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 |
|---|---|---|---|---|---|---|---|
043-1 | 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": 2
}
] | [
{
"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.2 |
045-1 | 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.2 |
059-0 | 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.0 |
089-3 | 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.1-C.1 |
088-0 | 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": 1
}
] | [
{
"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.1 |
021-0 | ambig | true | retails | Find customers with high purchase quantities. | [
{
"id": "A",
"phrase": "high purchase quantities",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"large total quantities of individual parts",
"large number of line items",
"large number of orders"
],
"intended_interpretation_idx": 0
... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [
"quantity_threshold"
],
"parameter_values": {
"quantity_threshold": 2800
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-A.0 |
008-3 | 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": 0
},
{
"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.0-B.3 |
036-2 | ambig | true | github_repos | For each repository with a GPL license, count the number of merged PRs created in the period of 2022 and January 2023. | [
{
"id": "A",
"phrase": "GPL license",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"mainline GPL license (gpl-2.0 or gpl-3.0)",
"all GPL variants including AGPL (agpl-3.0) and LGPL (lgpl-2.1 or lgpl-3.0)"
],
"intended_interpretation_idx": 0
},
... | [
{
"id": "GQRY-A.0-B.0",
"query": "WITH gpl_repos AS (\nSELECT grl.repo_name, grl.license\nFROM GITHUB_REPOS_LICENSES grl\nWHERE grl.license IN ('gpl-2.0', 'gpl-3.0')\n),\nall_events AS (\nSELECT * FROM YEAR_2022\nUNION ALL\nSELECT * FROM MONTH_202301\n),\nmerged_prs AS (\nSELECT\njson_extract(ae.repo, '$.na... | GQRY-A.0-B.1 |
051-0 | ambig | true | github_repos | For each sample repo with many watchers, show the number of public events in the first month of 2023 and 2022, return the result in (repo_name, count) pairs. | [
{
"id": "A",
"phrase": "many watchers",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "watcher_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
160
],
"intended_pa... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"watcher_threshold"
],
"parameter_values": {
"watcher_threshold": 160
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.1-C.1 |
097-2 | 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.1 |
041-1 | 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.1 |
051-3 | ambig | true | github_repos | For each sample repo with many watchers, show the number of public events in the first month of 2023 and 2022, return the result in (repo_name, count) pairs. | [
{
"id": "A",
"phrase": "many watchers",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "watcher_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
160
],
"intended_pa... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"watcher_threshold"
],
"parameter_values": {
"watcher_threshold": 160
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.0-C.0 |
068-1 | ambig | true | financial | Find the total value sent to each bank. | [
{
"id": "A",
"phrase": "total value sent to each bank",
"type": "finite",
"ambiguity_type": "semantic_table",
"interpretations": [
"computed using order table",
"computed using trans table"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"bank_to": "AB",
"total_value_sent": 1707389.500000000... | GQRY-A.1 |
097-0 | 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.0 |
092-1 | ambig | true | student_club | Find the total funds allocated for speaker gifts and t-shirts for each event attended by members in Georgetown in South Carolina. | [
{
"id": "A",
"phrase": "total funds allocated for speaker gifts and t-shirts",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"total funds for speaker gifts and t-shirts (single number)",
"total funds for speaker gifts and total funds for t-shirts (... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"event_id": "recEVTik3MlqbvLFi",
"event_name": "Oc... | GQRY-A.1-B.0 |
045-3 | 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": 0
},
{
"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.0-B.0 |
016-1 | ambig | true | retails | Show the number of suppliers and customers from America. | [
{
"id": "A",
"phrase": "suppliers and customers from America",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"suppliers AND (customers from America)",
"(suppliers from America) AND (customers from America)"
],
"intended_interpretation_idx":... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"supplier_count": 10000,
"customer_count": 6100
... | GQRY-A.0-B.0 |
099-3 | 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": 1
},
{
"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.1-B.0-C.1 |
081-0 | ambig | true | codebase_community | Find users who have either Teacher or Student badges in 2011 and 2012. Only includes the users with ID up to 1000. | [
{
"id": "A",
"phrase": "in 2011 and 2012",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"in both 2011 and 2012.",
"in either 2011 or 2012."
