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gretel_sql:4414be351e3cb0325e4c01cfcd30bf7c54fa8936380626256464d49cf0967d7b
task:4bfe093341fa2e9148b3b3746378a75360e1ad1041add250e13ba8b29f731ce6
83018c64a584c8a72f9ef73d108c601f690a0a89d09cefb571b96fd3ea3239a6
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
25
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total amount donated to each program?
CREATE TABLE Programs (ProgramID INT, ProgramName VARCHAR(255)); INSERT INTO Programs (ProgramID, ProgramName) VALUES (1, 'Education'), (2, 'Health'), (3, 'Environment'); CREATE TABLE DonorsPrograms (DonorID INT, ProgramID INT); INSERT INTO DonorsPrograms (DonorID, ProgramID) VALUES (1, 1), (2, 1), (3, 2), (4, 2), (5, ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT Programs.ProgramName, SUM(Donations.DonationAmount) AS TotalDonatedAmount FROM Programs INNER JOIN DonorsPrograms ON Programs.ProgramID = DonorsPrograms.ProgramID INNER JOIN Donations ON DonorsPrograms.DonorID = Donations.DonorID GROUP BY Programs.ProgramName;
{"domain":"nonprofit operations","domain_description":"Donation records, program outcomes, volunteer engagement metrics, budget reports, and community impact assessments.","id":25,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQL ...
b8d2a0ae5ef1c7dd5edd627ad573a4aafdf3a31b91b9cd70013e13bd8393f078
gretel_sql:test:25
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
24
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:4bfe093341fa2e9148b3b3746378a75360e1ad1041add250e13ba8b29f731ce6
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:4aa73a02c9655084807c496d82864ca7f306db6c50f2c66817d67a263aa2e261
task:5cc93490e8a9c5ef0211c436d805090ef927f4a7dc93663bf8ff93a987de2e9b
0f5c27e213b43e19c4d48df757464d6cffefce0212a2dd897097aa5e642a813f
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
109
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Which defense contracts have the highest total value, and what are their respective values?
CREATE TABLE Defense_Contracts (Contract_ID INT, Contract_Name VARCHAR(255), Agency VARCHAR(255), Value DECIMAL(18,2)); INSERT INTO Defense_Contracts (Contract_ID, Contract_Name, Agency, Value) VALUES (1, 'Contract A', 'DOD', 5000000), (2, 'Contract B', 'DOJ', 6000000), (3, 'Contract C', 'DOD', 7000000), (4, 'Contract ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT Contract_Name, Value FROM (SELECT Contract_Name, Value, ROW_NUMBER() OVER (ORDER BY Value DESC) as Rank FROM Defense_Contracts) as Ranked_Contracts WHERE Rank <= 3;
{"domain":"defense industry","domain_description":"Defense contract data, military equipment maintenance, threat intelligence metrics, and veteran employment stats.","id":109,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.)...
f9fcd425b721e4863a77910f8524b34f3e16da0574d8dc2efdb021f9953b69e2
gretel_sql:test:109
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
108
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:5cc93490e8a9c5ef0211c436d805090ef927f4a7dc93663bf8ff93a987de2e9b
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:ac5f0ae715b67600dbf8ce5a233b447ec91698ea080464b3217e0c407055e66d
task:a72b7dc3bb4ac92b6d07577365bf38326217f46518a2b52fe09248e5c50dcd1f
67988782d10d9406a2a2393df036571e86c130af3fe9ee728da96a9a7404b688
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
114
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average weight of cargo handled by vessels in the 'Bulk Carrier' type at each port?
CREATE TABLE ports (id INT, name VARCHAR(50), location VARCHAR(50), un_code VARCHAR(10)); CREATE TABLE vessels (id INT, name VARCHAR(50), type VARCHAR(50), year_built INT, port_id INT); CREATE TABLE cargo (id INT, description VARCHAR(50), weight FLOAT, port_id INT, vessel_id INT); CREATE VIEW vessel_cargo AS SELECT v.n...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT p.name AS port_name, AVG(vc.weight) AS avg_weight FROM ports p JOIN vessels v ON p.id = v.port_id JOIN vessel_cargo vc ON v.name = vc.vessel_name WHERE v.type = 'Bulk Carrier' GROUP BY p.name;
{"domain":"ocean shipping","domain_description":"Detailed records on cargo handling, fleet management, port operations, and regulatory compliance in ocean shipping.","id":114,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query ca...
cbc90ad494e107f7e99a3250f4fac61ed8b7cec945d93ac89348c6bfce39a51d
gretel_sql:test:114
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
113
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:a72b7dc3bb4ac92b6d07577365bf38326217f46518a2b52fe09248e5c50dcd1f
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:5a3983d437ea7313659783169e052c2fcc99ee5002db9768a5d4e16f9aacba4d
task:668d9ed3342193821fcf71aa112035d491236e82ba7a3a323df7ed6b554e99ae
ac036d0a37566ef33e478c11e478c09f9035bdb571b0abd7403dff2cf3cbb4a7
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
221
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Which mental health conditions were treated most frequently in Canada during 2022?
CREATE TABLE patients (id INT, country VARCHAR(255)); CREATE TABLE treatments (id INT, patient_id INT, treatment_date DATE); CREATE TABLE conditions (id INT, patient_id INT, condition VARCHAR(255)); INSERT INTO patients (id, country) VALUES (1, 'Canada'), (2, 'Canada'); INSERT INTO treatments (id, patient_id, treatment...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT conditions.condition, COUNT(conditions.condition) AS count FROM conditions JOIN patients ON conditions.patient_id = patients.id JOIN treatments ON patients.id = treatments.patient_id WHERE patients.country = 'Canada' AND treatments.treatment_date >= '2022-01-01' AND treatments.treatment_date < '2023-01-01' GROUP...
{"domain":"mental health","domain_description":"In-depth data on mental health conditions, treatment approaches, patient outcomes, and public awareness campaigns in mental health.","id":221,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation"...
1ade5c12ba44207339a049f5bedfe5c77f6a4b6cf22d53ace16a487a72389f68
gretel_sql:test:221
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
220
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:668d9ed3342193821fcf71aa112035d491236e82ba7a3a323df7ed6b554e99ae
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:7e20e548f8a175e0b0b2a6519f174bb728f2f13d78123e4bb5365200d2eae019
task:daf495e597b052523601e658cddeb5b2c335fb3edce926095df46755a04cf665
be005e0c93da36ce0eab4e13780dbef8eb25b6bf0a3c5fbdd4f0cd4e6759c093
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
230
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Find the mobile subscribers with consecutive speed drops greater than 25% for the last 3 months, ordered by subscription IDs.
CREATE TABLE mobile_usage_detailed (subscriber_id INT, month INT, speed FLOAT); INSERT INTO mobile_usage_detailed (subscriber_id, month, speed) VALUES (1, 1, 100), (1, 2, 80), (1, 3, 70), (2, 1, 200), (2, 2, 180), (2, 3, 160), (3, 1, 150), (3, 2, 130), (3, 3, 110);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT subscriber_id, speed, month FROM (SELECT subscriber_id, speed, month, LAG(speed, 1) OVER (PARTITION BY subscriber_id ORDER BY month) as prev_speed, LAG(speed, 2) OVER (PARTITION BY subscriber_id ORDER BY month) as prev_prev_speed FROM mobile_usage_detailed) t WHERE t.speed < 0.75 * t.prev_speed AND t.speed < 0.7...
{"domain":"telecommunications","domain_description":"Mobile and broadband subscriber data, network infrastructure investments, customer usage patterns, and regulatory compliance information.","id":230,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, ...
112b3a088b4574579d30ffc11195b74bc894217dda47629d876a18ec9141a0bb
gretel_sql:test:230
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
229
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:daf495e597b052523601e658cddeb5b2c335fb3edce926095df46755a04cf665
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:6bb8af4984d6034a3e9b32ff1346042eb58c39ad879aa32c4a6f5709edd08185
task:a3871b1db697742e405cdd20bd7f55ca3f34b6bf88d9b3c67c38b2a05b9d1d6b
8f64fc8cfb445395c52a2b3f77859683574cead87f8b1cb12dc18c687671528a
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
520
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total number of electric vehicles sold in 'California' in the 'sales' schema?
