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 |
|---|---|---|---|---|---|---|---|---|
14,124 | government_shift | bird:test.json:366 | return the details of the customer with largest count of used services. | select t1.customer_details from customers as t1 join customers_and_services as t2 on t1.customer_id = t2.customer_id group by t1.customer_details order by count(*) desc limit 1 | [
"return",
"the",
"details",
"of",
"the",
"customer",
"with",
"largest",
"count",
"of",
"used",
"services",
"."
] | [
{
"id": 2,
"type": "table",
"value": "customers_and_services"
},
{
"id": 0,
"type": "column",
"value": "customer_details"
},
{
"id": 3,
"type": "column",
"value": "customer_id"
},
{
"id": 1,
"type": "table",
"value": "customers"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
10,
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
14,125 | works_cycles | bird:train.json:7137 | What percentage of the AdventureWorks data is in Thai? | SELECT CAST(SUM(CASE WHEN T1.Name = 'Thai' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.CultureID) FROM Culture AS T1 INNER JOIN ProductModelProductDescriptionCulture AS T2 ON T1.CultureID = T2.CultureID | [
"What",
"percentage",
"of",
"the",
"AdventureWorks",
"data",
"is",
"in",
"Thai",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "productmodelproductdescriptionculture"
},
{
"id": 2,
"type": "column",
"value": "cultureid"
},
{
"id": 0,
"type": "table",
"value": "culture"
},
{
"id": 6,
"type": "column",
"value": "name"
},
{
"id": 7,
"t... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,126 | shakespeare | bird:train.json:2964 | How many characters are there in Twelfth Night? | SELECT COUNT(DISTINCT T4.id) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id INNER JOIN paragraphs AS T3 ON T2.id = T3.chapter_id INNER JOIN characters AS T4 ON T3.character_id = T4.id WHERE T1.Title = 'Twelfth Night' | [
"How",
"many",
"characters",
"are",
"there",
"in",
"Twelfth",
"Night",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Twelfth Night"
},
{
"id": 5,
"type": "column",
"value": "character_id"
},
{
"id": 0,
"type": "table",
"value": "characters"
},
{
"id": 4,
"type": "table",
"value": "paragraphs"
},
{
"id": 8,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6,
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
14,127 | department_store | spider:train_spider.json:4789 | Find the name and gender of the staff who has been assigned the job of Sales Person but never Clerical Staff. | SELECT T1.staff_name , T1.staff_gender FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id = T2.staff_id WHERE T2.job_title_code = "Sales Person" EXCEPT SELECT T1.staff_name , T1.staff_gender FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id = T2.staff_id WHERE T2.job_t... | [
"Find",
"the",
"name",
"and",
"gender",
"of",
"the",
"staff",
"who",
"has",
"been",
"assigned",
"the",
"job",
"of",
"Sales",
"Person",
"but",
"never",
"Clerical",
"Staff",
"."
] | [
{
"id": 3,
"type": "table",
"value": "staff_department_assignments"
},
{
"id": 4,
"type": "column",
"value": "job_title_code"
},
{
"id": 6,
"type": "column",
"value": "Clerical Staff"
},
{
"id": 1,
"type": "column",
"value": "staff_gender"
},
{
"id... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
14,129 | language_corpus | bird:train.json:5772 | How many pages of Wikipedia are there in total on the Catalan language? | SELECT pages FROM langs WHERE lang = 'ca' | [
"How",
"many",
"pages",
"of",
"Wikipedia",
"are",
"there",
"in",
"total",
"on",
"the",
"Catalan",
"language",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "langs"
},
{
"id": 1,
"type": "column",
"value": "pages"
},
{
"id": 2,
"type": "column",
"value": "lang"
},
{
"id": 3,
"type": "value",
"value": "ca"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,130 | scientist_1 | spider:train_spider.json:6470 | How many scientists are there? | SELECT count(*) FROM scientists | [
"How",
"many",
"scientists",
"are",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "scientists"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
14,131 | app_store | bird:train.json:2530 | What is the average rating of comic category apps? How many users hold positive attitude towards this app? | SELECT AVG(T1.Rating) , COUNT(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE NULL END) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.Category = 'COMICS' | [
"What",
"is",
"the",
"average",
"rating",
"of",
"comic",
"category",
"apps",
"?",
"How",
"many",
"users",
"hold",
"positive",
"attitude",
"towards",
"this",
"app",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "user_reviews"
},
{
"id": 0,
"type": "table",
"value": "playstore"
},
{
"id": 7,
"type": "column",
"value": "sentiment"
},
{
"id": 2,
"type": "column",
"value": "category"
},
{
"id": 8,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,132 | bike_1 | spider:train_spider.json:176 | What is the date, average temperature and mean humidity for the days with the 3 largest maximum gust speeds? | SELECT date , mean_temperature_f , mean_humidity FROM weather ORDER BY max_gust_speed_mph DESC LIMIT 3 | [
"What",
"is",
"the",
"date",
",",
"average",
"temperature",
"and",
"mean",
"humidity",
"for",
"the",
"days",
"with",
"the",
"3",
"largest",
"maximum",
"gust",
"speeds",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "mean_temperature_f"
},
{
"id": 4,
"type": "column",
"value": "max_gust_speed_mph"
},
{
"id": 3,
"type": "column",
"value": "mean_humidity"
},
{
"id": 0,
"type": "table",
"value": "weather"
},
{
"id": 1,
"t... | [
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": [
17,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
14,133 | advertising_agencies | bird:test.json:2146 | What are the start and end times of each meeting, as well as the corresponding client and staff details the attendees? | SELECT T1.start_date_time , T1.end_date_time , T2.client_details , T4.staff_details FROM meetings AS T1 JOIN clients AS T2 ON T1.client_id = T2.client_id JOIN staff_in_meetings AS T3 ON T1.meeting_id = T3.meeting_id JOIN staff AS T4 ON T3.staff_id = T4.staff_id | [
"What",
"are",
"the",
"start",
"and",
"end",
"times",
"of",
"each",
"meeting",
",",
"as",
"well",
"as",
"the",
"corresponding",
"client",
"and",
"staff",
"details",
"the",
"attendees",
"?"
] | [
{
"id": 5,
"type": "table",
"value": "staff_in_meetings"
},
{
"id": 0,
"type": "column",
"value": "start_date_time"
},
{
"id": 2,
"type": "column",
"value": "client_details"
},
{
"id": 1,
"type": "column",
"value": "end_date_time"
},
{
"id": 3,
... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": [
19
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
14,134 | program_share | spider:train_spider.json:3754 | find the program owners that have some programs in both morning and night time. | SELECT t1.owner FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id = t2.program_id WHERE t2.Time_of_day = "Morning" INTERSECT SELECT t1.owner FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id = t2.program_id WHERE t2.Time_of_day = "Night" | [
"find",
"the",
"program",
"owners",
"that",
"have",
"some",
"programs",
"in",
"both",
"morning",
"and",
"night",
"time",
"."
] | [
{
"id": 3,
"type": "column",
"value": "time_of_day"
},
{
"id": 6,
"type": "column",
"value": "program_id"
},
{
"id": 2,
"type": "table",
"value": "broadcast"
},
{
"id": 1,
"type": "table",
"value": "program"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
14,135 | mondial_geo | bird:train.json:8429 | What is the name of Anguilla's capital, and where is it located? | SELECT Capital, Province FROM country WHERE Name = 'Anguilla' | [
"What",
"is",
"the",
"name",
"of",
"Anguilla",
"'s",
"capital",
",",
"and",
"where",
"is",
"it",
"located",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "province"
},
{
"id": 4,
"type": "value",
"value": "Anguilla"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 1,
"type": "column",
"value": "capital"
},
{
"id": 3,
"type": "column",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,136 | olympics | bird:train.json:4917 | Please list the names of all the Olympic competitors from Finland. | SELECT T3.full_name FROM noc_region AS T1 INNER JOIN person_region AS T2 ON T1.id = T2.region_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.region_name = 'Finland' | [
"Please",
"list",
"the",
"names",
"of",
"all",
"the",
"Olympic",
"competitors",
"from",
"Finland",
"."