],
"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": 501,
"DisplayName": "momeara"
},... | GQRY-A.1 |
030-1 | ambig | true | professional_basketball | Compute the total post-season offensive rebounds and games played in their career for each player born in Oklahoma. | [
{
"id": "A",
"phrase": "total post-season offensive rebounds and games played",
"type": "finite",
"ambiguity_type": "syntactic_column",
"interpretations": [
"total post-season offensive rebounds and post-season games played",
"total post-season offensive rebounds and all games played... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"playerID": "beaslch01",
"firstName": "Charles",
... | GQRY-A.0-B.0 |
025-4 | 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.1-D.1 |
065-0 | ambig | true | financial | List the districts with low unemployment in 1995 - 1996. | [
{
"id": "A",
"phrase": "low unemployment",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "unemployment_rate_threshold",
"parameter_dtype": "float",
"parameter_sample_operators": [
"<",
"<="
],
"parameter_sample_values": [
1.7000000000... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"unemployment_rate_threshold"
],
"parameter_values": {
"unemployment_rate_threshold": 1.7000000000000002
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_dat... | GQRY-B.2 |
002-2 | 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.0 |
066-1 | ambig | true | financial | Show the average age and number of male clients by region, assuming the current date is August 1st, 2025. | [
{
"id": "A",
"phrase": "average age and number of male clients",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"average age of all clients and number of male clients",
"average age of male clients and number of male clients"
],
"intended_in... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"region": "Prague",
"avg_age": 71.431372549,
... | GQRY-A.1 |
017-1 | 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": 0
}
] | [
{
"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.0 |
003-1 | ambig | true | retails | Which supplier has the largest inventory value? | [
{
"id": "A",
"phrase": "inventory value",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"value at supply cost",
"value at retail 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": [
{
"s_suppkey": 5704,
"s_name": "Supplier#000005704",
... | GQRY-A.1 |
061-0 | ambig | true | financial | Compute the number of accounts with a substantial percentage balance increase within a short period. | [
{
"id": "A",
"phrase": "substantial percentage balance increase",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "percentage_increase",
"parameter_dtype": "float",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"days_threshold",
"percentage_increase"
],
"parameter_values": {
"days_threshold": 7,
"percentage_increase": 0.5
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
... | GQRY |
060-4 | 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.0-E.1 |
067-4 | 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.1 |
026-1 | 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.0 |
054-3 | 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.0 |
016-3 | ambig | true | retails | Show the number of suppliers and customers from America. | [
{
"id": "A",
"phrase": "suppliers and customers from America",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"suppliers AND (customers from America)",
"(suppliers from America) AND (customers from America)"
],
"intended_interpretation_idx":... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"supplier_count": 10000,
"customer_count": 6100
... | GQRY-A.0-B.1 |
094-0 | ambig | true | student_club | For each event held in the afternoon compute the total remaining budget. | [
{
"id": "A",
"phrase": "in the afternoon",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "afternoon_start_time",
"parameter_dtype": "str",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
"12:30:00"
],
... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"afternoon_start_time",
"afternoon_end_time"
],
"parameter_values": {
"afternoon_start_time": "12:30:00",
"afternoon_end_time": "17:00:00"
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
... | GQRY |
092-0 | ambig | true | student_club | Find the total funds allocated for speaker gifts and t-shirts for each event attended by members in Georgetown in South Carolina. | [
{
"id": "A",
"phrase": "total funds allocated for speaker gifts and t-shirts",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"total funds for speaker gifts and t-shirts (single number)",
"total funds for speaker gifts and total funds for t-shirts (... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"event_id": "recEVTik3MlqbvLFi",
"event_name": "Oc... | GQRY-A.0-B.1 |
050-1 | ambig | true | github_repos | What is the monthly average number of events during the period of the last quarter of 2022 and 2023? | [
{
"id": "A",
"phrase": "last quarter of 2022 and 2023",
"type": "finite",
"ambiguity_type": "syntactic_table",
"interpretations": [
"last quarter of 2022 and last quarter of 2023",
