice",
"ids",
"and",
"statuses",
"for",
"all",
"payments",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "invoice_status"
},
{
"id": 0,
"type": "column",
"value": "invoice_id"
},
{
"id": 2,
"type": "table",
"value": "invoices"
},
{
"id": 3,
"type": "table",
"value": "payments"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
4,687 | shipping | bird:train.json:5679 | List the weight of the customer's shipment with annual revenue of 39448581. | SELECT T1.weight FROM shipment AS T1 INNER JOIN customer AS T2 ON T1.cust_id = T2.cust_id WHERE T2.annual_revenue = 39448581 | [
"List",
"the",
"weight",
"of",
"the",
"customer",
"'s",
"shipment",
"with",
"annual",
"revenue",
"of",
"39448581",
"."
] | [
{
"id": 3,
"type": "column",
"value": "annual_revenue"
},
{
"id": 1,
"type": "table",
"value": "shipment"
},
{
"id": 2,
"type": "table",
"value": "customer"
},
{
"id": 4,
"type": "value",
"value": "39448581"
},
{
"id": 5,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": [
12
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O"
] |
4,688 | movie_platform | bird:train.json:68 | How many critics of the movie "Imitation of Life" got more than 1 like? | SELECT COUNT(*) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_title = 'Imitation of Life' AND T1.critic_likes > 1 | [
"How",
"many",
"critics",
"of",
"the",
"movie",
"\"",
"Imitation",
"of",
"Life",
"\"",
"got",
"more",
"than",
"1",
"like",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Imitation of Life"
},
{
"id": 5,
"type": "column",
"value": "critic_likes"
},
{
"id": 3,
"type": "column",
"value": "movie_title"
},
{
"id": 2,
"type": "column",
"value": "movie_id"
},
{
"id": 0,
"type": "t... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7,
8,
9
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
4,689 | e_learning | spider:train_spider.json:3816 | Find the subject ID, name of subject and the corresponding number of courses for each subject, and sort by the course count in ascending order. | SELECT T1.subject_id , T2.subject_name , COUNT(*) FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id = T2.subject_id GROUP BY T1.subject_id ORDER BY COUNT(*) ASC | [
"Find",
"the",
"subject",
"ID",
",",
"name",
"of",
"subject",
"and",
"the",
"corresponding",
"number",
"of",
"courses",
"for",
"each",
"subject",
",",
"and",
"sort",
"by",
"the",
"course",
"count",
"in",
"ascending",
"order",
"."
] | [
{
"id": 1,
"type": "column",
"value": "subject_name"
},
{
"id": 0,
"type": "column",
"value": "subject_id"
},
{
"id": 3,
"type": "table",
"value": "subjects"
},
{
"id": 2,
"type": "table",
"value": "courses"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,690 | driving_school | spider:train_spider.json:6675 | What is the status code with the least number of customers? | SELECT customer_status_code FROM Customers GROUP BY customer_status_code ORDER BY count(*) ASC LIMIT 1; | [
"What",
"is",
"the",
"status",
"code",
"with",
"the",
"least",
"number",
"of",
"customers",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "customer_status_code"
},
{
"id": 0,
"type": "table",
"value": "customers"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,691 | legislator | bird:train.json:4746 | What is the username of the current official Facebook presence of the oldest current legislator? | SELECT T2.facebook FROM current AS T1 INNER JOIN `social-media` AS T2 ON T2.bioguide = T1.bioguide_id ORDER BY T1.birthday_bio LIMIT 1 | [
"What",
"is",
"the",
"username",
"of",
"the",
"current",
"official",
"Facebook",
"presence",
"of",
"the",
"oldest",
"current",
"legislator",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "social-media"
},
{
"id": 3,
"type": "column",
"value": "birthday_bio"
},
{
"id": 5,
"type": "column",
"value": "bioguide_id"
},
{
"id": 0,
"type": "column",
"value": "facebook"
},
{
"id": 4,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
4,692 | bike_share_1 | bird:train.json:9084 | Does the bike with Id number 16 making any intercity trip? If yes, calculate the total travel duration during all the intercity trip. Convert the duration to hour. | SELECT T1.end_station_name, T2.city, CAST(SUM(T1.duration) AS REAL) / 3600 FROM trip AS T1 INNER JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T1.bike_id = 16 AND T1.start_station_name != T1.end_station_name | [