CREATE TABLE sales_regions (id INT, name VARCHAR(50)); CREATE TABLE sales (id INT, region_id INT, vehicle_count INT); CREATE TABLE vehicles (id INT, type VARCHAR(50)); INSERT INTO sales_regions VALUES (1, 'California'); INSERT INTO sales VALUES (1, 1, 5000); INSERT INTO vehicles VALUES (1, 'electric');
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT SUM(sales.vehicle_count) FROM sales INNER JOIN sales_regions ON sales.region_id = sales_regions.id INNER JOIN vehicles ON sales.id = vehicles.id WHERE vehicles.type = 'electric' AND sales_regions.name = 'California';
{"domain":"automotive","domain_description":"Vehicle safety testing results, autonomous driving research data, electric vehicle adoption statistics, and auto show information.","id":520,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"Fi...
9d38bf9cb28f33e9e5ea04e6a96c8e8d545757cf665a2ed0abde4792de84157a
gretel_sql:test:520
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
519
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:a3871b1db697742e405cdd20bd7f55ca3f34b6bf88d9b3c67c38b2a05b9d1d6b
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:87c4e12fe199ca347bf97c7e00082e4b70680965bef34d32f9654ae7ea2b33e9
task:651a9a5de76a2827874f63c3aa44cfac2c2b54f44eda7c0fdf9ca8ecfd4e2bb8
bab44501738294e19a13688cbababae07971f41a087ec3d4607480fcb6d33ab4
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
526
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the percentage of accidents for each aircraft model?
CREATE SCHEMA if not exists aerospace;CREATE TABLE if not exists aerospace.aircraft (id INT PRIMARY KEY, name VARCHAR(50), model VARCHAR(50), accidents INT); INSERT INTO aerospace.aircraft (id, name, model, accidents) VALUES (1, 'Boeing', '737', 3), (2, 'Boeing', '747', 2), (3, 'Airbus', 'A320', 6);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT model, (SUM(accidents) OVER (PARTITION BY model) * 100.0 / (SELECT SUM(accidents) FROM aerospace.aircraft)) as accident_percentage FROM aerospace.aircraft;
{"domain":"aerospace","domain_description":"Aircraft manufacturing data, satellite deployment projects, flight safety records, and space exploration research.","id":526,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with ...
38ab81904e9d975f69d0fa05ca547db4015474ec300712b5d55336f36abcbaf9
gretel_sql:test:526
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
525
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:651a9a5de76a2827874f63c3aa44cfac2c2b54f44eda7c0fdf9ca8ecfd4e2bb8
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:65cdf87508384e3bd00b6611e3293688bece5d4dcdc517cdb3d8539acd600c60
task:cc75dc0b60e865f0c5430eacb08019bed52c3ef6a60b453a0b7dd5856be3e89a
199f90e6943fe6c9fb106cf606f6027150300ef25d952cf88f84aff058ad05cc
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
530
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the maximum feeding rate by feed type and farm size?
CREATE TABLE Feed ( id INT PRIMARY KEY, type VARCHAR(50) ); CREATE TABLE Farm ( id INT PRIMARY KEY, feed_id INT, size INT, FOREIGN KEY (feed_id) REFERENCES Feed(id) ); CREATE TABLE FeedingRate ( farm_id INT, feed_id INT, rate INT, FOREIGN KEY (farm_id) REFERENCES Farm(id), FOREIGN KEY (feed_id) REFERENCES Feed(id) );
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT Feed.type, Farm.size, MAX(FeedingRate.rate) FROM Feed INNER JOIN FeedingRate ON Feed.id = FeedingRate.feed_id INNER JOIN Farm ON FeedingRate.farm_id = Farm.id GROUP BY Feed.type, Farm.size;
{"domain":"aquaculture","domain_description":"Aquatic farming data, fish stock management, ocean health metrics, and sustainable seafood trends.","id":530,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQL query joins the Feed, Fee...
1292890e22ff60e64aef70f7b4b88f852ef382ddbd049c8d67331dc343a48975
gretel_sql:test:530
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
529
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:cc75dc0b60e865f0c5430eacb08019bed52c3ef6a60b453a0b7dd5856be3e89a
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:638fa07b6e6620e4ae8c7114ef15a6867b63cbb5fe0ba84ac8a902973e24397c
task:f0e68e38897af2d9b4357b3e6d6d88803b3a6b4a358a52c7947517ca280b9bd8
f8fdfd74643d42004277a302f3d71f5a22dc70116726020732f517f2947af1dc
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
558
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
How many professional development courses were completed by teachers in the English department?
CREATE TABLE teachers (teacher_id INT, department_id INT, teacher_name VARCHAR(255)); INSERT INTO teachers VALUES (1, 1, 'Ms. Hernandez'); INSERT INTO teachers VALUES (2, 2, 'Mr. Johnson'); CREATE TABLE departments (department_id INT, department_name VARCHAR(255)); INSERT INTO departments VALUES (1, 'English'); INSERT ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT d.department_name, COUNT(c.course_id) FROM course_enrollment ce INNER JOIN teachers t ON ce.teacher_id = t.teacher_id INNER JOIN departments d ON t.department_id = d.department_id WHERE d.department_name = 'English';
{"domain":"education","domain_description":"Education data on student mental health, teacher professional development, open pedagogy, and lifelong learning.","id":558,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query performs a...
68e560f79fddabea2843cdd27372a25eb05682d3b88e8e22eab27cba4bb6672c
gretel_sql:test:558
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
557
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:f0e68e38897af2d9b4357b3e6d6d88803b3a6b4a358a52c7947517ca280b9bd8
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:605eb1151f2bd663d78aa6d8914e4f1bbb6b3b94a4533f363f26b8eaf9a26853
task:a1139ac892b43b22dcc4fcff41c14fdea6e7228520f879f048bed06ff6012a3c
2341f0102a8336a0623162f960aeb4ff5f3b392216fee8894e6abc4f16e45828
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
678
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average labor productivity by mine type in the past 12 months?
CREATE TABLE mine_labor_productivity (mine_type VARCHAR(255), productivity NUMERIC, measurement_date DATE); INSERT INTO mine_labor_productivity (mine_type, productivity, measurement_date) VALUES ('open_pit', 1234, '2021-08-01'), ('underground', 2345, '2021-08-01'), ('open_pit', 5432, '2021-07-01'), ('underground', 6789...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT mine_type, AVG(productivity) as avg_productivity FROM (SELECT mine_type, productivity, measurement_date, ROW_NUMBER() OVER (PARTITION BY mine_type ORDER BY measurement_date DESC) as rn FROM mine_labor_productivity WHERE measurement_date >= DATEADD(month, -12, CURRENT_DATE)) t WHERE rn = 1 GROUP BY mine_type;
{"domain":"mining industry","domain_description":"Mineral extraction statistics, environmental impact assessments, labor productivity metrics, and geological survey information.","id":678,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCEN...
587654e1d2bdf78b465061da0cf60f6d43f8b83a087923e2433c74c6335dd05a
gretel_sql:test:678
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
677
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:a1139ac892b43b22dcc4fcff41c14fdea6e7228520f879f048bed06ff6012a3c
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:1140fe7186b632d58ed096be6fcc60542e87a5d06d81be0050471bc91eeb2b54
task:9ba448bbb897e7afcf0455f3aecc67154b6af21bdab77fb862718386fe8a75db
c5ccf013a7ed323d8af9f17fadd52a465d3eda3b76e42c9e20fc0ab35820f0e2
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
682
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total number of articles written by each author in each region?
CREATE TABLE authors (id INT, name TEXT); CREATE TABLE regions (id INT, name TEXT); CREATE TABLE articles (id INT, title TEXT, content TEXT, author_id INT, region_id INT); INSERT INTO authors (id, name) VALUES (1, 'John Doe'), (2, 'Jane Smith'); INSERT INTO regions (id, name) VALUES (1, 'North'), (2, 'South'), (3, 'Eas...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT authors.name, regions.name, COUNT(articles.id) FROM authors INNER JOIN articles ON authors.id = articles.author_id INNER JOIN regions ON regions.id = articles.region_id GROUP BY authors.name, regions.name;
{"domain":"journalism","domain_description":"News reporting data, audience demographics, media ethics information, and investigative journalism projects.","id":682,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This SQL query retrieves...
