qid stringlengths 5 5 | task_type stringclasses 1
value | has_intended_resolution bool 1
class | db stringclasses 6
values | question stringlengths 33 189 | gold_ambiguity_points listlengths 1 5 | gold_queries listlengths 1 32 | gold_intended_query_id stringlengths 4 20 |
|---|---|---|---|---|---|---|---|
048-3 | ambig | true | github_repos | Count the number of repository IDs in YEAR_2023 that have public events and also received a large number of pull requests. | [
{
"id": "A",
"phrase": "public events",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"events with attribute public=1",
"events of type 'PublicEvent'"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "received a larg... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [
"pr_count_threshold"
],
"parameter_values": {
"pr_count_threshold": 12
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-A.0-B.1 |
077-2 | ambig | true | codebase_community | Find the top 10 users that have authored the most content on the platform. | [
{
"id": "A",
"phrase": "content",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"posts",
"posts and comments",
"posts, comments and edits"
],
"intended_interpretation_idx": 2
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"UserId": 805,
"DisplayName": "Glen_b",
... | GQRY-A.2 |
076-2 | ambig | true | codebase_community | How many users have received mostly positive scores for posts they contributed? | [
{
"id": "A",
"phrase": "mostly",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "percentage_threshold",
"parameter_dtype": "float",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
0.8
],
"intended_para... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"percentage_threshold"
],
"parameter_values": {
"percentage_threshold": 0.8
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.0-C.1 |
095-1 | ambig | true | student_club | List all events attended by members in Albany County in the Autumn season. | [
{
"id": "A",
"phrase": "Albany County",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Albany county, New York",
"Albany county, Wyoming"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "the Autumn season",
"type": "f... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"event_id": "rec2N69DMcrqN9PJC",
"event_name": "Wo... | GQRY-A.0-B.0 |
073-0 | ambig | true | codebase_community | Display all comments in posts tagged with 'neural-networks' created in 2010. | [
{
"id": "A",
"phrase": "created in 2010",
"type": "finite",
"ambiguity_type": "syntactic_table",
"interpretations": [
"posts created in 2010",
"comments created in 2010"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": "SELECT DISTINCT c.Id AS CommentId, c.Text AS CommentText\nFROM comments c\nJOIN posts p ON c.PostId = p.Id\nWHERE p.Tags LIKE '%<neural-networks>%'\nAND strftime('%Y', p.CreaionDate) = '2010';",
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"... | GQRY-A.0 |
007-5 | ambig | true | retails | Count total number of distinct parts shipped in Q4 1996 and 1997 from orders with high order priority. | [
{
"id": "A",
"phrase": "Count total number of distinct parts shipped in Q4 1996 and 1997",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"count all distinct part in the specified period",
"count total parts in Q4 1996 and count total parts in Q4 19... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_distinct_parts": 120499
}
],
... | GQRY-A.0-B.0-C.0 |
099-2 | ambig | true | student_club | For each event label, show the total amount spent by events with food and gifts. Return the result in (label, total_amount) format. | [
{
"id": "A",
"phrase": "event label",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"event type (event.type)",
"event status (event.status)"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "total amount spent",
"type... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"event_status": "Closed",
"total_spent": 1562.... | GQRY-A.0-B.1-C.0 |
017-0 | ambig | true | retails | Find the customer with the largest order value in each market segment. | [
{
"id": "A",
"phrase": "largest order value",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"largest single order value",
"largest total order value"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"market_segment": "AUTOMOBILE",
"customer_key": 92176,... | GQRY-A.1 |
020-3 | ambig | true | retails | List large parts that haven't been shipped to Middle East countries. Only includes parts with ID <= 1000. | [
{
"id": "A",
"phrase": "large parts",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"p_size is high",
"p_type starts with 'LARGE'"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "large parts",
"type": "infinite",
... | [
{
"id": "GQRY-A.0-C.0",
"query": null,
"parameter_names": [
"size_threshold"
],
"parameter_values": {
"size_threshold": 25
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_p... | GQRY-A.1-C.1 |
076-0 | ambig | true | codebase_community | How many users have received mostly positive scores for posts they contributed? | [
{
"id": "A",
"phrase": "mostly",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "percentage_threshold",
"parameter_dtype": "float",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
0.8
],
"intended_para... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"percentage_threshold"
],
"parameter_values": {
"percentage_threshold": 0.8
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.1-C.1 |
093-0 | ambig | true | student_club | Show the number of members for each region in the Morgan County . | [
{
"id": "A",
"phrase": "region",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"zip code",
"city"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "Morgan County",
"type": "finite",
"ambiguity_type": "semantic_val... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"zip_code": 25411,
"member_count": 0
}... | GQRY-A.0-B.8 |
070-2 | ambig | true | financial | What is the total number of weekly-fee accounts in districts with many large municipalities? | [
{
"id": "A",
"phrase": "many",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "municipality_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
8
],
"intended_paramete... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"municipality_threshold"
],
"parameter_values": {
"municipality_threshold": 8
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.0 |
026-3 | ambig | true | professional_basketball | For each team that Marcus Williams played for, compute his aggregate field goal percentage. | [
{
"id": "A",
"phrase": "Marcus Williams",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Marcus Williams from University of Connecticut born in 1985",