"Does",
"the",
"bike",
"with",
"I",
"d",
"number",
"16",
"making",
"any",
"intercity",
"trip",
"?",
"If",
"yes",
",",
"calculate",
"the",
"total",
"travel",
"duration",
"during",
"all",
"the",
"intercity",
"trip",
".",
"Convert",
"the",
"duration",
"to",
... | [
{
"id": 6,
"type": "column",
"value": "start_station_name"
},
{
"id": 0,
"type": "column",
"value": "end_station_name"
},
{
"id": 9,
"type": "column",
"value": "duration"
},
{
"id": 3,
"type": "table",
"value": "station"
},
{
"id": 7,
"type": "... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
24
]
},
{
"entity_id": 2,
"token_idxs": [
25
]
},
{
"entity_id": 3,
"token_idxs": [
29
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
4,693 | film_rank | spider:train_spider.json:4137 | What are the countries of markets and their corresponding years of market estimation? | SELECT T2.Country , T1.Year FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID = T2.Market_ID | [
"What",
"are",
"the",
"countries",
"of",
"markets",
"and",
"their",
"corresponding",
"years",
"of",
"market",
"estimation",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "film_market_estimation"
},
{
"id": 4,
"type": "column",
"value": "market_id"
},
{
"id": 0,
"type": "column",
"value": "country"
},
{
"id": 3,
"type": "table",
"value": "market"
},
{
"id": 1,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-TABLE",
"O"
] |
4,694 | candidate_poll | spider:train_spider.json:2396 | Which poll resource provided the most number of candidate information? | SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1 | [
"Which",
"poll",
"resource",
"provided",
"the",
"most",
"number",
"of",
"candidate",
"information",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "poll_source"
},
{
"id": 0,
"type": "table",
"value": "candidate"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
1,
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
4,695 | language_corpus | bird:train.json:5691 | Which word has the most occurrences within the same page of wikipedia about Catalan language? | SELECT T1.word FROM words AS T1 INNER JOIN pages_words AS T2 ON T1.wid = T2.wid WHERE T2.occurrences = ( SELECT MAX(occurrences) FROM pages_words ) | [
"Which",
"word",
"has",
"the",
"most",
"occurrences",
"within",
"the",
"same",
"page",
"of",
"wikipedia",
"about",
"Catalan",
"language",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "pages_words"
},
{
"id": 3,
"type": "column",
"value": "occurrences"
},
{
"id": 1,
"type": "table",
"value": "words"
},
{
"id": 0,
"type": "column",
"value": "word"
},
{
"id": 4,
"type": "column",
"value... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"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",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,696 | human_resources | bird:train.json:8972 | How many Account Representatives are there in Illinois with satisfying performance? | SELECT COUNT(*) FROM employee AS T1 INNER JOIN location AS T2 ON T1.locationID = T2.locationID INNER JOIN position AS T3 ON T3.positionID = T1.positionID WHERE T3.positiontitle = 'Account Representative' AND T1.performance = 'Good' AND T2.state = 'IL' | [
"How",
"many",
"Account",
"Representatives",
"are",
"there",
"in",
"Illinois",
"with",
"satisfying",
"performance",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Account Representative"
},
{
"id": 4,
"type": "column",
"value": "positiontitle"
},
{
"id": 6,
"type": "column",
"value": "performance"
},
{
"id": 3,
"type": "column",
"value": "positionid"
},
{
"id": 10,
"... | [
{
"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": [
2,
3
]
... | [
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
4,697 | movie_1 | spider:train_spider.json:2474 | Find the names of all reviewers who have contributed three or more ratings. | SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID = T2.rID GROUP BY T1.rID HAVING COUNT(*) >= 3 | [
"Find",
"the",
"names",
"of",
"all",
"reviewers",
"who",
"have",
"contributed",
"three",
"or",
"more",
"ratings",
"."