595530aa682c3b5c863053849e0c392fab3ca7cf706fce9f209e6ed737c60080
gretel_sql:test:682
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
681
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:9ba448bbb897e7afcf0455f3aecc67154b6af21bdab77fb862718386fe8a75db
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:2cb817c0878d4ceca8e51cbe2251ddaab269ba243af0133f8df8b97c10542591
task:9816b122d966001a0e963d05552c2c81c94d9e51bff1cfcb3eaae574f60afeaf
9703fe06ce5ef646effd3caba6dbb2eeffc909cdec5569e5f71325d866cc4eb3
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
735
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Show the total assets under management (AUM) for each investment strategy.
CREATE TABLE investment_strategies (strategy_id INT, strategy VARCHAR(20)); INSERT INTO investment_strategies (strategy_id, strategy) VALUES (1, 'Conservative'), (2, 'Moderate'), (3, 'Aggressive'); CREATE TABLE client_strategy (client_id INT, strategy_id INT); INSERT INTO client_strategy (client_id, strategy_id) VALUES...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT cs.strategy, SUM(value) AS total_aum FROM client_strategy cs JOIN clients c ON cs.client_id = c.client_id JOIN assets a ON c.client_id = a.client_id GROUP BY cs.strategy;
{"domain":"financial services","domain_description":"Detailed financial data including investment strategies, risk management, fraud detection, customer analytics, and regulatory compliance.","id":735,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_e...
c69a1cb177a81cee54cae23948e51d766b1dbc072cb1187ddbecb3bb3a8a5986
gretel_sql:test:735
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
734
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:9816b122d966001a0e963d05552c2c81c94d9e51bff1cfcb3eaae574f60afeaf
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:7459d61c01da813f6ad9cbb9b4229d0cf7ba56e8dfa410d8e4a34583baa08390
task:70f33e41fbcef91a27881f142726199b85e75bbb40417c18833e85b72ba12a31
aaa07784f839ac7d7cfb0da7045e5af9beb07288fed3355722086d4b68ffae18
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
746
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Who has the highest number of wins as a coach for each team in a season?
CREATE TABLE Coach (CoachID int, CoachName varchar(50), TeamID int); CREATE TABLE Match (MatchID int, HomeTeamID int, AwayTeamID int, HomeTeamResult varchar(5)); INSERT INTO Coach (CoachID, CoachName, TeamID) VALUES (1, 'Jose Mourinho', 1), (2, 'Pep Guardiola', 1), (3, 'Jurgen Klopp', 2), (4, 'Mauricio Pochettino', 2)...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT c.TeamID, c.CoachName, COUNT(CASE WHEN h.HomeTeamResult = 'Win' THEN 1 END) + COUNT(CASE WHEN a.HomeTeamResult = 'Win' THEN 1 END) AS Wins FROM Coach c LEFT JOIN Match h ON c.TeamID = h.HomeTeamID AND h.HomeTeamResult = 'Win' LEFT JOIN Match a ON c.TeamID = a.AwayTeamID AND a.HomeTeamResult = 'Win' GROUP BY c.Te...
{"domain":"sports","domain_description":"Extensive data on athlete performance, team management, fan engagement, facility operations, and event planning in sports.","id":746,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"We calculate t...
63fc7c03b5b1ef1e6264594bd37f803866784b1511508639b707998eea5cb854
gretel_sql:test:746
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
745
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:70f33e41fbcef91a27881f142726199b85e75bbb40417c18833e85b72ba12a31
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:01d48c68838101488159909639607f2b3ef488591cb373e254b64b2df4aa9c31
task:bc95ae27ab56e584f96e47115c558f8119bd5a8b666d2592292d0451cbaa4462
6063ff0c84448751a65d95d66772e00888e5ae9eda1e539a8d266a962c5728f8
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
762
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the name and location of the top 3 most donated heritage sites?
CREATE TABLE HeritageSites (SiteID int, Name varchar(100), Location varchar(100), TotalDonations decimal(10,2)); INSERT INTO HeritageSites (SiteID, Name, Location, TotalDonations) VALUES (1, 'Machu Picchu', 'Peru', 500000.00), (2, 'Great Wall', 'China', 700000.00), (3, 'Petra', 'Jordan', 600000.00);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT Name, Location FROM (SELECT Name, Location, ROW_NUMBER() OVER (ORDER BY TotalDonations DESC) as rn FROM HeritageSites) t WHERE rn <= 3;
{"domain":"cultural preservation","domain_description":"In-depth data on heritage sites, traditional arts, language preservation, and community engagement in cultural preservation.","id":762,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PER...
dc12336decfa75a4d789ecda6789b4afbc64107f621a9e1c38da7b19cc8eb9ed
gretel_sql:test:762
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
761
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:bc95ae27ab56e584f96e47115c558f8119bd5a8b666d2592292d0451cbaa4462
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:5a8aef0121a287c4b608a17e724ff816cd28d47ed00cd80665509e2f10a03707
task:9aac2c920b3e6b48701b3a0fb8d316135bb4e5b060787e428ce3ac3eaf5a54e9
2c473044d22c2b9d5ab5eccfbf0b27573300dc08a81f65e2fba98229985c57f6
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
784
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
List the top 2 countries with the highest water consumption in the current month.
CREATE TABLE water_consumption (country VARCHAR(255), consumption FLOAT, date DATE); INSERT INTO water_consumption (country, consumption, date) VALUES ('Brazil', 20000, '2022-05-01'); INSERT INTO water_consumption (country, consumption, date) VALUES ('Egypt', 25000, '2022-05-01');
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT country, consumption FROM (SELECT country, consumption, ROW_NUMBER() OVER (ORDER BY consumption DESC) as rank FROM water_consumption WHERE date >= '2022-05-01' GROUP BY country, consumption) subquery WHERE rank <= 2;
{"domain":"water resources","domain_description":"Water usage metrics, drought impact assessments, wastewater treatment data, and water conservation initiatives.","id":784,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) wi...
dd3f1a80e263ba18813c8c88581f6d56f165958216c1b8f5667969b39b4eadc3
gretel_sql:test:784
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
783
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:9aac2c920b3e6b48701b3a0fb8d316135bb4e5b060787e428ce3ac3eaf5a54e9
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:cf559e5c2bae52d4272c5eb38a7e2167eff926c33014044282561ff7c31bab38
task:a38fc41269ee25b90f01c3a1d518e1f7d2ac103125ed9f6c2662086d957ce00c
31b14284a2446b7f5887d374afa6710847564bf3eb09d99d233307779b8f9d5e
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
812
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average salary of 'engineer' workers in each factory?
CREATE TABLE factories (factory_id INT, factory_name VARCHAR(20)); INSERT INTO factories VALUES (1, 'Factory X'), (2, 'Factory Y'), (3, 'Factory Z'); CREATE TABLE roles (role_id INT, role_name VARCHAR(20)); INSERT INTO roles VALUES (1, 'engineer'), (2, 'manager'), (3, 'assistant'); CREATE TABLE workers (worker_id INT, ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT f.factory_name, AVG(salary) FROM workers w INNER JOIN factories f ON w.factory_id = f.factory_id INNER JOIN roles r ON w.role_id = r.role_id WHERE r.role_name = 'engineer' GROUP BY f.factory_name;
{"domain":"manufacturing","domain_description":"Detailed records on ethical manufacturing, circular economy, workforce development, and industry 4.0.","id":812,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"We join the 'workers', 'fact...
32729a02f28933aa612841bfc8d1614eebf512675b185150d8c3e7dd81e6c641
gretel_sql:test:812
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
811
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:a38fc41269ee25b90f01c3a1d518e1f7d2ac103125ed9f6c2662086d957ce00c
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:465e31d4932a5b02f83c2cf7b9f54f2a2196e23eb394402f6de197940f6ec0ee
task:e5574d8b9ccbfa0e6932d295d20b7579909c7d6875a9c85bb97894b516ff0538
b032f9a26c6c0cedb5604b01ab4d101f953894fb7ce4811deec6cda5ec1ab148
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
827
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average price of free-range eggs per store?