] | [
{
"id": 5,
"type": "table",
"value": "person_region"
},
{
"id": 2,
"type": "column",
"value": "region_name"
},
{
"id": 4,
"type": "table",
"value": "noc_region"
},
{
"id": 0,
"type": "column",
"value": "full_name"
},
{
"id": 6,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,137 | airline | bird:train.json:5901 | From August 10 to August 20, 2018, how many cancelled flights of air carrier named Spirit Air Lines: NK are there? | SELECT COUNT(*) FROM `Air Carriers` AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.OP_CARRIER_AIRLINE_ID WHERE T1.Description = 'Spirit Air Lines: NK' AND T2.CANCELLED = 0 AND T2.FL_DATE BETWEEN '2018/8/10' AND '2018/8/20' | [
"From",
"August",
"10",
"to",
"August",
"20",
",",
"2018",
",",
"how",
"many",
"cancelled",
"flights",
"of",
"air",
"carrier",
"named",
"Spirit",
"Air",
"Lines",
":",
"NK",
"are",
"there",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "op_carrier_airline_id"
},
{
"id": 5,
"type": "value",
"value": "Spirit Air Lines: NK"
},
{
"id": 0,
"type": "table",
"value": "Air Carriers"
},
{
"id": 4,
"type": "column",
"value": "description"
},
{
"id": 6,... | [
{
"entity_id": 0,
"token_idxs": [
14,
15
]
},
{
"entity_id": 1,
"token_idxs": [
18,
19
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"B-VALUE",
"B-TABLE",
"I-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O"
] |
14,138 | mondial_geo | bird:train.json:8286 | What kind of government does Iran have? | SELECT T2.Government FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country WHERE T1.Name = 'Iran' | [
"What",
"kind",
"of",
"government",
"does",
"Iran",
"have",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "government"
},
{
"id": 2,
"type": "table",
"value": "politics"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 6,
"type": "column",
"value": "country"
},
{
"id": 3,
"type": "column",
"valu... | [
{
"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": [
5
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
14,139 | cre_Doc_and_collections | bird:test.json:709 | Which unique subset does document owned by Braeden belong to? List the subset name. | SELECT DISTINCT T1.Document_Subset_Name FROM Document_Subsets AS T1 JOIN Document_Subset_Members AS T2 ON T1.Document_Subset_ID = T2.Document_Subset_ID JOIN Document_Objects AS T3 ON T2.Document_Object_ID = T3.Document_Object_ID WHERE T3.owner = 'Braeden' | [
"Which",
"unique",
"subset",
"does",
"document",
"owned",
"by",
"Braeden",
"belong",
"to",
"?",
"List",
"the",
"subset",
"name",
"."
] | [
{
"id": 5,
"type": "table",
"value": "document_subset_members"
},
{
"id": 0,
"type": "column",
"value": "document_subset_name"
},
{
"id": 6,
"type": "column",
"value": "document_object_id"
},
{
"id": 7,
"type": "column",
"value": "document_subset_id"
},
... | [
{
"entity_id": 0,
"token_idxs": [
13,
14
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,140 | shipping | bird:train.json:5656 | How many shipments did Zachery Hicks transport goods to New York in the year 2016? | SELECT COUNT(*) FROM city AS T1 INNER JOIN shipment AS T2 ON T1.city_id = T2.city_id INNER JOIN driver AS T3 ON T3.driver_id = T2.driver_id WHERE T3.first_name = 'Zachery' AND T3.last_name = 'Hicks' AND T1.city_name = 'New York' AND STRFTIME('%Y', T2.ship_date) = '2016' | [
"How",
"many",
"shipments",
"did",
"Zachery",
"Hicks",
"transport",
"goods",
"to",
"New",
"York",
"in",
"the",
"year",
"2016",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "first_name"
},
{
"id": 3,
"type": "column",
"value": "driver_id"
},
{
"id": 6,
"type": "column",
"value": "last_name"
},
{
"id": 8,
"type": "column",
"value": "city_name"
},
{
"id": 13,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
4
... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,141 | cre_Drama_Workshop_Groups | spider:train_spider.json:5165 | What are the names of the clients who do not have any booking? | SELECT Customer_Name FROM Clients EXCEPT SELECT T2.Customer_Name FROM Bookings AS T1 JOIN Clients AS T2 ON T1.Customer_ID = T2.Client_ID | [
"What",
"are",
"the",
"names",
"of",
"the",
"clients",
"who",
"do",
"not",
"have",
"any",
"booking",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "customer_name"
},
{
"id": 3,
"type": "column",
"value": "customer_id"
},
{
"id": 4,
"type": "column",
"value": "client_id"
},
{
"id": 2,
"type": "table",
"value": "bookings"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,142 | airline | bird:train.json:5907 | Provide the air carrier description of the flight with a tail number N922US from Phoenix. | SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T2.Code = T1.OP_CARRIER_AIRLINE_ID WHERE T1.TAIL_NUM = 'N922US' AND T1.ORIGIN = 'PHX' GROUP BY T2.Description | [
"Provide",
"the",
"air",
"carrier",
"description",
"of",
"the",
"flight",
"with",
"a",
"tail",
"number",
"N922US",
"from",
"Phoenix",
"."
] | [
{
"id": 4,
"type": "column",
"value": "op_carrier_airline_id"
},
{
"id": 2,
"type": "table",
"value": "Air Carriers"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 1,
"type": "table",
"value": "airlines"
},
{
"id": 5,
"type":... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2,
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
14,143 | works_cycles | bird:train.json:7373 | Which territory has the greatest difference in sales from previous year to this year? Indicate the difference, as well as the name and country of the region. | SELECT SalesLastYear - SalesYTD, Name, CountryRegionCode FROM SalesTerritory ORDER BY SalesLastYear - SalesYTD DESC LIMIT 1 | [
"Which",
"territory",
"has",
"the",
"greatest",
"difference",
"in",
"sales",
"from",
"previous",
"year",
"to",
"this",
"year",
"?",
"Indicate",
"the",
"difference",
",",
"as",
"well",
"as",
"the",
"name",
"and",
"country",
"of",
"the",
"region",
"."
] | [
{
"id": 2,
"type": "column",
"value": "countryregioncode"
},
{
"id": 0,
"type": "table",
"value": "salesterritory"
},
{
"id": 3,
"type": "column",
"value": "saleslastyear"
},
{
"id": 4,
"type": "column",
"value": "salesytd"
},
{
"id": 1,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
23
]
},
{
"entity_id": 2,
"token_idxs": [
25,
26,
27,
28
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
14,144 | retails | bird:train.json:6721 | How much is the profit for smoke turquoise purple blue salmon that was delivered in person on 5/7/1996? | SELECT T1.l_extendedprice * (1 - T1.l_discount) - T2.ps_supplycost * T1.l_quantity AS num FROM lineitem AS T1 INNER JOIN partsupp AS T2 ON T1.l_suppkey = T2.ps_suppkey INNER JOIN part AS T3 ON T2.ps_partkey = T3.p_partkey WHERE T1.l_receiptdate = '1996-05-07' AND T1.l_shipinstruct = 'DELIVER IN PERSON' AND T3.p_name = ... | [
"How",
"much",
"is",
"the",
"profit",
"for",
"smoke",
"turquoise",
"purple",
"blue",
"salmon",
"that",
"was",
"delivered",
"in",
"person",
"on",
"5/7/1996",
"?"
] | [
{
"id": 10,
"type": "value",
"value": "smoke turquoise purple blue salmon"
},
{
"id": 8,
"type": "value",
"value": "DELIVER IN PERSON"
},
{
"id": 11,
"type": "column",
"value": "l_extendedprice"
},
{
"id": 7,
"type": "column",
"value": "l_shipinstruct"
}... | [
{
"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",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O"
] |
14,145 | real_estate_rentals | bird:test.json:1437 | What are the login names of all senior citizens, sorted by first name? | SELECT login_name FROM Users WHERE user_category_code = 'Senior Citizen' ORDER BY first_name | [
"What",
"are",
"the",
"login",
"names",
"of",
"all",
"senior",
"citizens",
",",
"sorted",
"by",
"first",
"name",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "user_category_code"
},
{
"id": 3,
"type": "value",
"value": "Senior Citizen"
},
{
"id": 1,
"type": "column",
"value": "login_name"
},
{
"id": 4,
"type": "column",
"value": "first_name"
},
{
"id": 0,
"type"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
"entity_id": 4,
"token_idxs": [
12,
13
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,146 | riding_club | spider:train_spider.json:1724 | What is the name of the player with the largest number of votes? | SELECT Player_name FROM player ORDER BY Votes DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"player",
"with",
"the",
"largest",
"number",
"of",
"votes",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "player_name"
},
{
"id": 0,
"type": "table",
"value": "player"
},
{
"id": 2,
"type": "column",
"value": "votes"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,147 | mondial_geo | bird:train.json:8264 | Among the countries whose GDP is over 1000000, how many of them have a population groth rate of over 3%? | SELECT COUNT(T1.Country) FROM economy AS T1 INNER JOIN population AS T2 ON T1.Country = T2.Country WHERE T1.GDP > 1000000 AND T2.Population_Growth > 3 | [
"Among",
"the",
"countries",
"whose",
"GDP",
"is",
"over",
"1000000",
",",
"how",
"many",
"of",
"them",
"have",
"a",
"population",
"groth",
"rate",
"of",
"over",
"3",
"%",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "population_growth"
},
{
"id": 1,
"type": "table",
"value": "population"
},
{
"id": 0,
"type": "table",
"value": "economy"
},
{
"id": 2,
"type": "column",
"value": "country"
},
{
"id": 4,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": [
4
]
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
14,148 | hockey | bird:train.json:7693 | Among the players who had 10 empty net goals in their career, who is the tallest? Show his full name. | SELECT T1.firstName, T1.lastName FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID GROUP BY T2.playerID, T1.height HAVING SUM(T2.ENG) > 10 ORDER BY T1.height DESC LIMIT 1 | [
"Among",
"the",
"players",
"who",
"had",
"10",
"empty",
"net",
"goals",
"in",
"their",
"career",
",",
"who",
"is",
"the",
"tallest",
"?",
"Show",
"his",
"full",
"name",
"."