] | [
{
"id": 3,
"type": "table",
"value": "reviewer"
},
{
"id": 2,
"type": "table",
"value": "rating"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 0,
"type": "column",
"value": "rid"
},
{
"id": 4,
"type": "value",
"value": "3"
}
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,698 | superhero | bird:dev.json:773 | Which superhero has the same eyes, hair and skin colour? Indicate the publisher of the superhero. | SELECT T1.superhero_name, T2.publisher_name FROM superhero AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T1.eye_colour_id = T1.hair_colour_id AND T1.eye_colour_id = T1.skin_colour_id | [
"Which",
"superhero",
"has",
"the",
"same",
"eyes",
",",
"hair",
"and",
"skin",
"colour",
"?",
"Indicate",
"the",
"publisher",
"of",
"the",
"superhero",
"."
] | [
{
"id": 0,
"type": "column",
"value": "superhero_name"
},
{
"id": 1,
"type": "column",
"value": "publisher_name"
},
{
"id": 7,
"type": "column",
"value": "hair_colour_id"
},
{
"id": 8,
"type": "column",
"value": "skin_colour_id"
},
{
"id": 6,
"... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O"
] |
4,699 | store_1 | spider:train_spider.json:557 | List the customers first and last name of 10 least expensive invoices. | SELECT T1.first_name , T1.last_name FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id = T1.id ORDER BY total LIMIT 10; | [
"List",
"the",
"customers",
"first",
"and",
"last",
"name",
"of",
"10",
"least",
"expensive",
"invoices",
"."
] | [
{
"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",
"value": "customers"
},
{
"id": 3,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,701 | shakespeare | bird:train.json:3017 | How many chapters are there in "Midsummer Night's Dream"? | SELECT COUNT(T2.id) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Midsummer Night''s Dream' | [
"How",
"many",
"chapters",
"are",
"there",
"in",
"\"",
"Midsummer",
"Night",
"'s",
"Dream",
"\"",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Midsummer Night's Dream"
},
{
"id": 1,
"type": "table",
"value": "chapters"
},
{
"id": 5,
"type": "column",
"value": "work_id"
},
{
"id": 0,
"type": "table",
"value": "works"
},
{
"id": 2,
"type": "column",... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7,
8,
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
4,702 | european_football_2 | bird:dev.json:1143 | What was the highest score of the home team in the Netherlands Eredivisie league? | SELECT MAX(t2.home_team_goal) FROM League AS t1 INNER JOIN Match AS t2 ON t1.id = t2.league_id WHERE t1.name = 'Netherlands Eredivisie' | [
"What",
"was",
"the",
"highest",
"score",
"of",
"the",
"home",
"team",
"in",
"the",
"Netherlands",
"Eredivisie",
"league",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Netherlands Eredivisie"
},
{
"id": 4,
"type": "column",
"value": "home_team_goal"
},
{
"id": 6,
"type": "column",
"value": "league_id"
},
{
"id": 0,
"type": "table",
"value": "league"
},
{
"id": 1,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11,
12
]
},
{
"entity_id": 4,
"token_idxs": [
7,
8
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"O"
] |
4,703 | movie_1 | spider:train_spider.json:2508 | What is the name of the movie that is rated by most of times? | SELECT T2.title , T1.mID FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID = T2.mID GROUP BY T1.mID ORDER BY count(*) DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"movie",
"that",
"is",
"rated",
"by",
"most",
"of",
"times",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "rating"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 3,
"type": "table",
"value": "movie"
},
{
"id": 0,
"type": "column",
"value": "mid"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
4,704 | election | spider:train_spider.json:2793 | Which people severed as governor most frequently? | SELECT Governor FROM party GROUP BY Governor ORDER BY COUNT(*) DESC LIMIT 1 | [
"Which",
"people",
"severed",
"as",
"governor",
"most",
"frequently",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "governor"
},
{
"id": 0,
"type": "table",
"value": "party"
}
] | [
{
"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": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
4,705 | movie_3 | bird:train.json:9405 | Among the active customers, how many of them have Nina as their first name? | SELECT COUNT(customer_id) FROM customer WHERE first_name = 'Nina' AND active = 1 | [
"Among",
"the",
"active",
"customers",
",",
"how",
"many",
"of",
"them",
"have",
"Nina",
"as",
"their",
"first",
"name",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "customer_id"
},
{
"id": 2,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 4,
"type": "column",
"value": "active"
},
{
"id": 3,
"type": "value",
"v... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
13,
14
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
... | [
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
4,706 | public_review_platform | bird:train.json:4075 | Write down the any five of ID and name of category that starts with alphabet "P". | SELECT category_id, category_name FROM Categories WHERE category_name LIKE 'P%' LIMIT 5 | [
"Write",
"down",
"the",
"any",
"five",
"of",
"ID",
"and",
"name",
"of",
"category",
"that",
"starts",
"with",
"alphabet",
"\"",
"P",
"\"",
"."