CREATE TABLE Stores (store_id INT, store_name VARCHAR(255)); CREATE TABLE Products (product_id INT, product_name VARCHAR(255), is_free_range BOOLEAN, price INT); CREATE TABLE Inventory (store_id INT, product_id INT, quantity INT);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT s.store_name, AVG(p.price) as avg_price FROM Inventory i JOIN Stores s ON i.store_id = s.store_id JOIN Products p ON i.product_id = p.product_id WHERE p.is_free_range = TRUE AND p.product_category = 'egg' GROUP BY s.store_name;
{"domain":"food industry","domain_description":"Food safety records, nutrition data, supply chain transparency, and sustainable food trends.","id":827,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query joins the Stores, Products...
9ab947c4d6bc9c13812d17392c9bff770e34780b806291d6e034a1a082700f0d
gretel_sql:test:827
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
826
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:e5574d8b9ccbfa0e6932d295d20b7579909c7d6875a9c85bb97894b516ff0538
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:5c30989fbb666c1be52f42e17ae1853c362200c2c002627ee8914eca4859c8fa
task:14368e576c6c19a25ef9a785dfa5cb7a3c23b2244503755d1b4eed6cbad9a939
ea117b6e90849d900c0ffc580e759d7119323088f4f77bd78e4d83848387abf1
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
895
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
How many volunteers with 'Medical' skills were assigned before a volunteer with 'Engineering' skills?
CREATE TABLE volunteers_ext (id INT, name VARCHAR(50), age INT, gender VARCHAR(10), skill VARCHAR(50), assignment_date DATE, end_date DATE); INSERT INTO volunteers_ext (id, name, age, gender, skill, assignment_date, end_date) VALUES (1, 'David', 25, 'Male', 'Medical', '2022-06-01', '2022-09-30'), (2, 'Emma', 30, 'Femal...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT COUNT(*) FROM (SELECT skill, assignment_date, LAG(skill) OVER (ORDER BY assignment_date) AS prev_skill FROM volunteers_ext WHERE skill = 'Medical') t WHERE prev_skill = 'Engineering';
{"domain":"humanitarian aid","domain_description":"Extensive data on disaster response, refugee support, community development, and advocacy in humanitarian aid.","id":895,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) wi...
b4316fb2f1c4cbf3f49ec9be60740aec4eec75ef6cdb72203c6c1c3cbe66c59a
gretel_sql:test:895
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
894
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:14368e576c6c19a25ef9a785dfa5cb7a3c23b2244503755d1b4eed6cbad9a939
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:e59724c100af36653e60a79bf51e03365c936fa2e34be9f3358770f744c647a0
task:192a1ff1c67b2582f4c2bfbfe2b8a6d4e3361b1ee963fabc1f0bb516ff43483f
9b1643b005f623c0e0cfb0687a7fb79c34fc2fc9ca0b79c7cea94cfe46b32d50
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
926
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average age of players who play VR games and their total spending on games?
CREATE TABLE players (id INT, name VARCHAR(50), age INT, country VARCHAR(50)); INSERT INTO players (id, name, age, country) VALUES (1, 'John Doe', 25, 'USA'), (2, 'Jane Smith', 30, 'Canada'); CREATE TABLE games (id INT, name VARCHAR(50), type VARCHAR(50), price DECIMAL(5,2)); INSERT INTO games (id, name, type, price) V...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT AVG(players.age), SUM(games.price) FROM players INNER JOIN player_games ON players.id = player_games.player_id INNER JOIN games ON player_games.game_id = games.id WHERE games.type = 'VR';
{"domain":"gaming technology","domain_description":"Player demographics, game design data, esports event information, and virtual reality technology adoption.","id":926,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"First, we join the ...
ae7f531a5f3bcddad69b00e3a3047e65715ec65e2d71f77ed893bcc4a73dcf0b
gretel_sql:test:926
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
925
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:192a1ff1c67b2582f4c2bfbfe2b8a6d4e3361b1ee963fabc1f0bb516ff43483f
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:66f2d9803d8a04db0de3ff55a25b95ab916be000a2c54667d4c4c704de5a640b
task:f35523c79d36444f3d64cd5403cdfa72c5c0c4530c2d32d186ec143f3f70dae0
4eb7871388f934fc562f451545b5fd3c1213a1c386738efd15926f514e332632
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
959
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total salary paid to construction workers who worked on sustainable building projects in Washington?
CREATE TABLE ConstructionLaborStatistics (id INT, name VARCHAR(50), job VARCHAR(50), salary INT); INSERT INTO ConstructionLaborStatistics VALUES (1, 'Charles Doe', 'Carpenter', 50000); INSERT INTO ConstructionLaborStatistics VALUES (2, 'Diana Smith', 'Electrician', 60000); CREATE TABLE BuildingTypes (id INT, building_t...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT SUM(cls.salary) FROM ConstructionLaborStatistics cls JOIN WorkerBuildings wb ON cls.id = wb.worker_id JOIN BuildingTypes bt ON wb.building_id = bt.id WHERE bt.building_type = 'Sustainable' AND state = 'Washington';
{"domain":"construction","domain_description":"Building permit data, construction labor statistics, project timeline information, and sustainable building practices.","id":959,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"We find the ...
46e6728bd1a85e027a69d5fbe73a362e9a4489b37fee9efb6110ea8af635a8d6
gretel_sql:test:959
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
958
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:f35523c79d36444f3d64cd5403cdfa72c5c0c4530c2d32d186ec143f3f70dae0
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:c2c8897bca9564cfd28a876124d3931163624130d248f21cfb497c72edefc231
task:e7b4b5f2110113e5ecdac39ac67ff02aacbca58c5363f23bd1725f7dd2776d6b
64847034182786e5eabd96e3518f7a07e67c7aaaf5ecf761d788eb10781dd8e8
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
978
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the production rate for the well with the highest production rate?
CREATE TABLE wells (well_id INT, well_type VARCHAR(10), location VARCHAR(20), production_rate FLOAT); INSERT INTO wells (well_id, well_type, location, production_rate) VALUES (1, 'offshore', 'Gulf of Mexico', 1000), (2, 'onshore', 'Texas', 800), (3, 'offshore', 'North Sea', 1200);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT production_rate FROM (SELECT well_id, well_type, location, production_rate, ROW_NUMBER() OVER (ORDER BY production_rate DESC) rn FROM wells) t WHERE rn = 1;
{"domain":"oil and gas","domain_description":"Exploration data, production figures, infrastructure development, and market trends.","id":978,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and ordering","...
2a5cfa63434192e8899a6d24c32b5e41eb96e71716ba44dab4f8f5c7946f172e
gretel_sql:test:978
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
977
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:e7b4b5f2110113e5ecdac39ac67ff02aacbca58c5363f23bd1725f7dd2776d6b
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:57dd8407cbca173b558f00ef01f253e505f13cdf433ee055ae0e5513271eba2b
task:54457ae6a4f1c948787157d5269384e546075af95d8e601a04c1b46621dcab9b
e76a2d0a6305cc0f378b6f90f7fee120d8128a87c44970567c59aecb3ccbf083
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1009
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Find the number of IoT sensors installed in each farm that use Sprinkler irrigation.
CREATE TABLE IoT_Sensors (id INT, sensor_type VARCHAR(50), Farm_id INT); INSERT INTO IoT_Sensors (id, sensor_type, Farm_id) VALUES (1, 'Soil Moisture', 1), (2, 'Temperature', 1), (3, 'Humidity', 2); CREATE TABLE Irrigation (id INT, Farm_id INT, irrigation_type VARCHAR(50), duration INT); INSERT INTO Irrigation (id, Far...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT f.id, COUNT(s.id) FROM Farmers f JOIN Irrigation i ON f.id = i.Farm_id JOIN IoT_Sensors s ON f.id = s.Farm_id WHERE i.irrigation_type = 'Sprinkler' GROUP BY f.id;
{"domain":"precision agriculture","domain_description":"Precision farming data, satellite imagery analysis, IoT sensor metrics, and agricultural automation trends.","id":1009,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query jo...