] | [
{
"id": 2,
"type": "column",
"value": "firstname"
},
{
"id": 0,
"type": "column",
"value": "playerid"
},
{
"id": 3,
"type": "column",
"value": "lastname"
},
{
"id": 5,
"type": "table",
"value": "goalies"
},
{
"id": 1,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
21
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,149 | mondial_geo | bird:train.json:8325 | List the all the cities and its city population for provinces with population more than 1000000. | SELECT T1.Name, T1.Population FROM city AS T1 INNER JOIN province AS T2 ON T2.Name = T1.Province WHERE T2.Population > 1000000 | [
"List",
"the",
"all",
"the",
"cities",
"and",
"its",
"city",
"population",
"for",
"provinces",
"with",
"population",
"more",
"than",
"1000000",
"."
] | [
{
"id": 1,
"type": "column",
"value": "population"
},
{
"id": 3,
"type": "table",
"value": "province"
},
{
"id": 5,
"type": "column",
"value": "province"
},
{
"id": 4,
"type": "value",
"value": "1000000"
},
{
"id": 0,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
14,150 | advertising_agencies | bird:test.json:2080 | What are the agency ids and agency details for all agencies who have a client? | SELECT T1.agency_id , T1.agency_details FROM Agencies AS T1 JOIN Clients AS T2 ON T1.agency_id = T2.agency_id | [
"What",
"are",
"the",
"agency",
"ids",
"and",
"agency",
"details",
"for",
"all",
"agencies",
"who",
"have",
"a",
"client",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "agency_details"
},
{
"id": 0,
"type": "column",
"value": "agency_id"
},
{
"id": 2,
"type": "table",
"value": "agencies"
},
{
"id": 3,
"type": "table",
"value": "clients"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,151 | soccer_1 | spider:train_spider.json:1303 | List all of the player ids with a height of at least 180cm and an overall rating higher than 85. | SELECT player_api_id FROM Player WHERE height >= 180 INTERSECT SELECT player_api_id FROM Player_Attributes WHERE overall_rating > 85 | [
"List",
"all",
"of",
"the",
"player",
"ids",
"with",
"a",
"height",
"of",
"at",
"least",
"180",
"cm",
"and",
"an",
"overall",
"rating",
"higher",
"than",
"85",
"."
] | [
{
"id": 1,
"type": "table",
"value": "player_attributes"
},
{
"id": 5,
"type": "column",
"value": "overall_rating"
},
{
"id": 2,
"type": "column",
"value": "player_api_id"
},
{
"id": 0,
"type": "table",
"value": "player"
},
{
"id": 3,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
14,152 | allergy_1 | spider:train_spider.json:459 | How many students are there? | SELECT count(*) FROM Student | [
"How",
"many",
"students",
"are",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
14,154 | college_2 | spider:train_spider.json:1367 | What are the ids of instructors who taught in the Fall of 2009 but not in the Spring of 2010? | SELECT id FROM teaches WHERE semester = 'Fall' AND YEAR = 2009 EXCEPT SELECT id FROM teaches WHERE semester = 'Spring' AND YEAR = 2010 | [
"What",
"are",
"the",
"ids",
"of",
"instructors",
"who",
"taught",
"in",
"the",
"Fall",
"of",
"2009",
"but",
"not",
"in",
"the",
"Spring",
"of",
"2010",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "semester"
},
{
"id": 0,
"type": "table",
"value": "teaches"
},
{
"id": 6,
"type": "value",
"value": "Spring"
},
{
"id": 3,
"type": "value",
"value": "Fall"
},
{
"id": 4,
"type": "column",
"value": "yea... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
14,155 | movies_4 | bird:train.json:560 | How many main actors are there in the movie Pirates of the Caribbean: At World's End? | SELECT COUNT(T2.cast_order) FROM movie AS T1 INNER JOIN movie_cast AS T2 ON T1.movie_id = T2.movie_id INNER JOIN gender AS T3 ON T3.gender_id = T2.gender_id WHERE T3.gender = 'Male' OR T3.gender = 'Female' AND T1.title = 'Pirates of the Caribbean: At World''s End' AND T2.cast_order = ( SELECT MIN(T2.cast_order) FROM mo... | [
"How",
"many",
"main",
"actors",
"are",
"there",
"in",
"the",
"movie",
"Pirates",
"of",
"the",
"Caribbean",
":",
"At",
"World",
"'s",
"End",
"?"
] | [
{
"id": 10,
"type": "value",
"value": "Pirates of the Caribbean: At World's End"
},
{
"id": 1,
"type": "column",
"value": "cast_order"
},
{
"id": 3,
"type": "table",
"value": "movie_cast"
},
{
"id": 4,
"type": "column",
"value": "gender_id"
},
{
"i... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"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": [
17
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
14,157 | sales | bird:train.json:5435 | In customers with the first name of Erica, how many of them bought a quantity below 200? | SELECT COUNT(T1.ProductID) FROM Sales AS T1 INNER JOIN Customers AS T2 ON T1.CustomerID = T2.CustomerID WHERE T2.FirstName = 'Erica' AND T1.Quantity < 200 | [
"In",
"customers",
"with",
"the",
"first",
"name",
"of",
"Erica",
",",
"how",
"many",
"of",
"them",
"bought",
"a",
"quantity",
"below",
"200",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "customerid"
},
{
"id": 1,
"type": "table",
"value": "customers"
},
{
"id": 2,
"type": "column",
"value": "productid"
},
{
"id": 4,
"type": "column",
"value": "firstname"
},
{
"id": 6,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4,
5
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
14,158 | student_loan | bird:train.json:4437 | State name of unemployed students who have the longest duration of absense from school. | SELECT T1.name FROM longest_absense_from_school AS T1 INNER JOIN unemployed AS T2 ON T1.name = T2.name ORDER BY T1.month DESC LIMIT 1 | [
"State",
"name",
"of",
"unemployed",
"students",
"who",
"have",
"the",
"longest",
"duration",
"of",
"absense",
"from",
"school",
"."
] | [
{
"id": 1,
"type": "table",
"value": "longest_absense_from_school"
},
{
"id": 2,
"type": "table",
"value": "unemployed"
},
{
"id": 3,
"type": "column",
"value": "month"
},
{
"id": 0,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
11,
12,
13
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O"
] |
14,159 | formula_1 | bird:dev.json:954 | Please calculate the race completion percentage of Japanese drivers from 2007 to 2009. | SELECT CAST(SUM(IIF(T1.time IS NOT NULL, 1, 0)) AS REAL) * 100 / COUNT(T1.raceId) FROM results AS T1 INNER JOIN races AS T2 on T1.raceId = T2.raceId INNER JOIN drivers AS T3 on T1.driverId = T3.driverId WHERE T3.nationality = 'Japanese' AND T2.year BETWEEN 2007 AND 2009 | [
"Please",
"calculate",
"the",
"race",
"completion",
"percentage",
"of",
"Japanese",
"drivers",
"from",
"2007",
"to",
"2009",
"."
] | [
{
"id": 4,
"type": "column",
"value": "nationality"
},
{
"id": 3,
"type": "column",
"value": "driverid"
},
{
"id": 5,
"type": "value",
"value": "Japanese"
},
{
"id": 0,
"type": "table",
"value": "drivers"
},
{
"id": 1,
"type": "table",
"val... | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
14,160 | flight_4 | spider:train_spider.json:6864 | For each country, what is the average elevation of that country's airports? | SELECT avg(elevation) , country FROM airports GROUP BY country | [
"For",
"each",
"country",
",",
"what",
"is",
"the",
"average",
"elevation",
"of",
"that",
"country",
"'s",
"airports",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "elevation"
},
{
"id": 0,
"type": "table",
"value": "airports"
},
{
"id": 1,
"type": "column",
"value": "country"
}
] | [
{
"entity_id": 0,
"token_idxs": [
13
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O"
] |
14,162 | warehouse_1 | bird:test.json:1697 | Select the average value of all the boxes. | SELECT avg(value) FROM boxes | [
"Select",
"the",
"average",
"value",
"of",
"all",
"the",
"boxes",
"."