] | [
{
"id": 2,
"type": "column",
"value": "category_name"
},
{
"id": 1,
"type": "column",
"value": "category_id"
},
{
"id": 0,
"type": "table",
"value": "categories"
},
{
"id": 3,
"type": "value",
"value": "P%"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs"... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
4,707 | bike_racing | bird:test.json:1477 | What are the ids and names of racing bikes that are purchased by at least 4 cyclists? | SELECT T1.id , T1.product_name FROM bike AS T1 JOIN cyclists_own_bikes AS T2 ON T1.id = T2.bike_id GROUP BY T1.id HAVING count(*) >= 4 | [
"What",
"are",
"the",
"ids",
"and",
"names",
"of",
"racing",
"bikes",
"that",
"are",
"purchased",
"by",
"at",
"least",
"4",
"cyclists",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "cyclists_own_bikes"
},
{
"id": 1,
"type": "column",
"value": "product_name"
},
{
"id": 5,
"type": "column",
"value": "bike_id"
},
{
"id": 2,
"type": "table",
"value": "bike"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
4,708 | college_1 | spider:train_spider.json:3301 | Find the first name and gpa of the students whose gpa is lower than the average gpa of all students. | SELECT stu_fname , stu_gpa FROM student WHERE stu_gpa < (SELECT avg(stu_gpa) FROM student) | [
"Find",
"the",
"first",
"name",
"and",
"gpa",
"of",
"the",
"students",
"whose",
"gpa",
"is",
"lower",
"than",
"the",
"average",
"gpa",
"of",
"all",
"students",
"."
] | [
{
"id": 1,
"type": "column",
"value": "stu_fname"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 2,
"type": "column",
"value": "stu_gpa"
}
] | [
{
"entity_id": 0,
"token_idxs": [
19
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,709 | university_basketball | spider:train_spider.json:984 | What is the founded year of the non public school that was founded most recently? | SELECT founded FROM university WHERE affiliation != 'Public' ORDER BY founded DESC LIMIT 1 | [
"What",
"is",
"the",
"founded",
"year",
"of",
"the",
"non",
"public",
"school",
"that",
"was",
"founded",
"most",
"recently",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "affiliation"
},
{
"id": 0,
"type": "table",
"value": "university"
},
{
"id": 1,
"type": "column",
"value": "founded"
},
{
"id": 3,
"type": "value",
"value": "Public"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,710 | baseball_1 | spider:train_spider.json:3672 | For each year, return the year and the number of times the team Boston Red Stockings won in the postseasons. | SELECT count(*) , T1.year FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner = T2.team_id_br WHERE T2.name = 'Boston Red Stockings' GROUP BY T1.year | [
"For",
"each",
"year",
",",
"return",
"the",
"year",
"and",
"the",
"number",
"of",
"times",
"the",
"team",
"Boston",
"Red",
"Stockings",
"won",
"in",
"the",
"postseasons",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Boston Red Stockings"
},
{
"id": 5,
"type": "column",
"value": "team_id_winner"
},
{
"id": 1,
"type": "table",
"value": "postseason"
},
{
"id": 6,
"type": "column",
"value": "team_id_br"
},
{
"id": 0,
"type... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
20
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
14,
15,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
4,711 | movie_3 | bird:train.json:9375 | How long did Austin Cintron take to return the movie 'Destiny Saturday'? | SELECT T2.rental_date - T2.return_date FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id INNER JOIN inventory AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN film AS T4 ON T3.film_id = T4.film_id WHERE T1.first_name = 'AUSTIN' AND T4.title = 'DESTINY SATURDAY' | [
"How",
"long",
"did",
"Austin",
"Cintron",
"take",
"to",
"return",
"the",
"movie",
"'",
"Destiny",
"Saturday",
"'",
"?"