18e0c6a04e73d57937e51b8f42dce495bada1fcce20ed6453d850d566f9d2c13
gretel_sql:test:1009
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,008
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:54457ae6a4f1c948787157d5269384e546075af95d8e601a04c1b46621dcab9b
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:a3a6f3a5ff71f557ad8867b5153fb65fd983b8aceba08e05ee2c1141f7b135b5
task:f979f8ef3f1bfee4bc4e9af1863d72ea3c95c88873dc82faf56a9a6cc0bc2e44
b5f416338ad2926d1bdffdfb727cf11827a3ed3e1588d73aed0ea6e2ab062e19
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1033
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average dissolved oxygen level for each species in our fish farms?
CREATE TABLE fish_farms (id INT, name TEXT, location TEXT, water_type TEXT); INSERT INTO fish_farms (id, name, location, water_type) VALUES (1, 'Farm A', 'Seattle', 'Saltwater'); INSERT INTO fish_farms (id, name, location, water_type) VALUES (2, 'Farm B', 'Portland', 'Freshwater'); CREATE TABLE fish_species (id INT, na...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT fs.name AS species_name, AVG(av.dissolved_oxygen) AS avg_dissolved_oxygen FROM fish_inventory fi JOIN fish_farms ff ON fi.fish_farm_id = ff.id JOIN fish_species fs ON fi.fish_species_id = fs.id JOIN (SELECT fish_species_id, AVG(dissolved_oxygen) AS dissolved_oxygen FROM water_quality GROUP BY fish_species_id) av...
{"domain":"aquaculture","domain_description":"Aquatic farming data, fish stock management, ocean health metrics, and sustainable seafood trends.","id":1033,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query joins the fish_farms,...
887ab0c78c632b519f1071f7119b01202ba110c11be6cb56df2fa497de06fbcf
gretel_sql:test:1033
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,032
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:f979f8ef3f1bfee4bc4e9af1863d72ea3c95c88873dc82faf56a9a6cc0bc2e44
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=2;joins=3;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:f9b9d0cbd6b8f356f1fdb0252b797cfb5cdefc97d635773b95d85c2688076955
task:418483412367aad348001ae1e866b409432f34c35a911db3427878c1b44ea09e
647fc6fd9339004874dcc92dff31fca841adc36ed2dd74f99df831b6177ea05d
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1126
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
How many innovations have been made in the African region since 2016?
CREATE TABLE region (id INT, region VARCHAR(50)); INSERT INTO region (id, region) VALUES (1, 'North America'); INSERT INTO region (id, region) VALUES (2, 'Europe'); INSERT INTO region (id, region) VALUES (3, 'Africa'); CREATE TABLE innovation_region (id INT, innovation_id INT, region_id INT); INSERT INTO innovation_reg...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT COUNT(*) FROM innovation i INNER JOIN innovation_region ir ON i.id = ir.innovation_id INNER JOIN region r ON ir.region_id = r.id WHERE r.region = 'Africa' AND i.year >= 2016;
{"domain":"startups","domain_description":"Company founding data, funding records, diversity metrics, and innovation trends.","id":1126,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query joins the innovation, innovation_region, ...
e583dfd8a9b9149ee7b6055b732bfb8cb6e5a343d36d60b42ce759b947dc3ef2
gretel_sql:test:1126
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,125
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:418483412367aad348001ae1e866b409432f34c35a911db3427878c1b44ea09e
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:c69e12dbc5c450a9368a5b805663e84ea5abaa96216b9e58c14daa243739ab5d
task:dc854f72eecca7b38213372fe5935fcae76b70f22149574bae5d2ca90ad6efd1
cb5f90d914b6709023a07b1e55e3f7b3166b063f4fa034702bfeb357a361dc8c
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1178
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
List the booking dates and hotel names for all OTA bookings where the hotel has implemented at least one AI-powered solution.
CREATE TABLE otas (ota_id INT, booking_date DATE, hotel_id INT); CREATE TABLE hotels (hotel_id INT, hotel_name TEXT, region TEXT); CREATE TABLE ai_solutions (solution_id INT, hotel_id INT, implemented_date DATE); INSERT INTO hotels (hotel_id, hotel_name, region) VALUES (1, 'Beach Retreat', 'Americas'); INSERT INTO ai_s...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT otas.booking_date, hotels.hotel_name FROM otas INNER JOIN hotels ON otas.hotel_id = hotels.hotel_id INNER JOIN ai_solutions ON hotels.hotel_id = ai_solutions.hotel_id GROUP BY otas.booking_date, hotels.hotel_name HAVING COUNT(DISTINCT ai_solutions.solution_id) >= 1;
{"domain":"hospitality technology","domain_description":"Hotel tech adoption metrics, online travel agency data, virtual tour engagement stats, and hospitality AI trends.","id":1178,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This S...
cd33d91e040fb59fb6292d32bc51f67aa98f3b7ada08a6a7032bdeb02f805161
gretel_sql:test:1178
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,177
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:dc854f72eecca7b38213372fe5935fcae76b70f22149574bae5d2ca90ad6efd1
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:8091ef2ad6bfd5f179cf52d7d9c5784b9752f35a18d8f92632ddfd3e7bcaadb2
task:b71d9e9e836386899ba8fab23e0e852d6ef781e10c294c226d79df57b32a5b48
e05fc109dd77313ecbe78a07430ab46389c6cd1b4f8e158edbcb3499b83784e7
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1187
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Who are the top 3 authors with the highest number of articles published in The Guardian?
CREATE TABLE authors (id INT, name VARCHAR(100), publisher VARCHAR(50)); CREATE TABLE articles_authors (article_id INT, author_id INT); INSERT INTO authors (id, name, publisher) VALUES (1, 'Author1', 'The Guardian'), (2, 'Author2', 'The Guardian'), (3, 'Author3', 'The Guardian'); INSERT INTO articles_authors (article_i...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT a.name, COUNT(aa.article_id) AS articles_count FROM authors a JOIN articles_authors aa ON a.id = aa.author_id JOIN articles ar ON aa.article_id = ar.id WHERE ar.publisher = 'The Guardian' GROUP BY a.name ORDER BY articles_count DESC LIMIT 3;
{"domain":"media","domain_description":"Media data on content diversity, media literacy, disinformation detection, and media representation.","id":1187,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"Find the top 3 authors with the high...
79566f440acc5280d1fe46233ad6ac4a5057421fb1ba103e44a2974000552a8d
gretel_sql:test:1187
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,186
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:b71d9e9e836386899ba8fab23e0e852d6ef781e10c294c226d79df57b32a5b48
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:5ca9ec89e334c46262dc057ae5b3672513875e655f83e66402db9b6776e583a3
task:939eb10a689dc7ee5910af3e2631fc6fe324d187ca4b02e46aeb86cdde707339
fc1477d1e3b668dd55c4e4c79eac8415f9a19fce4d3f35ba6dbb9b08a67ab976
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1286
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Identify the top 3 countries with the highest percentage of attendees
CREATE TABLE attendee_info (attendee_id INT, country VARCHAR(20)); INSERT INTO attendee_info (attendee_id, country) VALUES (1, 'USA'), (2, 'Canada'), (3, 'Mexico'), (4, 'USA'), (5, 'Brazil'), (6, 'USA');
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT country, (COUNT(attendee_id) OVER (PARTITION BY country) * 100.0 / (SELECT COUNT(attendee_id) FROM attendee_info)) AS percentage FROM attendee_info GROUP BY country ORDER BY percentage DESC LIMIT 3;
{"domain":"arts and culture","domain_description":"Audience demographics, event attendance, program impact, and funding sources.","id":1286,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and ordering","s...
a3b27dcf054f2409ca3ef0b1252c1db16b374ee71f2b21f75eb45921ee5e4d6a
gretel_sql:test:1286
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,285
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:939eb10a689dc7ee5910af3e2631fc6fe324d187ca4b02e46aeb86cdde707339
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:8b6199f7be509bfdb02b69909153a02dbada923919ff778c44fc721fdfbc08e4
task:dd65ae24ccfeaf9deb50355ab01bd74336df6c7793e18da45ca0a997029cb989
029152ac5a4f73586513e210e1df3b68b88ad52b6db08a2fc1a4e85033d4646b
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1301
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total quantity of corn sold by farmers in 'Summerfield'?