"last quarter of 2022 and entire 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": [
{
"event_count": 28032
}
],
"num_rows"... | GQRY-A.1 |
022-1 | 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": 0
}
] | [
{
"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.0 |
029-1 | 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.2-B.0 |
036-3 | ambig | true | github_repos | For each repository with a GPL license, count the number of merged PRs created in the period of 2022 and January 2023. | [
{
"id": "A",
"phrase": "GPL license",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"mainline GPL license (gpl-2.0 or gpl-3.0)",
"all GPL variants including AGPL (agpl-3.0) and LGPL (lgpl-2.1 or lgpl-3.0)"
],
"intended_interpretation_idx": 0
},
... | [
{
"id": "GQRY-A.0-B.0",
"query": "WITH gpl_repos AS (\nSELECT grl.repo_name, grl.license\nFROM GITHUB_REPOS_LICENSES grl\nWHERE grl.license IN ('gpl-2.0', 'gpl-3.0')\n),\nall_events AS (\nSELECT * FROM YEAR_2022\nUNION ALL\nSELECT * FROM MONTH_202301\n),\nmerged_prs AS (\nSELECT\njson_extract(ae.repo, '$.na... | GQRY-A.0-B.0 |
083-1 | 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.2 |
019-1 | 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.1 |
058-0 | 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": 0
},
{
"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.0-B.0 |
043-0 | 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": 0
}
] | [
{
"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.0 |
011-0 | 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": 2
}
] | [
{
"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.2 |
083-4 | 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.1 |
047-2 | 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": 1
}
] | [
{
"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.1 |
004-3 | 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.1 |
051-1 | ambig | true | github_repos | For each sample repo with many watchers, show the number of public events in the first month of 2023 and 2022, return the result in (repo_name, count) pairs. | [
{
"id": "A",
"phrase": "many watchers",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "watcher_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
160
],
"intended_pa... | [
{
"id": "GQRY-B.0-C.0",
"query": null,
"parameter_names": [
"watcher_threshold"
],
"parameter_values": {
"watcher_threshold": 160
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.1-C.0 |
095-0 | 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.1 |
077-1 | 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": 1
}
] | [
{
"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.1 |
055-0 | 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": 0
}
] | [
{
"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.0 |
022-0 | 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": 2
}
] | [
{
"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.2 |
053-1 | ambig | true | github_repos | List the names of all repositories that contain large Python files or Java files. | [
{
"id": "A",
"phrase": "large",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "file_byte_threshold",
"parameter_dtype": "int",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
10240
],
"intended_parame... | [
{
"id": "GQRY-B.0",
"query": null,
"parameter_names": [
"file_byte_threshold"
],
"parameter_values": {
"file_byte_threshold": 10240
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY-B.1 |
016-0 | ambig | true | retails | Show the number of suppliers and customers from America. | [
{
"id": "A",
"phrase": "suppliers and customers from America",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"suppliers AND (customers from America)",
"(suppliers from America) AND (customers from America)"
],
"intended_interpretation_idx":... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"supplier_count": 10000,
"customer_count": 6100
... | GQRY-A.1-B.0 |
027-1 | 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": 1
},
{
"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.1-C.0 |
028-0 | 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.1 |
010-0 | ambig | true | retails | Find the regions associated with the highest-value order in the car industry. | [
{
"id": "A",
"phrase": "regions",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"customer regions",
"supplier regions"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"r_regionkey": 3,
"r_name": "EUROPE"
}
... | GQRY-A.0 |
027-4 | 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": 1
},
{
"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.1-C.1 |
067-2 | 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.0 |
050-0 | ambig | true | github_repos | What is the monthly average number of events during the period of the last quarter of 2022 and 2023? | [
{
"id": "A",
"phrase": "last quarter of 2022 and 2023",
"type": "finite",
"ambiguity_type": "syntactic_table",
"interpretations": [
"last quarter of 2022 and last quarter of 2023",
"last quarter of 2022 and entire 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": [
{