"Marcus Williams from University of Arizona born in 1986"
],
"intended_interpretation_idx": 1
... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": "SELECT\nt.tmID,\nt.name,\nCASE\nWHEN SUM(pt.fgAttempted) = 0 THEN 0\nELSE CAST(SUM(pt.fgMade) AS REAL) / SUM(pt.fgAttempted)\nEND as field_goal_percentage\nFROM players_teams pt\nJOIN players p ON pt.playerID = p.playerID\nJOIN teams t ON pt.tmID = t.tmID AND pt.year = ... | GQRY-A.1-B.1-C.2 |
085-0 | ambig | true | codebase_community | For each location, list the number of users with high scores for created posts. Only includes locations starting with letter 'a' or 'A'. | [
{
"id": "A",
"phrase": "high scores for created posts",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"high average score for created posts",
"high maximum score for created posts"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [
"score_threshold"
],
"parameter_values": {
"score_threshold": 3
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"Locati... | GQRY-A.1 |
045-4 | ambig | true | github_repos | What is the highest number of issues opened by a single repository (identified by repo ID) in Q3? Use the YEAR_* tables. | [
{
"id": "A",
"phrase": "issues opened",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"issues created",
"issues created or reopened"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "Q3",
"type": "finite",
"ambigui... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"MAX(issues_opened)": 67
}
],
"n... | GQRY-A.1-B.1 |
027-2 | ambig | true | professional_basketball | List all teams with low points allowed from the Western conference that have made the playoffs. | [
{
"id": "A",
"phrase": "low points allowed",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"low total points allowed",
"low points allowed per season",
"low points allowed per game"
],
"intended_interpretation_idx": 2
},
{
"id"... | [
{
"id": "GQRY-A.0-C.0",
"query": "SELECT t1.tmID, t1.name, SUM(t1.d_pts) AS total_points_allowed\nFROM teams t1\nWHERE t1.tmID IN (\nSELECT DISTINCT tmID\nFROM teams\nWHERE confID = 'WC' AND playoff IS NOT NULL\n)\nGROUP BY t1.tmID, t1.name\nHAVING SUM(t1.d_pts) < :points_allowed_threshold\nORDER BY SUM(t1.... | GQRY-A.2-C.1 |
006-1 | ambig | true | retails | Count number of suppliers with negative balance or located in Africa and do not supply Brand#32. | [
{
"id": "A",
"phrase": "with negative balance or located in Africa and do not supply Brand#32",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"suppliers (with negative balance) OR (located in Africa AND do not supply Brand#32)",
"suppliers (with ne... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"COUNT(DISTINCT s.s_suppkey)": 998
}
],
... | GQRY-A.0 |
026-6 | ambig | true | professional_basketball | For each team that Marcus Williams played for, compute his aggregate field goal percentage. | [
{
"id": "A",
"phrase": "Marcus Williams",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Marcus Williams from University of Connecticut born in 1985",
"Marcus Williams from University of Arizona born in 1986"
],
"intended_interpretation_idx": 1
... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": "SELECT\nt.tmID,\nt.name,\nCASE\nWHEN SUM(pt.fgAttempted) = 0 THEN 0\nELSE CAST(SUM(pt.fgMade) AS REAL) / SUM(pt.fgAttempted)\nEND as field_goal_percentage\nFROM players_teams pt\nJOIN players p ON pt.playerID = p.playerID\nJOIN teams t ON pt.tmID = t.tmID AND pt.year = ... | GQRY-A.1-B.1-C.1 |
083-2 | ambig | true | codebase_community | For each tag with many occurences (tags.Count), find the post with the highest score and its last update timestamp and reply count. | [
{
"id": "A",
"phrase": "many occurences",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
500
],
"intended_pa... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"count_threshold"
],
"parameter_values": {
"count_threshold": 500
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"... | GQRY-B.1-C.2 |
029-0 | ambig | true | professional_basketball | What is Charles Smith's best true shooting percentage in a season in his career? | [
{
"id": "A",
"phrase": "Charles Smith",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Charles Smith from University of Pittsburgh born on July 16, 1965",
"Charles Smith from Georgetown University born on November 29, 1967",
"Charles Smith from Marq... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"year": 1989,
"true_shooting_percentage": 0.588896... | GQRY-A.3-B.0 |
097-3 | ambig | true | student_club | Find the total funds allocated for food and advertisement for all events attended by members in New York. | [
{
"id": "A",
"phrase": "total funds allocated for food and advertisement",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"total funds for food and advertisement (single number)",
"total funds for food and total funds for advertisement (two separate... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_funds": 1795
}
],
"num_ro... | GQRY-A.0-B.2 |
089-0 | ambig | true | student_club | List all events with large attendance and also attended by members in Snohomish, show the number of attendees for each event. | [
{
"id": "A",
"phrase": "large attendance",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "attendance_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
2
],
"intende... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"attendance_threshold"
],
"parameter_values": {
"attendance_threshold": 2
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.0-C.1 |
070-1 | ambig | true | financial | What is the total number of weekly-fee accounts in districts with many large municipalities? | [
{
"id": "A",
"phrase": "many",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "municipality_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
8
],
"intended_paramete... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"municipality_threshold"
],
"parameter_values": {
"municipality_threshold": 8
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.2 |
025-1 | ambig | true | retails | For each manufacturer, find top N products with highest revenue from sales in America. Return the manufacturer, product, and revenue. | [
{
"id": "A",
"phrase": "top N",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "top_n",
"parameter_dtype": "int",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
3
],
"intended_parameter_operator": "<=... | [
{
"id": "GQRY-B.0-C.0-D.0",
"query": null,
"parameter_names": [
"top_n"
],
"parameter_values": {
"top_n": 3
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_mfgr": "Manufact... | GQRY-B.0-C.0-D.1 |