CREATE TABLE farmers (id INT, name VARCHAR(50), location VARCHAR(50), crops VARCHAR(50)); CREATE TABLE crops (id INT, name VARCHAR(50), yield INT); CREATE TABLE sales (id INT, farmer_id INT, crop_name VARCHAR(50), quantity INT, price DECIMAL(5,2)); INSERT INTO farmers VALUES (1, 'Jane Doe', 'Summerfield', 'Corn'); INSE...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT SUM(quantity) FROM sales INNER JOIN farmers ON sales.farmer_id = farmers.id INNER JOIN crops ON sales.crop_name = crops.name WHERE farmers.location = 'Summerfield' AND crops.name = 'Corn';
{"domain":"agriculture","domain_description":"Comprehensive data on agroecology, food justice, indigenous food systems, and urban agriculture.","id":1301,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query performs an inner join ...
86c05ef3c9666eccccdbc317eef1dc90fe593d5303f2cc9469be06b5f4ef38c1
gretel_sql:test:1301
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,300
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:dd65ae24ccfeaf9deb50355ab01bd74336df6c7793e18da45ca0a997029cb989
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:87e28bf7631d6664e4f85539ca2bb5b6fd275199a3b872fbe5cdd24a2b15cb76
task:3339f470b71c58870339ba40b989859de73c49ab732efa8ee0a6a5c9a079ec6c
c136c6516cc57d182ac637418f3cb3abeef5347f144dec6ee9e50ca3c9b3dfd6
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1303
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average total value of transactions for the top 3 digital assets in the 'Binance Smart Chain' network?
CREATE TABLE binance_transactions (asset_name VARCHAR(20), network VARCHAR(20), transactions_value FLOAT); INSERT INTO binance_transactions (asset_name, network, transactions_value) VALUES ('BNB', 'Binance Smart Chain', 200000), ('ETH', 'Binance Smart Chain', 300000), ('CAKE', 'Binance Smart Chain', 400000);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT asset_name, network, AVG(transactions_value) FROM binance_transactions WHERE network = 'Binance Smart Chain' AND asset_name IN (SELECT asset_name FROM (SELECT asset_name, ROW_NUMBER() OVER (ORDER BY transactions_value DESC) as rn FROM binance_transactions WHERE network = 'Binance Smart Chain') x WHERE rn <= 3) G...
{"domain":"blockchain","domain_description":"Comprehensive data on smart contracts, decentralized applications, digital assets, and regulatory frameworks in blockchain.","id":1303,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, ...
1435f1b77e5a316065c917773514494a3324c255d991599c04e32fa67f4f0af8
gretel_sql:test:1303
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,302
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:3339f470b71c58870339ba40b989859de73c49ab732efa8ee0a6a5c9a079ec6c
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=3;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:4cded537725dc5ab510341218ef158624d6801f127e0e912c1b581a1a396cf46
task:f817c483a186b0c73ce39caa88db2575b17606e7e4845a17c6fd7ccc13acab63
50d7ad974cb3ec769e37adeafb1ff97c1fd59ed1a9d7dabfcd0a6cb6b7195069
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1316
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
List the ports and their average cargo weight for company "HarborLink" in Q2 2017 and Q3 2017.
CREATE TABLE company (id INT, name VARCHAR(255)); INSERT INTO company (id, name) VALUES (1, 'HarborLink'); CREATE TABLE port (id INT, name VARCHAR(255)); CREATE TABLE cargo (id INT, port_id INT, company_id INT, weight INT, quarter INT); INSERT INTO port (id, name) VALUES (1, 'PortA'), (2, 'PortB'), (3, 'PortC'); INSERT...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT port.name, AVG(cargo.weight) FROM port INNER JOIN cargo ON port.id = cargo.port_id AND cargo.quarter IN (2, 3) INNER JOIN company ON cargo.company_id = company.id WHERE company.name = 'HarborLink' GROUP BY port.name;
{"domain":"ocean shipping","domain_description":"Detailed records on cargo handling, fleet management, port operations, and regulatory compliance in ocean shipping.","id":1316,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query c...
bbedad588f3a3452596d0c589c32a4ff72bc43fbeff7e4dfd24821c77866a9c0
gretel_sql:test:1316
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,315
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:f817c483a186b0c73ce39caa88db2575b17606e7e4845a17c6fd7ccc13acab63
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:762a41d3d49035e6cdbda94976579a01299870ba0632587b6a5fa07369ca151c
task:cb9973e895539e119850c9d0aa0f408b8d18a46b81d99ae55ea5b246f03b7253
a0bc3b8291877c9331c13e939f06610a929cea7e3b75ee1b38dedc1715d32ad3
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1321
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
List the top 2 countries with the highest average artifact weight, along with the year and total weight of those artifacts.
CREATE TABLE ExcavationSites (SiteID INT, Country VARCHAR(50), Year INT, ArtifactWeight FLOAT); INSERT INTO ExcavationSites (SiteID, Country, Year, ArtifactWeight) VALUES (1, 'USA', 2020, 23.5), (2, 'Mexico', 2020, 14.2), (3, 'USA', 2019, 34.8), (4, 'Canada', 2019, 45.6), (5, 'Canada', 2019, 56.7);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT Country, Year, SUM(ArtifactWeight) AS TotalWeight, AVG(ArtifactWeight) OVER (PARTITION BY Country) AS AvgWeight FROM (SELECT Country, Year, ArtifactWeight, ROW_NUMBER() OVER (PARTITION BY Country ORDER BY ArtifactWeight DESC) rn FROM ExcavationSites) x WHERE rn <= 2 GROUP BY Country, Year;
{"domain":"archeology","domain_description":"Detailed records on excavation sites, artifact analysis, historical context, and public outreach in archeology.","id":1321,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with p...
c696ef06f37dac90b076553b40e0c07a4a76d6aafa30e18a01af9122c2737db7
gretel_sql:test:1321
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,320
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:cb9973e895539e119850c9d0aa0f408b8d18a46b81d99ae55ea5b246f03b7253
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:e3ecd99a7bf2bd67b72a76d54c988e28c13ee5c918433fc1b23459c1acc48625
task:edc83842f8fafbd94b772ba78b614443a1ffe8907d292b2509d09048c19e99a6
e598fa238ed773468230da7058f1d7206f032a4700f66ebaec9338da3cf8801b
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1367
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Calculate the percentage of vessels in each ocean basin that have outdated engine technology.
CREATE TABLE fleet_information (id INT, vessel_name VARCHAR(255), ocean_basin VARCHAR(255), engine_technology DATE); INSERT INTO fleet_information (id, vessel_name, ocean_basin, engine_technology) VALUES (1, 'Ocean Titan', 'Atlantic', '2000-01-01'), (2, 'Sea Explorer', 'Pacific', '2010-01-01');
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT ocean_basin, PERCENTAGE_RANK() OVER (ORDER BY outdated_engine_count) FROM (SELECT ocean_basin, COUNT(*) FILTER (WHERE engine_technology < '2010-01-01') AS outdated_engine_count FROM fleet_information GROUP BY ocean_basin);
{"domain":"oceanography","domain_description":"Marine life research data, ocean floor mapping projects, pollution control initiatives, and maritime law compliance.","id":1367,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.)...
c87e5117c200a782cabc93bf714049908f20ffa77eff37127faf7d58b5c2467b
gretel_sql:test:1367
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,366
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:edc83842f8fafbd94b772ba78b614443a1ffe8907d292b2509d09048c19e99a6
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:6a3cdfe57773484f1b9a61de4b9f559623407a67c90d1949985030e3c1f7631d
task:fc94554297817190d67058846e324366315728d4159d79425cf9ccee7d970a7e
8c7748ec80232bf76d6e89a057bddb591677b21f415a773de80288f583b5927d
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1510
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total number of visitors from African countries who attended events in 2021?