"event_count": 28032
}
],
"num_rows"... | GQRY-A.0 |
086-2 | ambig | true | codebase_community | Which post received the most responses? | [
{
"id": "A",
"phrase": "responses",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"answers",
"comments",
"both answers and comments"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"post_id": 726,
"post_title": "Famous statistician quo... | GQRY-A.1 |
101-0 | ambig | true | student_club | List all events where a large percentage of attendees were from the same major. | [
{
"id": "A",
"phrase": "large percentage of attendees",
"type": "infinite",
"ambiguity_type": "semantic_value",
"parameter_name": "percentage_threshold",
"parameter_dtype": "float",
"parameter_sample_operators": [
">",
">="
],
"parameter_sample_values": [
0.15
... | [
{
"id": "GQRY",
"query": null,
"parameter_names": [
"percentage_threshold"
],
"parameter_values": {
"percentage_threshold": 0.15
},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
... | GQRY |
092-3 | ambig | true | student_club | Find the total funds allocated for speaker gifts and t-shirts for each event attended by members in Georgetown in South Carolina. | [
{
"id": "A",
"phrase": "total funds allocated for speaker gifts and t-shirts",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"total funds for speaker gifts and t-shirts (single number)",
"total funds for speaker gifts and total funds for t-shirts (... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"event_id": "recEVTik3MlqbvLFi",
"event_name": "Oc... | GQRY-A.0-B.0 |
015-0 | ambig | true | retails | For each industry, show the number of customers that bought part 309 from China. | [
{
"id": "A",
"phrase": "customers that bought part 309 from China",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"customers that bought part 309 from suppliers in China",
"customers in China that bought part 309"
],
"intended_interpretatio... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"industry": "AUTOMOBILE",
"customer_count": 0
... | GQRY-A.0 |
098-1 | 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": 2
... | [
{
"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.2 |
089-1 | 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.1-C.0 |
046-1 | 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.0-C.0 |
036-1 | ambig | true | github_repos | For each repository with a GPL license, count the number of merged PRs created in the period of 2022 and January 2023. | [
{
"id": "A",
"phrase": "GPL license",
"type": "finite",
"ambiguity_type": "semantic_value",
"interpretations": [
"mainline GPL license (gpl-2.0 or gpl-3.0)",
"all GPL variants including AGPL (agpl-3.0) and LGPL (lgpl-2.1 or lgpl-3.0)"
],
"intended_interpretation_idx": 1
},
... | [
{
"id": "GQRY-A.0-B.0",
"query": "WITH gpl_repos AS (\nSELECT grl.repo_name, grl.license\nFROM GITHUB_REPOS_LICENSES grl\nWHERE grl.license IN ('gpl-2.0', 'gpl-3.0')\n),\nall_events AS (\nSELECT * FROM YEAR_2022\nUNION ALL\nSELECT * FROM MONTH_202301\n),\nmerged_prs AS (\nSELECT\njson_extract(ae.repo, '$.na... | GQRY-A.1-B.1 |
005-4 | 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.2 |
008-2 | 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.2 |
029-2 | 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.1-B.2 |
057-1 | 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.1 |
046-3 | 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.0 |
009-2 | 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.0 |
060-1 | 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.0 |
020-0 | 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.0 |
076-1 | 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.0 |
005-3 | 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.0 |
042-4 | 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": 1
},
{
"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.1-B.1 |
054-4 | 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.3 |
056-1 | ambig | true | github_repos | Count the number of 2022 public events whose actor is also the actor of more than 5 events in 2022. | [
{
"id": "A",
"phrase": "public events",
"type": "finite",
"ambiguity_type": "semantic_computation",
"interpretations": [
"events with attribute public=1",
"events of type 'PublicEvent'"
],
"intended_interpretation_idx": 1
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"COUNT(*)": 455197
}
],
"num_rows": ... | GQRY-A.1 |
014-0 | 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": 0
}
] | [
{
"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.0 |
060-2 | 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.1-E.1 |
099-1 | 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": 1
},
{
"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.1-B.1-C.1 |
100-0 | ambig | true | student_club | List all active members who received income from dues or fundraising and sponsorship. | [
{
"id": "A",
"phrase": "income from dues or fundraising and sponsorship",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"income from dues, or from both fundraising and sponsorship.",
"income from both (dues or fundraising) and sponsorship."