048-2 | ambig | true | github_repos | Count the number of repository IDs in YEAR_2023 that have public events and also received a large number of pull requests. | [
{
"id": "A",
"phrase": "public events",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"events with attribute public=1",
"events of type 'PublicEvent'"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "received a larg... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [
"pr_count_threshold"
],
"parameter_values": {
"pr_count_threshold": 12
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-A.0-B.0 |
047-1 | ambig | true | github_repos | Count the number of forks in public repositories in March. | [
{
"id": "A",
"phrase": "March",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"March 2022",
"March 2023",
"March in both 2022 and 2023"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"COUNT(*)": 63
}
],
"num_rows": 1
... | GQRY-A.0 |
008-0 | ambig | true | retails | Identify the manufacturer that contributed the highest profit during the last quarter of 1994. Show the manufacturer and the profit. | [
{
"id": "A",
"phrase": "highest profit",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"include returned items",
"exclude returned items"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "during the last quarter of 1... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_mfgr": "Manufacturer#3",
"profit": 1098055932.9... | GQRY-A.1-B.1 |
057-0 | ambig | true | github_repos | Count the total number of actor IDs who pushed commits or created pull requests and created Wiki pages on Febuary 20, 2022. | [
{
"id": "A",
"phrase": "pushed commits or created pull requests and created Wiki pages",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"(pushed commits) OR (created pull requests AND created Wiki pages)",
"(pushed commits OR created pull requests) ... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_actors": 67
}
],
"num_rows": ... | GQRY-A.0 |
022-2 | ambig | true | retails | Show the customer with the highest account balance for each area. | [
{
"id": "A",
"phrase": "area",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"nation",
"region",
"market segment"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"n_name": "ALGERIA",
"c_custkey": 122154,
... | GQRY-A.1 |
038-0 | ambig | true | github_repos | Show all programming languages and their total usage in GITHUB_REPOS_LANGUAGES. | [
{
"id": "A",
"phrase": "total usage",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"total number of repositories",
"total number of bytes"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"language": "Shell",
"repo_count": 23
},
... | GQRY-A.0 |
018-1 | ambig | true | retails | Count the number of customers that have used air shipping. | [
{
"id": "A",
"phrase": "air shipping",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"ship mode is AIR",
"ship mode is AIR or REG AIR"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"COUNT(DISTINCT c.c_custkey)": 88988
}
],
... | GQRY-A.0 |
026-2 | ambig | true | professional_basketball | For each team that Marcus Williams played for, compute his aggregate field goal percentage. | [
{
"id": "A",
"phrase": "Marcus Williams",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Marcus Williams from University of Connecticut born in 1985",
"Marcus Williams from University of Arizona born in 1986"
],
"intended_interpretation_idx": 0
... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": "SELECT\nt.tmID,\nt.name,\nCASE\nWHEN SUM(pt.fgAttempted) = 0 THEN 0\nELSE CAST(SUM(pt.fgMade) AS REAL) / SUM(pt.fgAttempted)\nEND as field_goal_percentage\nFROM players_teams pt\nJOIN players p ON pt.playerID = p.playerID\nJOIN teams t ON pt.tmID = t.tmID AND pt.year = ... | GQRY-A.0-B.1-C.2 |
054-1 | ambig | true | github_repos | For each repository using C++ and Python in GITHUB_REPOS_LANGUAGES that has many watchers, compare the amount of C++ and Python. Return the result as (repo_name, C++ amount, Python amount). | [
{
"id": "A",
"phrase": "repository using C++ and Python",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"repository using both C++ and Python ('and' means logical AND)",
"repository using either C++ or Python ('and' means UNION)"
],
"intende... | [
{
"id": "GQRY-A.0-C.0",
"query": null,
"parameter_names": [
"watcher_threshold"
],
"parameter_values": {
"watcher_threshold": 100
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-A.0-C.2 |
007-6 | ambig | true | retails | Count total number of distinct parts shipped in Q4 1996 and 1997 from orders with high order priority. | [
{
"id": "A",
"phrase": "Count total number of distinct parts shipped in Q4 1996 and 1997",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"count all distinct part in the specified period",
"count total parts in Q4 1996 and count total parts in Q4 19... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_distinct_parts": 120499
}
],
... | GQRY-A.1-B.1-C.0 |
004-2 | ambig | true | retails | Find all customers with high account balances and list their number of priority orders. | [
{
"id": "A",
"phrase": "high account balances",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "account_balance_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
9990
],... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"account_balance_threshold"
],
"parameter_values": {
"account_balance_threshold": 9990
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
... | GQRY-B.2 |
039-1 | ambig | true | github_repos | Show the top 20 user names that contribute most events in January 2023 and 2022. | [
{
"id": "A",
"phrase": "January 2023 and 2022",
"type": "finite",
"ambiguity_type": "syntactic_table",
"interpretations": [
"January 2023 and January 2022",
"January 2023 and the entire 2022"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"user": "LombiqBot",
"event_count": 23522
... | GQRY-A.1 |
005-2 | ambig | true | retails | Which part has the highest price? | [
{
"id": "A",
"phrase": "highest price",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"highest absolute maximum price",
"highest average price (aggregated over suppliers and orders)"
],
"intended_interpretation_idx": 1
},
{
"id": "B"... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_partkey": 27602,
"p_name": "powder tan cornsilk... | GQRY-A.1-B.0 |
005-1 | ambig | true | retails | Which part has the highest price? | [
{
"id": "A",
"phrase": "highest price",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"highest absolute maximum price",
"highest average price (aggregated over suppliers and orders)"
],
"intended_interpretation_idx": 0
},
{