CREATE TABLE events (event_id INT, event_name VARCHAR(50), event_year INT, location VARCHAR(50)); INSERT INTO events (event_id, event_name, event_year, location) VALUES (1, 'Music Festival', 2021, 'Nigeria'), (2, 'Art Exhibition', 2022, 'Egypt'), (3, 'Theater Performance', 2021, 'South Africa'); CREATE TABLE countries ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT COUNT(*) FROM attendees JOIN events ON attendees.event_id = events.event_id JOIN countries ON attendees.country_id = countries.country_id WHERE events.event_year = 2021 AND countries.continent = 'Africa';
{"domain":"arts and culture","domain_description":"Audience demographics, event attendance, program impact, and funding sources.","id":1510,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"First, we join the attendees, events, and countr...
061f9414ff6fa38423e0203736d47a4b0a4f8dbc180b7088fb672d799f2fce71
gretel_sql:test:1510
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,509
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:fc94554297817190d67058846e324366315728d4159d79425cf9ccee7d970a7e
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:de6d37a92c3ff155f2d04d585285955980637b82bef19a0b350ca72c3347b1b2
task:6a323d2babdc9b885be5d156e2f009b6ce8e0c26ea3a6b36c1b4f23326e939be
a07a6258eb5cd19a0234ac344221d4fe0c7624bd7538fd4239fe552877964167
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1515
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the average age of clients who lost cases in the 'personal injury' category?
CREATE TABLE Cases (CaseID int, ClientID int, Category varchar(50)); INSERT INTO Cases (CaseID, ClientID, Category) VALUES (701, 7, 'Personal Injury'); CREATE TABLE Clients (ClientID int, Age int, Gender varchar(10)); INSERT INTO Clients (ClientID, Age, Gender) VALUES (7, 45, 'Male'); CREATE TABLE CaseOutcomes (CaseID ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT AVG(C.Age) as AvgAge FROM Clients C INNER JOIN Cases CA ON C.ClientID = CA.ClientID INNER JOIN CaseOutcomes CO ON CA.CaseID = CO.CaseID WHERE CA.Category = 'Personal Injury' AND CO.Outcome = 'Lost';
{"domain":"legal services","domain_description":"Case outcomes, legal precedents, attorney performance metrics, client demographics, and billing information.","id":1515,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query finds th...
45e914721c988f5409960a8d80da4230fce881d47ee66a0c493a3d7c2a1fb4ac
gretel_sql:test:1515
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,514
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:6a323d2babdc9b885be5d156e2f009b6ce8e0c26ea3a6b36c1b4f23326e939be
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:ef79c30a0789531703b8eddb68073b5375da80666becd02149f23ff2c0031af4
task:26fc01a2e492f7de5be58c73ca71b71bf70c516071ede4b4acea04ff6992a246
a3622d5b31cb16418d3e1e521bfd13eb14ee998344830a548993cc6fd511c039
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1531
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
How many transactions were made in each region for the 'Credit Cards' product type?
CREATE TABLE regions (id INT, region_name VARCHAR(50)); INSERT INTO regions (id, region_name) VALUES (1, 'Northeast'), (2, 'Southeast'); CREATE TABLE transactions (region_id INT, product_type_id INT, transaction_count INT); INSERT INTO transactions (region_id, product_type_id, transaction_count) VALUES (1, 1, 20), (1, ...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT r.region_name, p.product_type, SUM(t.transaction_count) as total_transactions FROM regions r JOIN transactions t ON r.id = t.region_id JOIN product_types p ON t.product_type_id = p.id WHERE p.product_type = 'Credit Cards' GROUP BY r.region_name, p.product_type;
{"domain":"financial services","domain_description":"Detailed financial data including investment strategies, risk management, fraud detection, customer analytics, and regulatory compliance.","id":1531,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_...
0d900449eb2a652885116c659921c5b31cb24ea676531ef7b767aa33ea3be555
gretel_sql:test:1531
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,530
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:26fc01a2e492f7de5be58c73ca71b71bf70c516071ede4b4acea04ff6992a246
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:a03e79bd8209d499ff19973e14231b79b4cb1545a9d064ad5a0d971e4cb433fc
task:f7b1bf27656cb27292cf90eb7fef0ee661651d5c35ae8011ea319b3c49a38ae0
781fcdffd244553d8701b9d474f72b3dae1f423888bc4cbf721b38ee331358eb
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1562
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total CO2 emission of each menu item, considering its ingredients and their origin?
CREATE TABLE menu_items (menu_id INT, name VARCHAR(50), co2_emission FLOAT); CREATE TABLE ingredients (ingredient_id INT, name VARCHAR(50), origin VARCHAR(50), co2_emission_per_kg FLOAT); CREATE TABLE recipe (menu_id INT, ingredient_id INT, quantity FLOAT);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT m.name, SUM(i.co2_emission_per_kg * r.quantity) as total_co2_emission FROM menu_items m JOIN recipe r ON m.menu_id = r.menu_id JOIN ingredients i ON r.ingredient_id = i.ingredient_id GROUP BY m.menu_id;
{"domain":"food services","domain_description":"Menu engineering, customer preferences, inventory management, and sustainability initiatives.","id":1562,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQL query joins the menu_items,...
1875055bfcec78d42960e73e1923895715a1fa5cf6b4cae67be64e711363c2d5
gretel_sql:test:1562
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,561
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:f7b1bf27656cb27292cf90eb7fef0ee661651d5c35ae8011ea319b3c49a38ae0
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:17792feb0a5139588e62172c1cc3bb87e05d71b8d776d64aa740dabcfca85719
task:7a2c93c498aa9fdb7b1071cd589756b310e45021cedd69eed22e6bb1e28c4a80
4538afbbbc45752bc61243c62d346e948e662f07752ca6e98c59afd84ef322d4
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1584
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What are the names of the vessels with the highest average speed that arrived in Busan?
CREATE TABLE VesselArrivals (ID INT, VesselName VARCHAR(50), ArrivalPort VARCHAR(50), ArrivalDate DATE, AverageSpeed DECIMAL(5,2)); INSERT INTO VesselArrivals (ID, VesselName, ArrivalPort, ArrivalDate, AverageSpeed) VALUES (1, 'Test Vessel 1', 'Busan', '2022-01-01', 20.0), (2, 'Test Vessel 2', 'Busan', '2022-01-02', 18...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT VesselName FROM (SELECT VesselName, ROW_NUMBER() OVER (ORDER BY AverageSpeed DESC) AS rn FROM VesselArrivals WHERE ArrivalPort = 'Busan') t WHERE rn = 1;
{"domain":"maritime","domain_description":"Vessel performance data, cargo tracking, safety records, and regulatory compliance.","id":1584,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and ordering","sql...
9122ca8044d6a310183df14adb53a14832637af95426a3b45ddf0793998b4e5e
gretel_sql:test:1584
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,583
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:7a2c93c498aa9fdb7b1071cd589756b310e45021cedd69eed22e6bb1e28c4a80
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:e328b528b1500e06131037295b1af701c8e1421c8caf806677f5f55e13b239cb
task:d99418893fc67eab0e4011e5aad6940506d35ae6fff3405b5b08e8d3313cd2a4
556ad253c9b5b9759d9a323fb0e22c66f04ee6c1f7373200051e5d83834d771a
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1610
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the number of primary care physicians per capita in each state of the United States?