] | [
{
"id": 0,
"type": "table",
"value": "boxes"
},
{
"id": 1,
"type": "column",
"value": "value"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,163 | e_commerce | bird:test.json:111 | What is the least common order status? | SELECT order_status_code FROM Orders GROUP BY order_status_code ORDER BY count(*) LIMIT 1 | [
"What",
"is",
"the",
"least",
"common",
"order",
"status",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "order_status_code"
},
{
"id": 0,
"type": "table",
"value": "orders"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,164 | card_games | bird:dev.json:489 | List the keyrune code for the set whose code is 'PKHC'. | SELECT keyruneCode FROM sets WHERE code = 'PKHC' | [
"List",
"the",
"keyrune",
"code",
"for",
"the",
"set",
"whose",
"code",
"is",
"'",
"PKHC",
"'",
"."
] | [
{
"id": 1,
"type": "column",
"value": "keyrunecode"
},
{
"id": 0,
"type": "table",
"value": "sets"
},
{
"id": 2,
"type": "column",
"value": "code"
},
{
"id": 3,
"type": "value",
"value": "PKHC"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
14,167 | professional_basketball | bird:train.json:2817 | How many total minutes has the Brooklyn-born player, known by the name of Superman, played during all of his NBA All-Star seasons? | SELECT SUM(T2.minutes) FROM players AS T1 INNER JOIN player_allstar AS T2 ON T1.playerID = T2.playerID WHERE T1.birthCity = 'Brooklyn' AND T1.nameNick LIKE '%Superman%' | [
"How",
"many",
"total",
"minutes",
"has",
"the",
"Brooklyn",
"-",
"born",
"player",
",",
"known",
"by",
"the",
"name",
"of",
"Superman",
",",
"played",
"during",
"all",
"of",
"his",
"NBA",
"All",
"-",
"Star",
"seasons",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "player_allstar"
},
{
"id": 7,
"type": "value",
"value": "%Superman%"
},
{
"id": 4,
"type": "column",
"value": "birthcity"
},
{
"id": 3,
"type": "column",
"value": "playerid"
},
{
"id": 5,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
24,
25,
26
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
18
]
},
{
"entity_id": 4,
"token_idxs": []... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O",
"O"
] |
14,168 | dorm_1 | spider:train_spider.json:5762 | Find the first and last name of students who are living in the dorms that have amenity TV Lounge. | SELECT T1.fname , T1.lname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid = T2.stuid WHERE T2.dormid IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid = T4.amenid WHERE T4.amenity_name = 'TV Lounge') | [
"Find",
"the",
"first",
"and",
"last",
"name",
"of",
"students",
"who",
"are",
"living",
"in",
"the",
"dorms",
"that",
"have",
"amenity",
"TV",
"Lounge",
"."
] | [
{
"id": 7,
"type": "table",
"value": "dorm_amenity"
},
{
"id": 8,
"type": "column",
"value": "amenity_name"
},
{
"id": 6,
"type": "table",
"value": "has_amenity"
},
{
"id": 9,
"type": "value",
"value": "TV Lounge"
},
{
"id": 3,
"type": "table",... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"O"
] |
14,169 | language_corpus | bird:train.json:5782 | What's the occurrence of the biwords pair whose first word is "àbac" and second word is "xinès"? | SELECT occurrences FROM biwords WHERE w1st = ( SELECT wid FROM words WHERE word = 'àbac' ) AND w2nd = ( SELECT wid FROM words WHERE word = 'xinès' ) | [
"What",
"'s",
"the",
"occurrence",
"of",
"the",
"biwords",
"pair",
"whose",
"first",
"word",
"is",
"\"",
"àbac",
"\"",
"and",
"second",
"word",
"is",
"\"",
"xinès",
"\"",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "occurrences"
},
{
"id": 0,
"type": "table",
"value": "biwords"
},
{
"id": 4,
"type": "table",
"value": "words"
},
{
"id": 8,
"type": "value",
"value": "xinès"
},
{
"id": 2,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
14,170 | music_2 | spider:train_spider.json:5201 | What are the names of the songs whose title has the word "the"? | SELECT title FROM songs WHERE title LIKE '% the %' | [
"What",
"are",
"the",
"names",
"of",
"the",
"songs",
"whose",
"title",
"has",
"the",
"word",
"\"",
"the",
"\"",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "% the %"
},
{
"id": 0,
"type": "table",
"value": "songs"
},
{
"id": 1,
"type": "column",
"value": "title"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,171 | toxicology | bird:dev.json:287 | Among all chemical compounds that contain molecule TR047, identify the percent that form a double-bond. | SELECT CAST(COUNT(CASE WHEN T.bond_type = '=' THEN T.bond_id ELSE NULL END) AS REAL) * 100 / COUNT(T.bond_id) FROM bond AS T WHERE T.molecule_id = 'TR047' | [
"Among",
"all",
"chemical",
"compounds",
"that",
"contain",
"molecule",
"TR047",
",",
"identify",
"the",
"percent",
"that",
"form",
"a",
"double",
"-",
"bond",
"."
] | [
{
"id": 1,
"type": "column",
"value": "molecule_id"
},
{
"id": 5,
"type": "column",
"value": "bond_type"
},
{
"id": 4,
"type": "column",
"value": "bond_id"
},
{
"id": 2,
"type": "value",
"value": "TR047"
},
{
"id": 0,
"type": "table",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
17
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,172 | thrombosis_prediction | bird:dev.json:1271 | How many patients with a normal anti-SSA came to the hospital before 2000? | SELECT COUNT(DISTINCT T1.ID) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.SSA IN ('negative', '0') AND STRFTIME('%Y', T2.Date) < '2000' | [
"How",
"many",
"patients",
"with",
"a",
"normal",
"anti",
"-",
"SSA",
"came",
"to",
"the",
"hospital",
"before",
"2000",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "laboratory"
},
{
"id": 4,
"type": "value",
"value": "negative"
},
{
"id": 0,
"type": "table",
"value": "patient"
},
{
"id": 6,
"type": "value",
"value": "2000"
},
{
"id": 8,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,173 | warehouse_1 | bird:test.json:1751 | Find the number of boxes saved in each warehouse. | SELECT count(*) , warehouse FROM boxes GROUP BY warehouse | [
"Find",
"the",
"number",
"of",
"boxes",
"saved",
"in",
"each",
"warehouse",
"."
] | [
{
"id": 1,
"type": "column",
"value": "warehouse"
},
{
"id": 0,
"type": "table",
"value": "boxes"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,174 | cre_Docs_and_Epenses | spider:train_spider.json:6434 | What are the ids and details for each project? | SELECT project_id , project_details FROM Projects | [
"What",
"are",
"the",
"ids",
"and",
"details",
"for",
"each",
"project",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "project_details"
},
{
"id": 1,
"type": "column",
"value": "project_id"
},
{
"id": 0,
"type": "table",
"value": "projects"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,175 | legislator | bird:train.json:4895 | Please list the full official names of all the current legislators who served the term that started on 2013/1/3. | SELECT T1.official_full_name FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.start = '2013-01-03' | [
"Please",
"list",
"the",
"full",
"official",
"names",
"of",
"all",
"the",
"current",
"legislators",
"who",
"served",
"the",
"term",
"that",
"started",
"on",
"2013/1/3",
"."
] | [
{
"id": 0,
"type": "column",
"value": "official_full_name"
},
{
"id": 2,
"type": "table",
"value": "current-terms"
},
{
"id": 5,
"type": "column",
"value": "bioguide_id"
},
{
"id": 4,
"type": "value",
"value": "2013-01-03"
},
{
"id": 6,
"type":... | [
{
"entity_id": 0,
"token_idxs": [
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16
]
},
{
"entity_id": 4,
"token_idxs": [
18
]
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
14,176 | restaurant_bills | bird:test.json:623 | Find the average card credit customers whose membership level is above 1. | SELECT avg(Card_Credit) FROM customer WHERE Level_of_Membership > 1 | [
"Find",
"the",
"average",
"card",
"credit",
"customers",
"whose",
"membership",
"level",
"is",
"above",
"1",
"."