"id": "B"... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_partkey": 27602,
"p_name": "powder tan cornsilk... | GQRY-A.0-B.1 |
042-3 | ambig | true | github_repos | Count the total number of Wiki pages updated in the last month of 2022 and 2023. | [
{
"id": "A",
"phrase": "Wiki pages updated",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Wiki pages created",
"Wiki pages edited",
"Wiki pages created or edited"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "l... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_wiki_pages_updated": 0
}
],
... | GQRY-A.0-B.0 |
069-1 | ambig | true | financial | For each account frequency category, show the most recent activity date. | [
{
"id": "A",
"phrase": "activity date",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"account creation date",
"card issued date",
"loan date",
"transaction date",
"any of account, loan, card, or transaction dates"
],
"intended_i... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"frequency": "POPLATEK MESICNE",
"most_recent_status_u... | GQRY-A.3 |
019-0 | ambig | true | retails | For each manufacturer, count the number of parts with over 2000 retail price or size 40. | [
{
"id": "A",
"phrase": "parts with over 2000 retail price or size 40",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"parts with over (2000 retail price or size 40)",
"parts with (over 2000 retail price) or (size 40)"
],
"intended_interpret... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_mfgr": "Manufacturer#1",
"SUM(CASE WHEN p_retailpri... | GQRY-A.0 |
025-2 | ambig | true | retails | For each manufacturer, find top N products with highest revenue from sales in America. Return the manufacturer, product, and revenue. | [
{
"id": "A",
"phrase": "top N",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "top_n",
"parameter_dtype": "int",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
3
],
"intended_parameter_operator": "<=... | [
{
"id": "GQRY-B.0-C.0-D.0",
"query": null,
"parameter_names": [
"top_n"
],
"parameter_values": {
"top_n": 3
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_mfgr": "Manufact... | GQRY-B.1-C.1-D.1 |
058-1 | ambig | true | financial | Compute the amount of deposits from December 1997 to end of 1998. | [
{
"id": "A",
"phrase": "amount",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"the number of deposit transactions",
"the total monetary value of deposits"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "deposits",... | [
{
"id": "GQRY-A.0-B.0",
"query": "SELECT COUNT(*)\nFROM trans\nWHERE operation = 'VKLAD'\nAND (date >= '1997-12-01' AND date < '1999-01-01');",
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sam... | GQRY-A.1-B.1 |
054-6 | ambig | true | github_repos | For each repository using C++ and Python in GITHUB_REPOS_LANGUAGES that has many watchers, compare the amount of C++ and Python. Return the result as (repo_name, C++ amount, Python amount). | [
{
"id": "A",
"phrase": "repository using C++ and Python",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"repository using both C++ and Python ('and' means logical AND)",
"repository using either C++ or Python ('and' means UNION)"
],
"intende... | [
{
"id": "GQRY-A.0-C.0",
"query": null,
"parameter_names": [
"watcher_threshold"
],
"parameter_values": {
"watcher_threshold": 100
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-A.1-C.1 |
001-4 | ambig | true | retails | Report the total revenue for each nation in 1995. | [
{
"id": "A",
"phrase": "total revenue",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"before discount (Gross revenue)",
"after discount (Net revenue)"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "total revenue"... | [
{
"id": "GQRY-A.0-B.0-C.0-D.0",
"query": "WITH revenue AS (\nSELECT n.n_nationkey,\nn.n_name,\nCOALESCE(SUM(l.l_extendedprice), 0) AS total_revenue\nFROM lineitem l\nJOIN orders o ON o.o_orderkey = l.l_orderkey\nJOIN customer c ON o.o_custkey = c.c_custkey\nJOIN nation n ON c.c_nationkey = n.n_nationkey\nWH... | GQRY-A.1-B.1-C.1-D.3 |
040-0 | ambig | true | github_repos | Find all repository IDs that experienced rapid increase in push based on absolute change from 2022 to 2023. | [
{
"id": "A",
"phrase": "rapid increase in push",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "push_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
70
],
"intend... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"push_threshold"
],
"parameter_values": {
"push_threshold": 70
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"repo_id": 5... | GQRY |
025-6 | ambig | true | retails | For each manufacturer, find top N products with highest revenue from sales in America. Return the manufacturer, product, and revenue. | [
{
"id": "A",
"phrase": "top N",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "top_n",
"parameter_dtype": "int",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
3
],
"intended_parameter_operator": "<=... | [
{
"id": "GQRY-B.0-C.0-D.0",
"query": null,
"parameter_names": [
"top_n"
],
"parameter_values": {
"top_n": 3
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_mfgr": "Manufact... | GQRY-B.1-C.0-D.1 |
077-0 | ambig | true | codebase_community | Find the top 10 users that have authored the most content on the platform. | [
{
"id": "A",
"phrase": "content",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"posts",
"posts and comments",
"posts, comments and edits"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"UserId": 805,
"DisplayName": "Glen_b",
... | GQRY-A.0 |
052-0 | ambig | true | github_repos | List all repositories in YEAR_2023, and for each, return its name and the source (identified by name) that contribute most events to it. | [
{
"id": "A",
"phrase": "source",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"user",
"organization"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"repo_name": "0xSpaceShard/starknet-devnet",
"source":... | GQRY-A.1 |
043-2 | ambig | true | github_repos | Show the repository name with most events in May. | [
{
"id": "A",
"phrase": "May",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"May 2022",
"May 2023",
"May in both 2022 and 2023"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"repo_name": "Lombiq/Orchard",
"event_count": 7833
... | GQRY-A.1 |
075-1 | ambig | true | codebase_community | List all users in Tokyo and show total views for each of them. | [
{
"id": "A",
"phrase": "total views",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"total profile views",
"total views for authored posts",
"total views for authored or edited posts"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"UserId": 83,
"DisplayName": "c4il",