] | [
{
"id": 8,
"type": "value",
"value": "DESTINY SATURDAY"
},
{
"id": 11,
"type": "column",
"value": "inventory_id"
},
{
"id": 1,
"type": "column",
"value": "rental_date"
},
{
"id": 2,
"type": "column",
"value": "return_date"
},
{
"id": 12,
"type"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
4,712 | video_games | bird:train.json:3495 | In which region where a game had the lowest number of sales? | SELECT DISTINCT T1.region_name FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id ORDER BY T2.num_sales LIMIT 1 | [
"In",
"which",
"region",
"where",
"a",
"game",
"had",
"the",
"lowest",
"number",
"of",
"sales",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "region_sales"
},
{
"id": 0,
"type": "column",
"value": "region_name"
},
{
"id": 3,
"type": "column",
"value": "num_sales"
},
{
"id": 5,
"type": "column",
"value": "region_id"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
4,713 | club_1 | spider:train_spider.json:4277 | Find the club which has the largest number of members majoring in "600". | SELECT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid = t2.clubid JOIN student AS t3 ON t2.stuid = t3.stuid WHERE t3.major = "600" GROUP BY t1.clubname ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"club",
"which",
"has",
"the",
"largest",
"number",
"of",
"members",
"majoring",
"in",
"\"",
"600",
"\"",
"."
] | [
{
"id": 5,
"type": "table",
"value": "member_of_club"
},
{
"id": 0,
"type": "column",
"value": "clubname"
},
{
"id": 1,
"type": "table",
"value": "student"
},
{
"id": 7,
"type": "column",
"value": "clubid"
},
{
"id": 2,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
4,714 | theme_gallery | spider:train_spider.json:1666 | What are the theme and year for all exhibitions that have a ticket price under 15? | SELECT theme , YEAR FROM exhibition WHERE ticket_price < 15 | [
"What",
"are",
"the",
"theme",
"and",
"year",
"for",
"all",
"exhibitions",
"that",
"have",
"a",
"ticket",
"price",
"under",
"15",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "ticket_price"
},
{
"id": 0,
"type": "table",
"value": "exhibition"
},
{
"id": 1,
"type": "column",
"value": "theme"
},
{
"id": 2,
"type": "column",
"value": "year"
},
{
"id": 4,
"type": "value",
"value... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
15
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O"
] |
4,715 | customers_and_invoices | spider:train_spider.json:1583 | What is the average, minimum, maximum, and total transaction amount? | SELECT avg(transaction_amount) , min(transaction_amount) , max(transaction_amount) , sum(transaction_amount) FROM Financial_transactions | [
"What",
"is",
"the",
"average",
",",
"minimum",
",",
"maximum",
",",
"and",
"total",
"transaction",
"amount",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "financial_transactions"
},
{
"id": 1,
"type": "column",
"value": "transaction_amount"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9,
10
]
},
{
"entity_id": 1,
"token_idxs": [
11,
12
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"I-COLUMN",
"O"
] |
4,716 | superhero | bird:dev.json:808 | Find the race of the superhero who weighs 108kg and is 188cm tall. | SELECT DISTINCT T2.race FROM superhero AS T1 INNER JOIN race AS T2 ON T1.race_id = T2.id WHERE T1.weight_kg = 108 AND T1.height_cm = 188 | [
"Find",
"the",
"race",
"of",
"the",
"superhero",
"who",
"weighs",
"108",
"kg",
"and",
"is",
"188",
"cm",
"tall",
"."