] | [
{
"id": 1,
"type": "column",
"value": "level_of_membership"
},
{
"id": 3,
"type": "column",
"value": "card_credit"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 2,
"type": "value",
"value": "1"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
3,
4
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,177 | movie_platform | bird:train.json:148 | For the list with more than 200 followers, state the title and how long the list has been created? | SELECT list_title , 365 * (strftime('%Y', 'now') - strftime('%Y', list_creation_timestamp_utc)) + 30 * (strftime('%m', 'now') - strftime('%m', list_creation_timestamp_utc)) + strftime('%d', 'now') - strftime('%d', list_creation_timestamp_utc) FROM lists WHERE list_followers > 200 | [
"For",
"the",
"list",
"with",
"more",
"than",
"200",
"followers",
",",
"state",
"the",
"title",
"and",
"how",
"long",
"the",
"list",
"has",
"been",
"created",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "list_creation_timestamp_utc"
},
{
"id": 2,
"type": "column",
"value": "list_followers"
},
{
"id": 1,
"type": "column",
"value": "list_title"
},
{
"id": 0,
"type": "table",
"value": "lists"
},
{
"id": 3,
"t... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
14,178 | dorm_1 | spider:train_spider.json:5707 | What is the last name of every student who is either female or living in a city with the code BAL or male and under 20? | SELECT lname FROM student WHERE sex = 'F' AND city_code = 'BAL' UNION SELECT lname FROM student WHERE sex = 'M' AND age < 20 | [
"What",
"is",
"the",
"last",
"name",
"of",
"every",
"student",
"who",
"is",
"either",
"female",
"or",
"living",
"in",
"a",
"city",
"with",
"the",
"code",
"BAL",
"or",
"male",
"and",
"under",
"20",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "city_code"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "column",
"value": "lname"
},
{
"id": 2,
"type": "column",
"value": "sex"
},
{
"id": 5,
"type": "value",
"value": "BAL... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
19
]
},
{
"entity... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,179 | software_company | bird:train.json:8555 | Among the male customer in their twenties, how many are from places where the average income is more than 3000? | SELECT COUNT(T2.GEOID) FROM Customers AS T1 INNER JOIN Demog AS T2 ON T1.GEOID = T2.GEOID WHERE T1.SEX = 'Male' AND T2.INCOME_K > 3000 AND T1.age >= 20 AND T1.age <= 29 | [
"Among",
"the",
"male",
"customer",
"in",
"their",
"twenties",
",",
"how",
"many",
"are",
"from",
"places",
"where",
"the",
"average",
"income",
"is",
"more",
"than",
"3000",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "customers"
},
{
"id": 5,
"type": "column",
"value": "income_k"
},
{
"id": 1,
"type": "table",
"value": "demog"
},
{
"id": 2,
"type": "column",
"value": "geoid"
},
{
"id": 4,
"type": "value",
"value": "M... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
0
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
"... | [
"B-TABLE",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,180 | document_management | spider:train_spider.json:4501 | Find the name of the document that has been accessed the greatest number of times, as well as the count of how many times it has been accessed? | SELECT document_name , access_count FROM documents ORDER BY access_count DESC LIMIT 1 | [
"Find",
"the",
"name",
"of",
"the",
"document",
"that",
"has",
"been",
"accessed",
"the",
"greatest",
"number",
"of",
"times",
",",
"as",
"well",
"as",
"the",
"count",
"of",
"how",
"many",
"times",
"it",
"has",
"been",
"accessed",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "document_name"
},
{
"id": 2,
"type": "column",
"value": "access_count"
},
{
"id": 0,
"type": "table",
"value": "documents"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
28
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
14,181 | student_loan | bird:train.json:4443 | How many students are disabled? | SELECT COUNT(name) FROM disabled | [
"How",
"many",
"students",
"are",
"disabled",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "disabled"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"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"
] |
14,182 | works_cycles | bird:train.json:7446 | What are the products with a large photo? | SELECT T2.ProductID FROM ProductPhoto AS T1 INNER JOIN ProductProductPhoto AS T2 ON T1.ProductPhotoID = T2.ProductPhotoID WHERE T1.LargePhotoFileName LIKE '%large.gif' | [
"What",
"are",
"the",
"products",
"with",
"a",
"large",
"photo",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "productproductphoto"
},
{
"id": 3,
"type": "column",
"value": "largephotofilename"
},
{
"id": 5,
"type": "column",
"value": "productphotoid"
},
{
"id": 1,
"type": "table",
"value": "productphoto"
},
{
"id": 4,
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
14,183 | book_publishing_company | bird:train.json:236 | Calculate the average level difference between the Marketing editors hired by the US and non-US publishers? | SELECT (CAST(SUM(CASE WHEN T1.country = 'USA' THEN job_lvl ELSE 0 END) AS REAL) / SUM(CASE WHEN T1.country = 'USA' THEN 1 ELSE 0 END)) - (CAST(SUM(CASE WHEN T1.country != 'USA' THEN job_lvl ELSE 0 END) AS REAL) / SUM(CASE WHEN T1.country != 'USA' THEN 1 ELSE 0 END)) FROM publishers AS T1 INNER JOIN employee AS T2 ON T1... | [
"Calculate",
"the",
"average",
"level",
"difference",
"between",
"the",
"Marketing",
"editors",
"hired",
"by",
"the",
"US",
"and",
"non",
"-",
"US",
"publishers",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Managing Editor"
},
{
"id": 3,
"type": "table",
"value": "publishers"
},
{
"id": 1,
"type": "column",
"value": "job_desc"
},
{
"id": 4,
"type": "table",
"value": "employee"
},
{
"id": 9,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
17
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"tok... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
14,184 | store_product | spider:train_spider.json:4939 | What are the names of all products that are not the most frequently-used maximum page size? | SELECT product FROM product WHERE product != (SELECT max_page_size FROM product GROUP BY max_page_size ORDER BY count(*) DESC LIMIT 1) | [
"What",
"are",
"the",
"names",
"of",
"all",
"products",
"that",
"are",
"not",
"the",
"most",
"frequently",
"-",
"used",
"maximum",
"page",
"size",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "max_page_size"
},
{
"id": 0,
"type": "table",
"value": "product"
},
{
"id": 1,
"type": "column",
"value": "product"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
16,
17
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,185 | warehouse_1 | bird:test.json:1750 | What are the codes of boxes stored in warehouses in Chicago? | SELECT T1.code FROM boxes AS T1 JOIN Warehouses AS T2 ON T1.warehouse = T2.code WHERE T2.location = 'Chicago' | [
"What",
"are",
"the",
"codes",
"of",
"boxes",
"stored",
"in",
"warehouses",
"in",
"Chicago",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "warehouses"
},
{
"id": 5,
"type": "column",
"value": "warehouse"
},
{
"id": 3,
"type": "column",
"value": "location"
},
{
"id": 4,
"type": "value",
"value": "Chicago"
},
{
"id": 1,
"type": "table",
"val... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
14,186 | student_club | bird:dev.json:1326 | How many members of the Student_Club have majored Environmental Engineering?
| SELECT COUNT(T1.member_id) FROM member AS T1 INNER JOIN major AS T2 ON T1.link_to_major = T2.major_id WHERE T2.major_name = 'Environmental Engineering' | [
"How",
"many",
"members",
"of",
"the",
"Student_Club",
"have",
"majored",
"Environmental",
"Engineering",
"?",
"\n"
] | [
{
"id": 3,
"type": "value",
"value": "Environmental Engineering"
},
{
"id": 5,
"type": "column",
"value": "link_to_major"
},
{
"id": 2,
"type": "column",
"value": "major_name"
},
{
"id": 4,
"type": "column",
"value": "member_id"
},
{
"id": 6,
"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
14,187 | works_cycles | bird:train.json:7123 | What is the currency of Brazil? | SELECT T1.Name FROM Currency AS T1 INNER JOIN CountryRegionCurrency AS T2 ON T1.CurrencyCode = T2.CurrencyCode INNER JOIN CountryRegion AS T3 ON T2.CountryRegionCode = T3.CountryRegionCode WHERE T3.Name = 'Brazil' | [
"What",
"is",
"the",
"currency",
"of",
"Brazil",
"?"
] | [
{
"id": 4,
"type": "table",
"value": "countryregioncurrency"
},
{
"id": 5,
"type": "column",
"value": "countryregioncode"
},
{
"id": 1,
"type": "table",
"value": "countryregion"
},
{
"id": 6,
"type": "column",
"value": "currencycode"
},
{
"id": 3,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
14,188 | codebase_community | bird:dev.json:695 | How many users were awarded with 'Citizen Patrol' badge? | SELECT COUNT(id) FROM badges WHERE `Name` = 'Citizen Patrol' | [
"How",
"many",
"users",
"were",
"awarded",
"with",
"'",
"Citizen",
"Patrol",
"'",
"badge",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Citizen Patrol"
},
{
"id": 0,
"type": "table",
"value": "badges"
},
{
"id": 1,
"type": "column",
"value": "Name"
},
{
"id": 3,
"type": "column",
"value": "id"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"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,
"tok... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-TABLE",
"O"
] |
14,189 | election | spider:train_spider.json:2749 | What are the delegate and committee information for each election record? | SELECT Delegate , Committee FROM election | [
"What",
"are",
"the",
"delegate",
"and",
"committee",
"information",
"for",
"each",
"election",
"record",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "committee"
},
{
"id": 0,
"type": "table",
"value": "election"
},
{
"id": 1,
"type": "column",
"value": "delegate"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O"
] |
14,190 | cars | bird:train.json:3083 | Provide the engine displacement status of the $37443.85589 car. | SELECT T1.displacement FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID WHERE T2.price = '37443.85589' | [
"Provide",
"the",
"engine",
"displacement",
"status",
"of",
"the",
"$",
"37443.85589",
"car",
"."