"To... | GQRY-A.1 |
028-1 | ambig | true | professional_basketball | For each player and each team they played for in the 2007 season, show the post-season playoff status and games played. | [
{
"id": "A",
"phrase": "post-season playoff status and games played",
"type": "finite",
"ambiguity_type": "syntactic_column",
"interpretations": [
"post-season playoff status and post-season games played",
"post-season playoff status and all games played"
],
"intended_interpr... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"playerID": "abdursh01",
"firstName": "Julius",
... | GQRY-A.0 |
080-0 | ambig | true | codebase_community | Retrieve comments with only a few words posted during September 2012. | [
{
"id": "A",
"phrase": "a few words",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "word_count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
4
],
"intended_par... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"word_count_threshold"
],
"parameter_values": {
"word_count_threshold": 4
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"... | GQRY |
014-1 | ambig | true | retails | Show the supplier that supplies the most parts in their inventory in each region. | [
{
"id": "A",
"phrase": "supplies the most parts",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"supplies the largest number of distinct parts",
"supplies the largest total inventory quantity"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"region_key": 0,
"region_name": "AFRICA",
... | GQRY-A.1 |
046-4 | ambig | true | github_repos | In the January 2023 table, find all repo names that were forked or had issues opened and have public events. | [
{
"id": "A",
"phrase": "forked or had issues opened and have public events",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"(were forked OR had issues opened) AND (have public events in January 2023)",
"(were forked) OR (had issues opened AND have ... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"repo_name": "unifyai/ivy"
},
{
... | GQRY-A.0-B.1-C.0 |
025-0 | ambig | true | retails | For each manufacturer, find top N products with highest revenue from sales in America. Return the manufacturer, product, and revenue. | [
{
"id": "A",
"phrase": "top N",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "top_n",
"parameter_dtype": "int",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
3
],
"intended_parameter_operator": "<=... | [
{
"id": "GQRY-B.0-C.0-D.0",
"query": null,
"parameter_names": [
"top_n"
],
"parameter_values": {
"top_n": 3
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_mfgr": "Manufact... | GQRY-B.0-C.0-D.0 |
007-1 | ambig | true | retails | Count total number of distinct parts shipped in Q4 1996 and 1997 from orders with high order priority. | [
{
"id": "A",
"phrase": "Count total number of distinct parts shipped in Q4 1996 and 1997",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"count all distinct part in the specified period",
"count total parts in Q4 1996 and count total parts in Q4 19... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_distinct_parts": 120499
}
],
... | GQRY-A.1-B.0-C.1 |
041-3 | ambig | true | github_repos | Use the GITHUB_REPOS_* tables to count the total number of sample files in projects with Python and over 10 watchers. | [
{
"id": "A",
"phrase": "projects with Python",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"repos with language='Python' in GITHUB_REPOS_LANGUAGES",
"repos with .py files in GITHUB_REPOS_SAMPLE_FILES",
"repos with .py files in GITHUB_REPOS_SAMPLE_... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"file_count": 126
}
],
"num_rows... | GQRY-A.1-B.2 |
011-2 | ambig | true | retails | Find the lowest unit price for each part that has suppliers from Arab countries. Only include parts with ID from 1 to 20. | [
{
"id": "A",
"phrase": "unit price",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"unit supply cost",
"listed retail unit price",
"final transaction unit price"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"partkey": 2,
"part_name": "hot spring dodger dim ligh... | GQRY-A.1 |
041-2 | ambig | true | github_repos | Use the GITHUB_REPOS_* tables to count the total number of sample files in projects with Python and over 10 watchers. | [
{
"id": "A",
"phrase": "projects with Python",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"repos with language='Python' in GITHUB_REPOS_LANGUAGES",
"repos with .py files in GITHUB_REPOS_SAMPLE_FILES",
"repos with .py files in GITHUB_REPOS_SAMPLE_... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"file_count": 126
}
],
"num_rows... | GQRY-A.2-B.0 |
089-2 | ambig | true | student_club | List all events with large attendance and also attended by members in Snohomish, show the number of attendees for each event. | [
{
"id": "A",
"phrase": "large attendance",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "attendance_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
2
],
"intende... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"attendance_threshold"
],
"parameter_values": {
"attendance_threshold": 2
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.0-C.0 |
001-0 | ambig | true | retails | Report the total revenue for each nation in 1995. | [
{
"id": "A",
"phrase": "total revenue",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"before discount (Gross revenue)",
"after discount (Net revenue)"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "total revenue"... | [
{
"id": "GQRY-A.0-B.0-C.0-D.0",
"query": "WITH revenue AS (\nSELECT n.n_nationkey,\nn.n_name,\nCOALESCE(SUM(l.l_extendedprice), 0) AS total_revenue\nFROM lineitem l\nJOIN orders o ON o.o_orderkey = l.l_orderkey\nJOIN customer c ON o.o_custkey = c.c_custkey\nJOIN nation n ON c.c_nationkey = n.n_nationkey\nWH... | GQRY-A.0-B.0-C.1-D.3 |
083-5 | ambig | true | codebase_community | For each tag with many occurences (tags.Count), find the post with the highest score and its last update timestamp and reply count. | [
{
"id": "A",
"phrase": "many occurences",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
500
],
"intended_pa... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"count_threshold"
],
"parameter_values": {
"count_threshold": 500
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"... | GQRY-B.1-C.0 |
054-5 | ambig | true | github_repos | For each repository using C++ and Python in GITHUB_REPOS_LANGUAGES that has many watchers, compare the amount of C++ and Python. Return the result as (repo_name, C++ amount, Python amount). | [
{
"id": "A",
"phrase": "repository using C++ and Python",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"repository using both C++ and Python ('and' means logical AND)",