] | [
{
"id": 1,
"type": "table",
"value": "superhero"
},
{
"id": 5,
"type": "column",
"value": "weight_kg"
},
{
"id": 7,
"type": "column",
"value": "height_cm"
},
{
"id": 3,
"type": "column",
"value": "race_id"
},
{
"id": 0,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
0
]
},
{
"entity_id": 5,
"... | [
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
4,717 | synthea | bird:train.json:1485 | What is the average body mass index for patients with higher total cholesterol? | SELECT SUM(T1.VALUE) / COUNT(T1.PATIENT) FROM observations AS T1 INNER JOIN ( SELECT DISTINCT PATIENT FROM observations WHERE DESCRIPTION = 'Total Cholesterol' AND VALUE > 200 ) AS T2 ON T1.PATIENT = T2.PATIENT WHERE T1.DESCRIPTION = 'Body Mass Index' | [
"What",
"is",
"the",
"average",
"body",
"mass",
"index",
"for",
"patients",
"with",
"higher",
"total",
"cholesterol",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Total Cholesterol"
},
{
"id": 2,
"type": "value",
"value": "Body Mass Index"
},
{
"id": 0,
"type": "table",
"value": "observations"
},
{
"id": 1,
"type": "column",
"value": "description"
},
{
"id": 3,
"type... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"en... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
4,718 | flight_4 | spider:train_spider.json:6843 | Which countries has the most number of airlines whose active status is 'Y'? | SELECT country FROM airlines WHERE active = 'Y' GROUP BY country ORDER BY count(*) DESC LIMIT 1 | [
"Which",
"countries",
"has",
"the",
"most",
"number",
"of",
"airlines",
"whose",
"active",
"status",
"is",
"'",
"Y",
"'",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "airlines"
},
{
"id": 1,
"type": "column",
"value": "country"
},
{
"id": 2,
"type": "column",
"value": "active"
},
{
"id": 3,
"type": "value",
"value": "Y"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
4,719 | hockey | bird:train.json:7652 | How many teams have the same total number of postseason wins and postseason loses? | SELECT DISTINCT COUNT(tmID) FROM Goalies WHERE PostW = PostL | [
"How",
"many",
"teams",
"have",
"the",
"same",
"total",
"number",
"of",
"postseason",
"wins",
"and",
"postseason",
"loses",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "goalies"
},
{
"id": 1,
"type": "column",
"value": "postw"
},
{
"id": 2,
"type": "column",
"value": "postl"
},
{
"id": 3,
"type": "column",
"value": "tmid"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,720 | county_public_safety | spider:train_spider.json:2548 | List the names of the city with the top 5 white percentages. | SELECT Name FROM city ORDER BY White DESC LIMIT 5 | [
"List",
"the",
"names",
"of",
"the",
"city",
"with",
"the",
"top",
"5",
"white",
"percentages",
"."
] | [
{
"id": 2,
"type": "column",
"value": "white"
},
{
"id": 0,
"type": "table",
"value": "city"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
4,721 | loan_1 | spider:train_spider.json:3066 | Find the name of bank branch that provided the greatest total amount of loans. | SELECT T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id = T2.branch_id GROUP BY T1.bname ORDER BY sum(T2.amount) DESC LIMIT 1 | [
"Find",
"the",
"name",
"of",
"bank",
"branch",
"that",
"provided",
"the",
"greatest",
"total",
"amount",
"of",
"loans",
"."