],
... | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"member_id": "rec1x5zBFIqoOuPW8",
"first_name": "Angel... | GQRY-A.0 |
054-0 | 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.1 |
033-1 | 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.0 |
025-3 | 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.1-D.0 |
096-2 | ambig | true | student_club | List all members from New York who have signed up for events in September or October 1st in 2019 and display their major, phone number and the event name. | [
{
"id": "A",
"phrase": "New York",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"New York City",
"New York County",
"New York State"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "September or October 1st",
... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"first_name": "Luisa",
"last_name": "Guidi",
... | GQRY-A.1-B.1 |
054-2 | 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.0 |
099-4 | 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": 1
},
{
"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.1-B.1-C.0 |
025-5 | 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.0 |
001-3 | 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.1-C.0-D.0 |
045-0 | 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": 0
},
{
"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.0-B.2 |
073-1 | 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": 1
}
] | [
{
"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.1 |
035-0 | ambig | true | professional_basketball | Count the number of people who played or coached in the NBA finals in the 1990s. | [
{
"id": "A",
"phrase": "played or coached in the NBA finals in the 1990s",
"type": "finite",
"ambiguity_type": "syntactic_computation",
"interpretations": [
"(played in the 1990s) or (coached in the NBA finals in the 1990s)",
"(played or coached) in the NBA finals in the 1990s."
... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"COUNT(DISTINCT personID)": 1027
}
],
... | GQRY-A.1-B.0 |
067-3 | 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.0 |
099-6 | 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": 1
},
{
"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.1-B.0-C.0 |
030-0 | ambig | true | professional_basketball | Compute the total post-season offensive rebounds and games played in their career for each player born in Oklahoma. | [
{
"id": "A",
"phrase": "total post-season offensive rebounds and games played",
"type": "finite",
"ambiguity_type": "syntactic_column",
"interpretations": [
"total post-season offensive rebounds and post-season games played",
"total post-season offensive rebounds and all games played... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"playerID": "beaslch01",
"firstName": "Charles",
... | GQRY-A.1-B.1 |
096-3 | ambig | true | student_club | List all members from New York who have signed up for events in September or October 1st in 2019 and display their major, phone number and the event name. | [
{
"id": "A",
"phrase": "New York",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"New York City",
"New York County",
"New York State"
],
"intended_interpretation_idx": 1
},
{
"id": "B",
"phrase": "September or October 1st",
... | [
{
"id": "GQRY-A.0-B.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"first_name": "Luisa",
"last_name": "Guidi",
... | GQRY-A.1-B.0 |
003-0 | ambig | true | retails | Which supplier has the largest inventory value? | [
{
"id": "A",
"phrase": "inventory value",
"type": "finite",
"ambiguity_type": "semantic_column",
"interpretations": [
"value at supply cost",
"value at retail price"
],
"intended_interpretation_idx": 0
}
] | [
{
"id": "GQRY-A.0",
"query": null,
"parameter_names": [],
"parameter_values": {},
"exec_result": {
"df": {
"format": "parquet_base64_v1",
"preview": {
"sample_data": [
{
"s_suppkey": 5704,
"s_name": "Supplier#000005704",
... | GQRY-A.0 |
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