"repository using either C++ or Python ('and' means UNION)"
],
"intende... | [
{
"id": "GQRY-A.0-C.0",
"query": null,
"parameter_names": [
"watcher_threshold"
],
"parameter_values": {
"watcher_threshold": 100
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-A.1-C.2 |
099-0 | ambig | true | student_club | For each event label, show the total amount spent by events with food and gifts. Return the result in (label, total_amount) format. | [
{
"id": "A",
"phrase": "event label",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"event type (event.type)",
"event status (event.status)"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "total amount spent",
"type... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"event_status": "Closed",
"total_spent": 1562.... | GQRY-A.0-B.0-C.1 |
024-1 | ambig | true | retails | Show large orders placed in February 1996. | [
{
"id": "A",
"phrase": "large orders",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"orders with high total price",
"orders with large number of line items",
"orders with large total quantity of parts"
],
"intended_interpretation_idx"... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [
"large_threshold"
],
"parameter_values": {
"large_threshold": 250
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"o_or... | GQRY-A.2 |
055-1 | ambig | true | github_repos | Find emails of all contributors in the sample commits. | [
{
"id": "A",
"phrase": "contributors",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"commit author",
"committer",
"both commit author and committer"
],
"intended_interpretation_idx": 2
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"email": "4a57785b02e7b752fc2eb7d1c97db94302a41bf7@users.noreply.git... | GQRY-A.2 |
026-5 | ambig | true | professional_basketball | For each team that Marcus Williams played for, compute his aggregate field goal percentage. | [
{
"id": "A",
"phrase": "Marcus Williams",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Marcus Williams from University of Connecticut born in 1985",
"Marcus Williams from University of Arizona born in 1986"
],
"intended_interpretation_idx": 1
... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": "SELECT\nt.tmID,\nt.name,\nCASE\nWHEN SUM(pt.fgAttempted) = 0 THEN 0\nELSE CAST(SUM(pt.fgMade) AS REAL) / SUM(pt.fgAttempted)\nEND as field_goal_percentage\nFROM players_teams pt\nJOIN players p ON pt.playerID = p.playerID\nJOIN teams t ON pt.tmID = t.tmID AND pt.year = ... | GQRY-A.1-B.0-C.2 |
067-1 | ambig | true | financial | For each account in the Brno district, list the average and maximum amount along with the latest balance. | [
{
"id": "A",
"phrase": "the Brno district",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Brno - mesto (the urban district of Brno)",
"Brno - venkov (the countryside district of Brno)"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"account_id": 10,
"avg_amount": 6670.608974359,
... | GQRY-A.0-B.2 |
084-1 | ambig | true | codebase_community | List the posts with negative comments authored by MYaseen208. | [
{
"id": "A",
"phrase": "authored by MYaseen208",
"type": "finite",
"ambiguity_type": "syntactic_table",
"interpretations": [
"comments authored by MYaseen208",
"posts authored by MYaseen208"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"Id": 8787,
"Title": "Generalized linear latent and mi... | GQRY-A.1 |
007-3 | ambig | true | retails | Count total number of distinct parts shipped in Q4 1996 and 1997 from orders with high order priority. | [
{
"id": "A",
"phrase": "Count total number of distinct parts shipped in Q4 1996 and 1997",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"count all distinct part in the specified period",
"count total parts in Q4 1996 and count total parts in Q4 19... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_distinct_parts": 120499
}
],
... | GQRY-A.1-B.1-C.1 |
001-2 | ambig | true | retails | Report the total revenue for each nation in 1995. | [
{
"id": "A",
"phrase": "total revenue",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"before discount (Gross revenue)",
"after discount (Net revenue)"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "total revenue"... | [
{
"id": "GQRY-A.0-B.0-C.0-D.0",
"query": "WITH revenue AS (\nSELECT n.n_nationkey,\nn.n_name,\nCOALESCE(SUM(l.l_extendedprice), 0) AS total_revenue\nFROM lineitem l\nJOIN orders o ON o.o_orderkey = l.l_orderkey\nJOIN customer c ON o.o_custkey = c.c_custkey\nJOIN nation n ON c.c_nationkey = n.n_nationkey\nWH... | GQRY-A.1-B.0-C.1-D.3 |
044-2 | ambig | true | github_repos | Find the most active organization in 2023. | [
{
"id": "A",
"phrase": "most active organization",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"organization with the highest total number of events",
"organization with the highest number of repositories that generated events",
"organizatio... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"org_id": 8158177,
"event_count": 42493
}
... | GQRY-A.2 |
020-1 | ambig | true | retails | List large parts that haven't been shipped to Middle East countries. Only includes parts with ID <= 1000. | [
{
"id": "A",
"phrase": "large parts",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"p_size is high",
"p_type starts with 'LARGE'"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
"phrase": "large parts",
"type": "infinite",
... | [
{
"id": "GQRY-A.0-C.0",
"query": null,
"parameter_names": [
"size_threshold"
],
"parameter_values": {
"size_threshold": 25
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"p_p... | GQRY-A.0-C.0 |
091-0 | ambig | true | student_club | List all meetings held in early morning. | [
{
"id": "A",
"phrase": "early morning",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "time_threshold",
"parameter_dtype": "str",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
"09:45:00"
],
"intende... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"time_threshold"
],
"parameter_values": {
"time_threshold": "09:45:00"
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"eve... | GQRY |
085-1 | ambig | true | codebase_community | For each location, list the number of users with high scores for created posts. Only includes locations starting with letter 'a' or 'A'. | [
{
"id": "A",
"phrase": "high scores for created posts",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"high average score for created posts",
"high maximum score for created posts"
],
"intended_interpretation_idx": 0
},
{
"id": "B",
... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [
"score_threshold"
],
"parameter_values": {
"score_threshold": 3