] | [
{
"id": 3,
"type": "column",
"value": "branch_id"
},
{
"id": 4,
"type": "column",
"value": "amount"
},
{
"id": 0,
"type": "column",
"value": "bname"
},
{
"id": 1,
"type": "table",
"value": "bank"
},
{
"id": 2,
"type": "table",
"value": "loa... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O"
] |
4,722 | soccer_1 | spider:train_spider.json:1306 | List the names and birthdays of the top five players in terms of potential. | SELECT DISTINCT T1.player_name , T1.birthday FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id = T2.player_api_id ORDER BY potential DESC LIMIT 5 | [
"List",
"the",
"names",
"and",
"birthdays",
"of",
"the",
"top",
"five",
"players",
"in",
"terms",
"of",
"potential",
"."
] | [
{
"id": 3,
"type": "table",
"value": "player_attributes"
},
{
"id": 5,
"type": "column",
"value": "player_api_id"
},
{
"id": 0,
"type": "column",
"value": "player_name"
},
{
"id": 4,
"type": "column",
"value": "potential"
},
{
"id": 1,
"type": ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entit... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-TABLE",
"O",
"B-COLUMN",
"O"
] |
4,723 | codebase_comments | bird:train.json:675 | Provide the tokenized name of the method "Sky.Excel.ExcelBook.TypeConvert". | SELECT NameTokenized FROM Method WHERE Name = 'Sky.Excel.ExcelBook.TypeConvert' | [
"Provide",
"the",
"tokenized",
"name",
"of",
"the",
"method",
"\"",
"Sky",
".",
"Excel",
".",
"ExcelBook",
".",
"TypeConvert",
"\"",
"."
] | [
{
"id": 3,
"type": "value",
"value": "Sky.Excel.ExcelBook.TypeConvert"
},
{
"id": 1,
"type": "column",
"value": "nametokenized"
},
{
"id": 0,
"type": "table",
"value": "method"
},
{
"id": 2,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10,
11,
12,
13,
14
]
},
{
... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
4,724 | address | bird:train.json:5134 | Name 10 cities with their states that are under the Lexington-Fayette, KY office of the Canada Border Services Agency. | SELECT DISTINCT T2.city, T2.state FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Lexington-Fayette, KY' LIMIT 10 | [
"Name",
"10",
"cities",
"with",
"their",
"states",
"that",
"are",
"under",
"the",
"Lexington",
"-",
"Fayette",
",",
"KY",
"office",
"of",
"the",
"Canada",
"Border",
"Services",
"Agency",
"."
] | [
{
"id": 5,
"type": "value",
"value": "Lexington-Fayette, KY"
},
{
"id": 4,
"type": "column",
"value": "cbsa_name"
},
{
"id": 3,
"type": "table",
"value": "zip_data"
},
{
"id": 1,
"type": "column",
"value": "state"
},
{
"id": 0,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
0
]
},
{
"entity_id": 5,
"... | [
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,725 | movie_platform | bird:train.json:23 | How many users gave "Pavee Lackeen: The Traveller Girl" movie a rating score of 4? | SELECT COUNT(T2.user_id) FROM movies AS T1 INNER JOIN ratings AS T2 ON T1.movie_id = T2.movie_id WHERE T1.movie_title = 'Pavee Lackeen: The Traveller Girl' AND T2.rating_score = 4 | [
"How",
"many",
"users",
"gave",
"\"",
"Pavee",
"Lackeen",
":",
"The",
"Traveller",
"Girl",
"\"",
"movie",
"a",
"rating",
"score",
"of",
"4",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Pavee Lackeen: The Traveller Girl"
},
{
"id": 6,
"type": "column",
"value": "rating_score"
},
{
"id": 4,
"type": "column",
"value": "movie_title"
},
{
"id": 3,
"type": "column",
"value": "movie_id"
},
{
"id": 1... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-TABLE",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
4,726 | aan_1 | bird:test.json:1014 | Count the number of papers Mckeown , Kathleen has cited . | select count(*) from citation as t1 join author_list as t2 on t1.paper_id = t2.paper_id join author as t3 on t2.author_id = t3.author_id where t3.name = "mckeown , kathleen" | [
"Count",
"the",
"number",
"of",
"papers",
"Mckeown",
",",
"Kathleen",
"has",
"cited",
"."