] | [
{
"id": 0,
"type": "column",
"value": "displacement"
},
{
"id": 4,
"type": "value",
"value": "37443.85589"
},
{
"id": 2,
"type": "table",
"value": "price"
},
{
"id": 3,
"type": "column",
"value": "price"
},
{
"id": 1,
"type": "table",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
0
]
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id": 5,
"... | [
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
14,191 | codebase_comments | bird:train.json:632 | What is the path of solution of "spinachLexer.mT__55" method? | SELECT T1.Path FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.Name = 'spinachLexer.mT__55' | [
"What",
"is",
"the",
"path",
"of",
"solution",
"of",
"\"",
"spinachLexer.mT__55",
"\"",
"method",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "spinachLexer.mT__55"
},
{
"id": 6,
"type": "column",
"value": "solutionid"
},
{
"id": 1,
"type": "table",
"value": "solution"
},
{
"id": 2,
"type": "table",
"value": "method"
},
{
"id": 0,
"type": "column",... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"B-TABLE",
"O"
] |
14,192 | regional_sales | bird:train.json:2665 | Find the store ID with more orders between "Aurora" and "Babylon" city. | SELECT T2.StoreID FROM `Sales Orders` AS T1 INNER JOIN `Store Locations` AS T2 ON T2.StoreID = T1._StoreID WHERE T2.`City Name` = 'Aurora (Township)' OR T2.`City Name` = 'Babylon (Town)' GROUP BY T2.StoreID ORDER BY COUNT(T1.OrderNumber) DESC LIMIT 1 | [
"Find",
"the",
"store",
"ID",
"with",
"more",
"orders",
"between",
"\"",
"Aurora",
"\"",
"and",
"\"",
"Babylon",
"\"",
"city",
"."
] | [
{
"id": 5,
"type": "value",
"value": "Aurora (Township)"
},
{
"id": 2,
"type": "table",
"value": "Store Locations"
},
{
"id": 6,
"type": "value",
"value": "Babylon (Town)"
},
{
"id": 1,
"type": "table",
"value": "Sales Orders"
},
{
"id": 7,
"ty... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
"entity_id"... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
14,193 | restaurant_bills | bird:test.json:622 | What is the average card credit of customers with membership level higher than 1? | SELECT avg(Card_Credit) FROM customer WHERE Level_of_Membership > 1 | [
"What",
"is",
"the",
"average",
"card",
"credit",
"of",
"customers",
"with",
"membership",
"level",
"higher",
"than",
"1",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "level_of_membership"
},
{
"id": 3,
"type": "column",
"value": "card_credit"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 2,
"type": "value",
"value": "1"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
4,
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
14,194 | book_1 | bird:test.json:532 | What are the ids of all orders and the corresponding client names? | SELECT T1.idOrder , T2.name FROM Orders AS T1 JOIN Client AS T2 ON T1.idClient = T2.idClient | [
"What",
"are",
"the",
"ids",
"of",
"all",
"orders",
"and",
"the",
"corresponding",
"client",
"names",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "idclient"
},
{
"id": 0,
"type": "column",
"value": "idorder"
},
{
"id": 2,
"type": "table",
"value": "orders"
},
{
"id": 3,
"type": "table",
"value": "client"
},
{
"id": 1,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,195 | european_football_2 | bird:dev.json:1098 | What is Ajax's highest chance creation passing score and what is it classified as? | SELECT t2.chanceCreationPassing, t2.chanceCreationPassingClass FROM Team AS t1 INNER JOIN Team_Attributes AS t2 ON t1.team_api_id = t2.team_api_id WHERE t1.team_long_name = 'Ajax' ORDER BY t2.chanceCreationPassing DESC LIMIT 1 | [
"What",
"is",
"Ajax",
"'s",
"highest",
"chance",
"creation",
"passing",
"score",
"and",
"what",
"is",
"it",
"classified",
"as",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "chancecreationpassingclass"
},
{
"id": 0,
"type": "column",
"value": "chancecreationpassing"
},
{
"id": 3,
"type": "table",
"value": "team_attributes"
},
{
"id": 4,
"type": "column",
"value": "team_long_name"
},
{... | [
{
"entity_id": 0,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_id... | [
"O",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,197 | codebase_community | bird:dev.json:632 | How many votes were made by Harlan? | SELECT COUNT(T1.Id) FROM users AS T1 INNER JOIN postHistory AS T2 ON T1.Id = T2.UserId INNER JOIN votes AS T3 ON T3.PostId = T2.PostId WHERE T1.DisplayName = 'Harlan' | [
"How",
"many",
"votes",
"were",
"made",
"by",
"Harlan",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "displayname"
},
{
"id": 5,
"type": "table",
"value": "posthistory"
},
{
"id": 2,
"type": "value",
"value": "Harlan"
},
{
"id": 6,
"type": "column",
"value": "postid"
},
{
"id": 7,
"type": "column",
"va... | [
{
"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"
] |
14,198 | e_learning | spider:train_spider.json:3826 | What is the name of each course and the corresponding number of student enrollment? | SELECT T1.course_name , COUNT(*) FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id = T2.course_id GROUP BY T1.course_name | [
"What",
"is",
"the",
"name",
"of",
"each",
"course",
"and",
"the",
"corresponding",
"number",
"of",
"student",
"enrollment",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "student_course_enrolment"
},
{
"id": 0,
"type": "column",
"value": "course_name"
},
{
"id": 3,
"type": "column",
"value": "course_id"
},
{
"id": 1,
"type": "table",
"value": "courses"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
12,
13
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
14,199 | climbing | spider:train_spider.json:1113 | What are the names of the climbers, ordered by points descending? | SELECT Name FROM climber ORDER BY Points DESC | [
"What",
"are",
"the",
"names",
"of",
"the",
"climbers",
",",
"ordered",
"by",
"points",
"descending",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "climber"
},
{
"id": 2,
"type": "column",
"value": "points"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
14,200 | book_review | bird:test.json:596 | What are the types and release dates of books? | SELECT TYPE , Release FROM book | [
"What",
"are",
"the",
"types",
"and",
"release",
"dates",
"of",
"books",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "release"
},
{
"id": 0,
"type": "table",
"value": "book"
},
{
"id": 1,
"type": "column",
"value": "type"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
14,201 | assets_maintenance | spider:train_spider.json:3147 | Which fault log included the most number of faulty parts? List the fault log id, description and record time. | SELECT T1.fault_log_entry_id , T1.fault_description , T1.fault_log_entry_datetime FROM Fault_Log AS T1 JOIN Fault_Log_Parts AS T2 ON T1.fault_log_entry_id = T2.fault_log_entry_id GROUP BY T1.fault_log_entry_id ORDER BY count(*) DESC LIMIT 1 | [
"Which",
"fault",
"log",
"included",
"the",
"most",
"number",
"of",
"faulty",
"parts",
"?",
"List",
"the",
"fault",
"log",
"i",
"d",
",",
"description",
"and",
"record",
"time",
"."
] | [
{
"id": 2,
"type": "column",
"value": "fault_log_entry_datetime"
},
{
"id": 0,
"type": "column",
"value": "fault_log_entry_id"
},
{
"id": 1,
"type": "column",
"value": "fault_description"
},
{
"id": 4,
"type": "table",
"value": "fault_log_parts"
},
{
... | [
{
"entity_id": 0,
"token_idxs": [
15,
16
]
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
13,
14
]
},
{
"entity_id": 4,
"token_idxs": [
8,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
14,203 | beer_factory | bird:train.json:5249 | Please list the dates on which a male customer has purchased more than 3 root beers. | SELECT T2.TransactionDate FROM customers AS T1 INNER JOIN `transaction` AS T2 ON T1.CustomerID = T2.CustomerID WHERE T1.Gender = 'M' GROUP BY T2.TransactionDate HAVING COUNT(T2.CustomerID) > 3 | [
"Please",
"list",
"the",
"dates",
"on",
"which",
"a",
"male",
"customer",
"has",
"purchased",
"more",
"than",
"3",
"root",
"beers",
"."
] | [
{
"id": 0,
"type": "column",
"value": "transactiondate"
},
{
"id": 2,
"type": "table",
"value": "transaction"
},
{
"id": 6,
"type": "column",
"value": "customerid"
},
{
"id": 1,
"type": "table",
"value": "customers"
},
{
"id": 3,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
13
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
14,204 | software_company | bird:train.json:8533 | List down the geographic identifier with an income that ranges from 2100 to 2500. | SELECT GEOID FROM Demog WHERE INCOME_K >= 2100 AND INCOME_K <= 2500 | [
"List",
"down",
"the",
"geographic",
"identifier",
"with",
"an",
"income",
"that",
"ranges",
"from",
"2100",
"to",
"2500",
"."