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"Locati... | GQRY-A.0 |
072-2 | ambig | true | codebase_community | Find posts with many related posts. | [
{
"id": "A",
"phrase": "many",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
10
],
"intended_parameter_oper... | [
{
"id": "GQRY-B.0",
"query": "SELECT p.Id, p.Title, COUNT(DISTINCT pl.RelatedPostId) AS RelatedCount\nFROM posts p\nJOIN postLinks pl ON p.Id = pl.PostId\nGROUP BY p.Id, p.Title\nHAVING COUNT(DISTINCT pl.RelatedPostId) > :count_threshold\nORDER BY RelatedCount DESC;",
"parameter_names": [
"count_t... | GQRY-B.1 |
074-0 | ambig | true | codebase_community | List users who have received a significant number of badges from July 20 to September 20 in 2010. | [
{
"id": "A",
"phrase": "significant number",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "badge_count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
12
],
"int... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"badge_count_threshold"
],
"parameter_values": {
"badge_count_threshold": 12
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY |
088-1 | ambig | true | student_club | For each region, report the total club funding received by members. | [
{
"id": "A",
"phrase": "region",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"zip code",
"city",
"county",
"state"
],
"intended_interpretation_idx": 3
}
] | [
{
"id": "GQRY-A.0",
"query": "SELECT z.zip_code, COALESCE(SUM(i.amount), 0) AS total_income\nFROM zip_code z\nLEFT JOIN member m ON m.zip = z.zip_code\nLEFT JOIN income i ON i.link_to_member = m.member_id\nGROUP BY z.zip_code\nORDER BY z.zip_code;",
"parameter_names": [],
"parameter_values": {},
... | GQRY-A.3 |
067-0 | ambig | true | financial | For each account in the Brno district, list the average and maximum amount along with the latest balance. | [
{
"id": "A",
"phrase": "the Brno district",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Brno - mesto (the urban district of Brno)",
"Brno - venkov (the countryside district of Brno)"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"account_id": 10,
"avg_amount": 6670.608974359,
... | GQRY-A.1-B.2 |
046-5 | ambig | true | github_repos | In the January 2023 table, find all repo names that were forked or had issues opened and have public events. | [
{
"id": "A",
"phrase": "forked or had issues opened and have public events",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"(were forked OR had issues opened) AND (have public events in January 2023)",
"(were forked) OR (had issues opened AND have ... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"repo_name": "unifyai/ivy"
},
{
... | GQRY-A.1-B.0-C.1 |
032-1 | ambig | true | professional_basketball | List tall players that have played for NBA teams from Los Angeles. | [
{
"id": "A",
"phrase": "tall players",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "height_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
84
],
"intended_param... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"height_threshold"
],
"parameter_values": {
"height_threshold": 84
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"pla... | GQRY-B.0 |
078-0 | ambig | true | codebase_community | List all users who created a lot of posts in one month. | [
{
"id": "A",
"phrase": "a lot of posts",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "post_count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
60
],
"intended... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"post_count_threshold"
],
"parameter_values": {
"post_count_threshold": 60
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY |
098-0 | ambig | true | student_club | For each event held at the end of the week that has attendance, find the major with the most members attending. | [
{
"id": "A",
"phrase": "at the end of the week",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"Friday (end of the work week)",
"Saturday (end of the calendar week)",
"Sunday (end of the calendar week)"
],
"intended_interpretation_idx": 1
... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [],
"num_rows": 0
},
"parquet_base64": "UEFSMRUEFQAVAkwVABUAEgAAABUEFQAV... | GQRY-A.1 |
049-0 | ambig | true | github_repos | List the active PR contributors in 2022. | [
{
"id": "A",
"phrase": "active",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "pr_count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
100
],
"intended_paramete... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"pr_count_threshold"
],
"parameter_values": {
"pr_count_threshold": 100
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.1 |
009-1 | ambig | true | retails | List the customer who made the highest number of purchases using rail shipping. | [
{
"id": "A",
"phrase": "number of purchases using rail shipping",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"number of orders containing at least one line item shipped by rail",
"number of line items shipped by rail",
"total quantity shipp... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"c_custkey": 63035,
"c_name": "Customer#000063035",
... | GQRY-A.2 |
007-2 | ambig | true | retails | Count total number of distinct parts shipped in Q4 1996 and 1997 from orders with high order priority. | [
{
"id": "A",
"phrase": "Count total number of distinct parts shipped in Q4 1996 and 1997",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"count all distinct part in the specified period",
"count total parts in Q4 1996 and count total parts in Q4 19... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"total_distinct_parts": 120499
}
],
... | GQRY-A.0-B.1-C.1 |
002-6 | ambig | true | retails | Count the number of suppliers that offer both air and rail shipping in America. | [
{
"id": "A",
"phrase": "suppliers that offer both air and rail shipping in America",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"suppliers that offer air and rail shipping to customers in America",
"suppliers in America that offer air and rail s... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": "WITH usa_suppliers_with_air AS (\nSELECT DISTINCT l.l_suppkey\nFROM lineitem l\nJOIN orders o ON l.l_orderkey = o.o_orderkey\nJOIN customer c ON o.o_custkey = c.c_custkey\nJOIN nation n ON c.c_nationkey = n.n_nationkey\nWHERE n.n_name = 'UNITED STATES'\nAND l.l_shipmode... | GQRY-A.1-B.1-C.1 |
087-1 | ambig | true | student_club | Which events had a low budget remaining or high spending on food or advertisement? | [
{
"id": "A",
"phrase": "low budget remaining",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "budget_remaining_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
20
],
... | [
{