] | [
{
"id": 2,
"type": "column",
"value": "mckeown , kathleen"
},
{
"id": 4,
"type": "table",
"value": "author_list"
},
{
"id": 5,
"type": "column",
"value": "author_id"
},
{
"id": 3,
"type": "table",
"value": "citation"
},
{
"id": 6,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O"
] |
4,727 | thrombosis_prediction | bird:dev.json:1179 | For the patient who was diagnosed with SLE on 1994/2/19, what was his/her anti-Cardiolipin antibody concentration status on 1993/11/12? | SELECT `aCL IgA`, `aCL IgG`, `aCL IgM` FROM Examination WHERE ID IN ( SELECT ID FROM Patient WHERE Diagnosis = 'SLE' AND Description = '1994-02-19' ) AND `Examination Date` = '1993-11-12' | [
"For",
"the",
"patient",
"who",
"was",
"diagnosed",
"with",
"SLE",
"on",
"1994/2/19",
",",
"what",
"was",
"his",
"/",
"her",
"anti",
"-",
"Cardiolipin",
"antibody",
"concentration",
"status",
"on",
"1993/11/12",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "Examination Date"
},
{
"id": 0,
"type": "table",
"value": "examination"
},
{
"id": 10,
"type": "column",
"value": "description"
},
{
"id": 6,
"type": "value",
"value": "1993-11-12"
},
{
"id": 11,
"type": "... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
4,728 | soccer_1 | spider:train_spider.json:1293 | List all country and league names. | SELECT T1.name , T2.name FROM Country AS T1 JOIN League AS T2 ON T1.id = T2.country_id | [
"List",
"all",
"country",
"and",
"league",
"names",
"."
] | [
{
"id": 4,
"type": "column",
"value": "country_id"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 2,
"type": "table",
"value": "league"
},
{
"id": 0,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
4,729 | shakespeare | bird:train.json:3046 | How many of the works of Shakespeare are Tragedy? | SELECT COUNT(id) FROM works WHERE GenreType = 'Tragedy' | [
"How",
"many",
"of",
"the",
"works",
"of",
"Shakespeare",
"are",
"Tragedy",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "genretype"
},
{
"id": 2,
"type": "value",
"value": "Tragedy"
},
{
"id": 0,
"type": "table",
"value": "works"
},
{
"id": 3,
"type": "column",
"value": "id"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
4,730 | soccer_2016 | bird:train.json:2036 | How many matches have 7 points of winning margin? | SELECT COUNT(Match_Id) FROM Match WHERE win_margin = 7 | [
"How",
"many",
"matches",
"have",
"7",
"points",
"of",
"winning",
"margin",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "win_margin"
},
{
"id": 3,
"type": "column",
"value": "match_id"
},
{
"id": 0,
"type": "table",
"value": "match"
},
{
"id": 2,
"type": "value",
"value": "7"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
4,731 | cars | bird:train.json:3065 | What is the acceleration of the most expensive car? | SELECT T1.acceleration FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID ORDER BY T2.price DESC LIMIT 1 | [
"What",
"is",
"the",
"acceleration",
"of",
"the",
"most",
"expensive",
"car",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "acceleration"
},
{
"id": 2,
"type": "table",
"value": "price"
},
{
"id": 3,
"type": "column",
"value": "price"
},
{
"id": 1,
"type": "table",
"value": "data"
},
{
"id": 4,
"type": "column",
"value": "i... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
4,732 | loan_1 | spider:train_spider.json:3026 | Find the number of different cities which banks are located at. | SELECT count(DISTINCT city) FROM bank | [
"Find",
"the",
"number",
"of",
"different",
"cities",
"which",
"banks",
"are",
"located",
"at",
"."
] | [
{
"id": 0,
"type": "table",
"value": "bank"
},
{
"id": 1,
"type": "column",
"value": "city"
}
] | [
{
"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"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.