] | [
{
"id": 2,
"type": "column",
"value": "income_k"
},
{
"id": 0,
"type": "table",
"value": "demog"
},
{
"id": 1,
"type": "column",
"value": "geoid"
},
{
"id": 3,
"type": "value",
"value": "2100"
},
{
"id": 4,
"type": "value",
"value": "2500"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
14,205 | works_cycles | bird:train.json:7426 | Please list the product names of all the products on the LL Road Frame Sale. | SELECT T3.Name FROM SpecialOffer AS T1 INNER JOIN SpecialOfferProduct AS T2 ON T1.SpecialOfferID = T2.SpecialOfferID INNER JOIN Product AS T3 ON T2.ProductID = T3.ProductID WHERE T1.Description = 'LL Road Frame Sale' | [
"Please",
"list",
"the",
"product",
"names",
"of",
"all",
"the",
"products",
"on",
"the",
"LL",
"Road",
"Frame",
"Sale",
"."
] | [
{
"id": 5,
"type": "table",
"value": "specialofferproduct"
},
{
"id": 3,
"type": "value",
"value": "LL Road Frame Sale"
},
{
"id": 7,
"type": "column",
"value": "specialofferid"
},
{
"id": 4,
"type": "table",
"value": "specialoffer"
},
{
"id": 2,
... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11,
12,
13,
14
]
},
{
"entity_id": 4,
"token_idxs": []
}... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
14,206 | college_2 | spider:train_spider.json:1383 | Give all information regarding instructors, in order of salary from least to greatest. | SELECT * FROM instructor ORDER BY salary | [
"Give",
"all",
"information",
"regarding",
"instructors",
",",
"in",
"order",
"of",
"salary",
"from",
"least",
"to",
"greatest",
"."
] | [
{
"id": 0,
"type": "table",
"value": "instructor"
},
{
"id": 1,
"type": "column",
"value": "salary"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
14,207 | sing_contest | bird:test.json:745 | What are the voice sound quality score, rhythm tempo score and stage presence score performed by the participant named 'Freeway'? | SELECT T1.voice_sound_quality , T1.rhythm_tempo , T1.stage_presence FROM performance_score AS T1 JOIN participants AS T2 ON T1.participant_id = T2.id WHERE T2.name = 'Freeway' | [
"What",
"are",
"the",
"voice",
"sound",
"quality",
"score",
",",
"rhythm",
"tempo",
"score",
"and",
"stage",
"presence",
"score",
"performed",
"by",
"the",
"participant",
"named",
"'",
"Freeway",
"'",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "voice_sound_quality"
},
{
"id": 3,
"type": "table",
"value": "performance_score"
},
{
"id": 2,
"type": "column",
"value": "stage_presence"
},
{
"id": 7,
"type": "column",
"value": "participant_id"
},
{
"id": 1... | [
{
"entity_id": 0,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
8,
9
]
},
{
"entity_id": 2,
"token_idxs": [
12,
13
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O"
] |
14,208 | music_platform_2 | bird:train.json:7979 | What is the content of the review under the title "really interesting!" and is created on 2018-04-24 at 12:05:16? | SELECT content FROM reviews WHERE title = 'really interesting!' AND created_at = '2018-04-24T12:05:16-07:00' | [
"What",
"is",
"the",
"content",
"of",
"the",
"review",
"under",
"the",
"title",
"\"",
"really",
"interesting",
"!",
"\"",
"and",
"is",
"created",
"on",
"2018",
"-",
"04",
"-",
"24",
"at",
"12:05:16",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "2018-04-24T12:05:16-07:00"
},
{
"id": 3,
"type": "value",
"value": "really interesting!"
},
{
"id": 4,
"type": "column",
"value": "created_at"
},
{
"id": 0,
"type": "table",
"value": "reviews"
},
{
"id": 1,
... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
11,
12,
13
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
14,209 | country_language | bird:test.json:1374 | List the name of the country with the biggest score in politics. | SELECT name FROM countries ORDER BY politics_score DESC LIMIT 1 | [
"List",
"the",
"name",
"of",
"the",
"country",
"with",
"the",
"biggest",
"score",
"in",
"politics",
"."
] | [
{
"id": 2,
"type": "column",
"value": "politics_score"
},
{
"id": 0,
"type": "table",
"value": "countries"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"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",
"O",
"B-COLUMN",
"O"
] |
14,210 | restaurant | bird:train.json:1735 | What is the county of the Sankee restaurant? | SELECT T2.county FROM generalinfo AS T1 INNER JOIN geographic AS T2 ON T1.city = T2.city WHERE T1.label = 'sankee' | [
"What",
"is",
"the",
"county",
"of",
"the",
"Sankee",
"restaurant",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "generalinfo"
},
{
"id": 2,
"type": "table",
"value": "geographic"
},
{
"id": 0,
"type": "column",
"value": "county"
},
{
"id": 4,
"type": "value",
"value": "sankee"
},
{
"id": 3,
"type": "column",
"valu... | [
{
"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": [
6
]
},
{
"entity_id": 5,
"token_idxs": [
3
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
14,211 | hr_1 | spider:train_spider.json:3522 | What are the department names, cities, and state provinces for each department? | SELECT T1.department_name , T2.city , T2.state_province FROM departments AS T1 JOIN locations AS T2 ON T2.location_id = T1.location_id | [
"What",
"are",
"the",
"department",
"names",
",",
"cities",
",",
"and",
"state",
"provinces",
"for",
"each",
"department",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "department_name"
},
{
"id": 2,
"type": "column",
"value": "state_province"
},
{
"id": 3,
"type": "table",
"value": "departments"
},
{
"id": 5,
"type": "column",
"value": "location_id"
},
{
"id": 4,
"type":... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
14,212 | simpson_episodes | bird:train.json:4255 | Please indicate the keywords of the episode that won the Primetime Emmy Award category. | SELECT T2.keyword FROM Award AS T1 INNER JOIN Keyword AS T2 ON T2.episode_id = T1.episode_id WHERE T1.award_category = 'Primetime Emmy'; | [
"Please",
"indicate",
"the",
"keywords",
"of",
"the",
"episode",
"that",
"won",
"the",
"Primetime",
"Emmy",
"Award",
"category",
"."
] | [
{
"id": 3,
"type": "column",
"value": "award_category"
},
{
"id": 4,
"type": "value",
"value": "Primetime Emmy"
},
{
"id": 5,
"type": "column",
"value": "episode_id"
},
{
"id": 0,
"type": "column",
"value": "keyword"
},
{
"id": 2,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": [
10,
11
]
},
{... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"B-COLUMN",
"O"
] |
14,213 | government_shift | bird:test.json:364 | Which services were used by customers by more than 3 times? Give me the service details. | SELECT t1.service_details FROM services AS t1 JOIN customers_and_services AS t2 ON t1.service_id = t2.service_id GROUP BY t1.service_details HAVING count(*) > 3 | [
"Which",
"services",
"were",
"used",
"by",
"customers",
"by",
"more",
"than",
"3",
"times",
"?",
"Give",
"me",
"the",
"service",
"details",
"."
] | [
{
"id": 2,
"type": "table",
"value": "customers_and_services"
},
{
"id": 0,
"type": "column",
"value": "service_details"
},
{
"id": 4,
"type": "column",
"value": "service_id"
},
{
"id": 1,
"type": "table",
"value": "services"
},
{
"id": 3,
"typ... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
14,214 | shooting | bird:train.json:2475 | List all cases from the year 2012 in which the subject was deceased | SELECT case_number FROM incidents WHERE STRFTIME('%Y', date) > '2011' AND subject_statuses = 'Deceased' | [
"List",
"all",
"cases",
"from",
"the",
"year",
"2012",
"in",
"which",
"the",
"subject",
"was",
"deceased"
] | [
{
"id": 3,
"type": "column",
"value": "subject_statuses"
},
{
"id": 1,
"type": "column",
"value": "case_number"
},
{
"id": 0,
"type": "table",
"value": "incidents"
},
{
"id": 4,
"type": "value",
"value": "Deceased"
},
{
"id": 2,
"type": "value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity_i... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE"
] |
14,215 | student_club | bird:dev.json:1328 | List the last name of all the students who majored Law and Constitutional Studies.