"id": "GQRY-B.0",
"query": "WITH event_budget AS (\nSELECT\nlink_to_event AS event_id,\nSUM(remaining) AS total_remaining,\nSUM(CASE WHEN category = 'Food' THEN spent ELSE 0 END) AS food_spent,\nSUM(CASE WHEN category = 'Advertisement' THEN spent ELSE 0 END) AS adv_spent\nFROM budget\nGROUP BY link_to_eve... | GQRY-B.1 |
041-0 | ambig | true | github_repos | Use the GITHUB_REPOS_* tables to count the total number of sample files in projects with Python and over 10 watchers. | [
{
"id": "A",
"phrase": "projects with Python",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"repos with language='Python' in GITHUB_REPOS_LANGUAGES",
"repos with .py files in GITHUB_REPOS_SAMPLE_FILES",
"repos with .py files in GITHUB_REPOS_SAMPLE_... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"file_count": 126
}
],
"num_rows... | GQRY-A.2-B.2 |
041-4 | ambig | true | github_repos | Use the GITHUB_REPOS_* tables to count the total number of sample files in projects with Python and over 10 watchers. | [
{
"id": "A",
"phrase": "projects with Python",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"repos with language='Python' in GITHUB_REPOS_LANGUAGES",
"repos with .py files in GITHUB_REPOS_SAMPLE_FILES",
"repos with .py files in GITHUB_REPOS_SAMPLE_... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"file_count": 126
}
],
"num_rows... | GQRY-A.0-B.2 |
069-2 | ambig | true | financial | For each account frequency category, show the most recent activity date. | [
{
"id": "A",
"phrase": "activity date",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"account creation date",
"card issued date",
"loan date",
"transaction date",
"any of account, loan, card, or transaction dates"
],
"intended_i... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"frequency": "POPLATEK MESICNE",
"most_recent_status_u... | GQRY-A.0 |
071-1 | ambig | true | financial | Show the district name and average salary for each district with the lowest number of urban residents and entrepreneurs. | [
{
"id": "A",
"phrase": "each district with the lowest number of urban residents and entrepreneurs",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"(each district with the lowest number of urban residents) and (each district with the lowest number of entr... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"district_name": "Rokycany",
"average_salary": 8843,
... | GQRY-A.1 |
027-3 | ambig | true | professional_basketball | List all teams with low points allowed from the Western conference that have made the playoffs. | [
{
"id": "A",
"phrase": "low points allowed",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"low total points allowed",
"low points allowed per season",
"low points allowed per game"
],
"intended_interpretation_idx": 0
},
{
"id"... | [
{
"id": "GQRY-A.0-C.0",
"query": "SELECT t1.tmID, t1.name, SUM(t1.d_pts) AS total_points_allowed\nFROM teams t1\nWHERE t1.tmID IN (\nSELECT DISTINCT tmID\nFROM teams\nWHERE confID = 'WC' AND playoff IS NOT NULL\n)\nGROUP BY t1.tmID, t1.name\nHAVING SUM(t1.d_pts) < :points_allowed_threshold\nORDER BY SUM(t1.... | GQRY-A.0-C.1 |
083-3 | ambig | true | codebase_community | For each tag with many occurences (tags.Count), find the post with the highest score and its last update timestamp and reply count. | [
{
"id": "A",
"phrase": "many occurences",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "count_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
500
],
"intended_pa... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"count_threshold"
],
"parameter_values": {
"count_threshold": 500
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"... | GQRY-B.0-C.0 |
060-0 | ambig | true | financial | Count the number of young clients with significant loans and large transactions for each area. | [
{
"id": "A",
"phrase": "Count the number of young clients with significant loans and large transactions",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"count clients with either significant loans or large transactions ('and' means UNION)",
"count c... | [
{
"id": "GQRY-A.0-E.0",
"query": null,
"parameter_names": [
"loan_amount_threshold",
"birth_date_threshold",
"transaction_amount_threshold",
"birth_date_threshold"
],
"parameter_values": {
"loan_amount_threshold": 12000,
"birth_date_threshold": "1960-01-01",
... | GQRY-A.2-E.1 |
033-0 | ambig | true | professional_basketball | Find all William Smith who played forward and guard. | [
{
"id": "A",
"phrase": "played forward and guard",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"played both forward and guard ('and' means logical AND)",
"played either forward or guard ('and' means UNION)"
],
"intended_interpretation_idx"... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"playerID": "smithbi01",
"firstName": "William",
... | GQRY-A.1 |
046-2 | ambig | true | github_repos | In the January 2023 table, find all repo names that were forked or had issues opened and have public events. | [
{
"id": "A",
"phrase": "forked or had issues opened and have public events",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"(were forked OR had issues opened) AND (have public events in January 2023)",
"(were forked) OR (had issues opened AND have ... | [
{
"id": "GQRY-A.0-B.0-C.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"repo_name": "unifyai/ivy"
},
{
... | GQRY-A.1-B.1-C.1 |
059-1 | ambig | true | financial | Which clients have a card issued before 1999 and have made at least two orders for household or leasing payments? | [
{
"id": "A",
"phrase": "at least two orders for household or leasing payments",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"at least two orders in total for either household or leasing payments",
"at least two orders for household payments or at ... | [
{
"id": "GQRY-A.0",
"query": "WITH clients_with_orders AS (\nSELECT d2.client_id\nFROM \"order\" o\nJOIN disp d2 ON o.account_id = d2.account_id\nWHERE o.k_symbol IN ('SIPO', 'LEASING')\nGROUP BY d2.client_id\nHAVING COUNT(DISTINCT o.order_id) >= 2\n)\nSELECT cwo.client_id\nFROM clients_with_orders cwo\nJOI... | GQRY-A.1 |
062-0 | ambig | true | financial | For each bank in the database, show the total sum of household or insurance payments in 1997 and 1998, grouped accordingly. | [
{
"id": "A",
"phrase": "grouped accordingly",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"group by bank only",
"group by bank and payment type",
"group by bank and year",
"group by bank, payment type, year"
],
"intended_interp... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"bank": "AB",
"total_amount": 24060373
},
... | GQRY-A.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.