| SELECT T1.last_name FROM member AS T1 INNER JOIN major AS T2 ON T1.link_to_major = T2.major_id WHERE T2.major_name = 'Law and Constitutional Studies' | [
"List",
"the",
"last",
"name",
"of",
"all",
"the",
"students",
"who",
"majored",
"Law",
"and",
"Constitutional",
"Studies",
".",
"\n"
] | [
{
"id": 4,
"type": "value",
"value": "Law and Constitutional Studies"
},
{
"id": 5,
"type": "column",
"value": "link_to_major"
},
{
"id": 3,
"type": "column",
"value": "major_name"
},
{
"id": 0,
"type": "column",
"value": "last_name"
},
{
"id": 6,
... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
10,
11,
12,
13
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
14,216 | advertising_agencies | bird:test.json:2130 | What are the meeting ids, meeting outcomes, meeting types, and client details for all meetings? | SELECT T1.meeting_id , T1.meeting_outcome , T1.meeting_type , T2.client_details FROM meetings AS T1 JOIN clients AS T2 ON T1.client_id = T2.client_id | [
"What",
"are",
"the",
"meeting",
"ids",
",",
"meeting",
"outcomes",
",",
"meeting",
"types",
",",
"and",
"client",
"details",
"for",
"all",
"meetings",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "meeting_outcome"
},
{
"id": 3,
"type": "column",
"value": "client_details"
},
{
"id": 2,
"type": "column",
"value": "meeting_type"
},
{
"id": 0,
"type": "column",
"value": "meeting_id"
},
{
"id": 6,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_id... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
14,217 | college_completion | bird:train.json:3713 | Which state is "Mercer University" located in? | SELECT T FROM ( SELECT DISTINCT CASE WHEN chronname = 'Mercer University' THEN state ELSE NULL END AS T FROM institution_details ) WHERE T IS NOT NULL | [
"Which",
"state",
"is",
"\"",
"Mercer",
"University",
"\"",
"located",
"in",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "institution_details"
},
{
"id": 4,
"type": "value",
"value": "Mercer University"
},
{
"id": 3,
"type": "column",
"value": "chronname"
},
{
"id": 2,
"type": "column",
"value": "state"
},
{
"id": 0,
"type": "... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
4,
5
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O"
] |
14,218 | retails | bird:train.json:6704 | What is the name of the customer with the highest amount of debt? | SELECT c_name FROM customer WHERE c_acctbal = ( SELECT MIN(c_acctbal) FROM customer ) | [
"What",
"is",
"the",
"name",
"of",
"the",
"customer",
"with",
"the",
"highest",
"amount",
"of",
"debt",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "c_acctbal"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 1,
"type": "column",
"value": "c_name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
14,219 | online_exams | bird:test.json:226 | Which students do not have any answers? Find their first names. | SELECT First_Name FROM Students WHERE Student_ID NOT IN (SELECT Student_ID FROM Student_Answers) | [
"Which",
"students",
"do",
"not",
"have",
"any",
"answers",
"?",
"Find",
"their",
"first",
"names",
"."
] | [
{
"id": 3,
"type": "table",
"value": "student_answers"
},
{
"id": 1,
"type": "column",
"value": "first_name"
},
{
"id": 2,
"type": "column",
"value": "student_id"
},
{
"id": 0,
"type": "table",
"value": "students"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
10,
11
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,220 | customers_and_addresses | spider:train_spider.json:6129 | Which product has been ordered most number of times? | SELECT t2.product_details FROM order_items AS t1 JOIN products AS t2 ON t1.product_id = t2.product_id GROUP BY t1.product_id ORDER BY count(*) DESC LIMIT 1 | [
"Which",
"product",
"has",
"been",
"ordered",
"most",
"number",
"of",
"times",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "product_details"
},
{
"id": 2,
"type": "table",
"value": "order_items"
},
{
"id": 0,
"type": "column",
"value": "product_id"
},
{
"id": 3,
"type": "table",
"value": "products"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5
]
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O"
] |
14,221 | country_language | bird:test.json:1364 | Show the names of countries in descending order of overall scores. | SELECT name FROM countries ORDER BY overall_score DESC | [
"Show",
"the",
"names",
"of",
"countries",
"in",
"descending",
"order",
"of",
"overall",
"scores",
"."
] | [
{
"id": 2,
"type": "column",
"value": "overall_score"
},
{
"id": 0,
"type": "table",
"value": "countries"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
9,
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id"... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,222 | address | bird:train.json:5189 | What are the states with an above-average female population? | SELECT DISTINCT T2.state FROM state AS T1 INNER JOIN zip_data AS T2 ON T1.abbreviation = T2.state WHERE T2.female_population > ( SELECT AVG(female_population) FROM zip_data ) | [
"What",
"are",
"the",
"states",
"with",
"an",
"above",
"-",
"average",
"female",
"population",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "female_population"
},
{
"id": 4,
"type": "column",
"value": "abbreviation"
},
{
"id": 2,
"type": "table",
"value": "zip_data"
},
{
"id": 0,
"type": "column",
"value": "state"
},
{
"id": 1,
"type": "table",... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"tok... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,223 | boat_1 | bird:test.json:850 | What are the ids of all boats that are reserved by someone? | SELECT DISTINCT bid FROM Reserves | [
"What",
"are",
"the",
"ids",
"of",
"all",
"boats",
"that",
"are",
"reserved",
"by",
"someone",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "reserves"
},
{
"id": 1,
"type": "column",
"value": "bid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
14,224 | voter_2 | spider:train_spider.json:5494 | Find the distinct first names of all the students who have vice president votes and whose city code is not PIT. | SELECT DISTINCT T1.Fname FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID = T2.VICE_PRESIDENT_Vote EXCEPT SELECT DISTINCT Fname FROM STUDENT WHERE city_code = "PIT" | [
"Find",
"the",
"distinct",
"first",
"names",
"of",
"all",
"the",
"students",
"who",
"have",
"vice",
"president",
"votes",
"and",
"whose",
"city",
"code",
"is",
"not",
"PIT",
"."
] | [
{
"id": 6,
"type": "column",
"value": "vice_president_vote"
},
{
"id": 2,
"type": "table",
"value": "voting_record"
},
{
"id": 3,
"type": "column",
"value": "city_code"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 1,
"type": "co... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16,
17
]
},
{
"entity_id": 4,
"token_idxs": [
20
]
},
{
"entity_i... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
14,225 | college_1 | spider:train_spider.json:3248 | How many employees are there all together? | SELECT count(*) FROM employee | [
"How",
"many",
"employees",
"are",
"there",
"all",
"together",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "employee"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
14,226 | world | bird:train.json:7843 | What is the surface area of the country where Sutton Coldfield city belongs? | SELECT T1.SurfaceArea FROM Country AS T1 INNER JOIN City AS T2 ON T1.Code = T2.CountryCode WHERE T2.Name = 'Sutton Coldfield' | [
"What",
"is",
"the",
"surface",
"area",
"of",
"the",
"country",
"where",
"Sutton",
"Coldfield",
"city",
"belongs",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Sutton Coldfield"
},
{
"id": 0,
"type": "column",
"value": "surfacearea"
},
{
"id": 6,
"type": "column",
"value": "countrycode"
},
{
"id": 1,
"type": "table",
"value": "country"
},
{
"id": 2,
"type": "table... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-COLUMN",
"B-TABLE",
"O",
"O"
] |
14,227 | theme_gallery | spider:train_spider.json:1678 | What are the themes and years for exhibitions, sorted by ticket price descending? | SELECT theme , YEAR FROM exhibition ORDER BY ticket_price DESC | [
"What",
"are",
"the",
"themes",
"and",
"years",
"for",
"exhibitions",
",",
"sorted",
"by",
"ticket",
"price",
"descending",
"?"
] | [
{
"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"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
11,
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
14,228 | behavior_monitoring | spider:train_spider.json:3090 | Return all distinct detention type codes. | SELECT DISTINCT detention_type_code FROM Detention | [
"Return",
"all",
"distinct",
"detention",
"type",
"codes",
"."
] | [
{
"id": 1,
"type": "column",
"value": "detention_type_code"
},
{
"id": 0,
"type": "table",
"value": "detention"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O"
] |
14,229 | party_people | spider:train_spider.json:2069 | How many party events do we have? | SELECT count(*) FROM party_events | [
"How",
"many",
"party",
"events",
"do",
"we",
"have",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "party_events"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"O"
] |
14,230 | movie_2 | bird:test.json:1813 | How many movies are playing across all theaters? | SELECT count(*) FROM movies AS T1 JOIN movietheaters AS T2 ON T1.code = T2.movie | [
"How",
"many",
"movies",
"are",
"playing",
"across",
"all",
"theaters",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "movietheaters"
},
{
"id": 0,
"type": "table",
"value": "movies"
},
{
"id": 3,
"type": "column",
"value": "movie"
},
{
"id": 2,
"type": "column",
"value": "code"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
14,231 | retails | bird:train.json:6764 | Find and list the part key of the parts which has an above-average retail price. | SELECT p_partkey FROM part WHERE p_retailprice > ( SELECT AVG(p_retailprice) FROM part ) | [
"Find",
"and",
"list",
"the",
"part",
"key",
"of",
"the",
"parts",
"which",
"has",
"an",
"above",
"-",
"average",
"retail",
"price",
"."
] | [
{
"id": 2,
"type": "column",
"value": "p_retailprice"
},
{
"id": 1,
"type": "column",
"value": "p_partkey"
},
{
"id": 0,
"type": "table",
"value": "part"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
15,
16
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.