CREATE TABLE us_states (id INT, name VARCHAR(255)); CREATE TABLE primary_care_physicians (id INT, state_id INT, count INT); CREATE TABLE population (id INT, state_id INT, total_population INT); INSERT INTO us_states (id, name) VALUES (1, 'Alabama'), (2, 'Alaska'), (3, 'Arizona'), (4, 'Arkansas'), (5, 'California');
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT s.name, pc.count / p.total_population AS physicians_per_capita FROM primary_care_physicians pc JOIN us_states s ON pc.state_id = s.id JOIN population p ON pc.state_id = p.state_id;
{"domain":"public health","domain_description":"Community health statistics, infectious disease tracking data, healthcare access metrics, and public health policy analysis.","id":1610,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This...
e8a5afbee2ce4c58702ebfadfd9e358a70490e21eaeaf43c480eb738b2b47993
gretel_sql:test:1610
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,609
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:d99418893fc67eab0e4011e5aad6940506d35ae6fff3405b5b08e8d3313cd2a4
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:04eb006484af290c371699dbd4c5c4dc8954750e1c41efad7a57d89e158d076b
task:fbaca957d2329d4181b84f5712d9d34a05ed8780473579ad60ded5acaa1a500d
ef9a5e5718b0f1d4f4abe5d1e1c9682c5b719cb8092d6816fb9ad416607caa52
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1669
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Which countries have the highest and lowest ocean acidity levels?
CREATE TABLE ocean_acidity (country TEXT, avg_ph REAL); INSERT INTO ocean_acidity (country, avg_ph) VALUES ('United States', 7.8), ('Canada', 7.6), ('Mexico', 7.9);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT country, avg_ph FROM (SELECT country, avg_ph, ROW_NUMBER() OVER (ORDER BY avg_ph DESC) as rnk FROM ocean_acidity) subq WHERE rnk = 1 OR rnk = (SELECT COUNT(*) FROM ocean_acidity) ORDER BY avg_ph;
{"domain":"oceans","domain_description":"Ocean data on marine conservation, ocean acidification, deep-sea exploration, and maritime safety.","id":1669,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and o...
4f013a3cdceba239d4648392d0c92cbe6d0d21ceac5864c026e423f894450e39
gretel_sql:test:1669
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,668
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:fbaca957d2329d4181b84f5712d9d34a05ed8780473579ad60ded5acaa1a500d
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=3;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:c0e478e277636d4ded47904f433733506cfa4cb5de5a26d9a5980e2af4d6804d
task:1cbd92181b981f0d989be02ec431a7c59a3efc3aa3ada1a26370b30542ee757a
b362c64856db8b1a86006db6e086da870e4a79ba72cb4bb88ff89994a38e60f2
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1672
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
Identify the top 3 countries with the highest number of players who have adopted virtual reality technology.
CREATE TABLE CountryData (Country VARCHAR(50), Population INT, VRAdopters INT); INSERT INTO CountryData (Country, Population, VRAdopters) VALUES ('USA', 331002651, 50000), ('China', 1439323776, 25000), ('Canada', 37410003), ('India', 1380004385, 10000);
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT Country, VRAdopters FROM (SELECT Country, VRAdopters, ROW_NUMBER() OVER (ORDER BY VRAdopters DESC) AS RN FROM CountryData JOIN (SELECT PlayerID, VRDevice FROM VRAdoption GROUP BY PlayerID, VRDevice) VR ON CountryData.Country = VR.PlayerCountry) T WHERE RN <= 3
{"domain":"gaming technology","domain_description":"Player demographics, game design data, esports event information, and virtual reality technology adoption.","id":1672,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with...
3068b9cd7ccf2b89247cb76954700b5f80b5bf7d549feefcb041ddeee45de519
gretel_sql:test:1672
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,671
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:1cbd92181b981f0d989be02ec431a7c59a3efc3aa3ada1a26370b30542ee757a
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=3;joins=1;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:e6d059f342cfb9d93e11ad20293db21f723c31571a68cd472283aad509d429a7
task:fd78f98f90969e07e25286eef9e2323a4edc8a80b1c7effc65a247928fa03caf
1ad1ade9e9912e3fe9761be4c2cbd7bcfe918b3ebff5006af68afe8f8eaedd63
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1751
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the maximum consecutive number of days with a financial wellbeing score below 60 for each customer?
CREATE TABLE customer_scores (customer_id INT, score_date DATE, financial_wellbeing_score INT); INSERT INTO customer_scores (customer_id, score_date, financial_wellbeing_score) VALUES (1, '2021-01-01', 65), (1, '2021-01-02', 60), (1, '2021-01-03', 55), (1, '2021-01-04', 60), (2, '2021-01-01', 70), (2, '2021-01-02', 75)...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT customer_id, MAX(consecutive_below_60) FROM (SELECT customer_id, score_date, financial_wellbeing_score, COUNT(*) FILTER (WHERE financial_wellbeing_score < 60) OVER (PARTITION BY customer_id ORDER BY score_date ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS consecutive_below_60 FROM customer_scores) subquer...
{"domain":"finance","domain_description":"Financial data on Shariah-compliant finance, socially responsible lending, financial capability, and financial wellbeing.","id":1751,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.)...
d1919d3b018e586512741aa56bc453592d712354910f360d2c0a55fab447abb4
gretel_sql:test:1751
window functions
intermediate
Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,750
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:fd78f98f90969e07e25286eef9e2323a4edc8a80b1c7effc65a247928fa03caf
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:window functions", "sql_structure:selects=2;joins=0;statements=1;advanced=OVER", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
gretel_sql:cde37366c9a77951ab03d4af9e2e0a8cb805cf3a54c0f932e333137b1870538e
task:fcaa3dc216b5f2edc188454a81e45b073278a6294b32f28ab7636896e40ccd40
e8d13bf1b649c99ca3737287e6957bb2fefdd4c1374842a512c0fa1c234b11c3
gretel_sql
gretelai/synthetic_text_to_sql
740ab236e64503fba51be1101df7a1be83bf455d
synthetic_text_to_sql_test.snappy.parquet
1763
test
https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet
apache-2.0
Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest.
proposed
What is the total food and beverage revenue last month for hotels in 'Bangkok'?
CREATE TABLE revenue (hotel_id INT, revenue_source VARCHAR(50), revenue INT, revenue_date DATE); INSERT INTO revenue (hotel_id, revenue_source, revenue, revenue_date) VALUES (5, 'Room revenue', 12000, '2022-03-01'), (5, 'Food and beverage', 4000, '2022-03-02'), (5, 'Other revenue', 1000, '2022-03-03'); CREATE TABLE hot...
database_business_operations
database_business_operations.database_query_analysis
heuristic_unreviewed
Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review.
en
none
SELECT SUM(revenue) FROM revenue JOIN hotels ON revenue.hotel_id = hotels.hotel_id JOIN dates ON revenue.revenue_date = dates.date WHERE hotels.city = 'Bangkok' AND revenue_source = 'Food and beverage' AND dates.date >= DATEADD(month, -1, GETDATE());
{"domain":"hospitality technology","domain_description":"Hotel tech adoption metrics, online travel agency data, virtual tour engagement stats, and hospitality AI trends.","id":1763,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQ...
7523c6adc7cb6d6bbfe300c5712a1a2b08b05b868db61795f894fcb1ef13b685
gretel_sql:test:1763
multiple_joins
intermediate
Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed.
heuristic
needs_difficulty_calibration
not_measured
analysis_only_unvalidated
primary
task_annotation
1,762
[ "difficulty_not_calibrated", "not_individually_reviewed", "reference_not_independently_verified", "reference_or_constraint_only_no_rubric", "response_diversity_not_validated", "source_evaluation_split", "sql_dialect_and_result_equivalence_need_review", "sql_not_executed", "synthetic_reference" ]
false
true
false
task:fcaa3dc216b5f2edc188454a81e45b073278a6294b32f28ab7636896e40ccd40
1
1
[ "gretel_sql" ]
held_out
curated_candidate
[]
core_candidate
[ "source_sql_complexity:multiple_joins", "sql_structure:selects=1;joins=2;statements=1;advanced=none", "combined_relational_operations_require_calibration" ]
structural_heuristic
Qwen3.6-27B
heuristic_not_model_measured
End of preview. Expand in Data Studio

Rubric Diversity Tasks

The default qwen_core configuration contains 1,296,719 curated task groups after screening 108,713 foundational tasks into the separate foundational configuration. The tasks configuration retains all 1,405,432 curated task groups.

Difficulty is labeled for a Qwen3.6-27B curriculum using transparent source and structural priors. No new Qwen3.6-27B success rates were measured. core_candidate means an explicit nonfoundational source/structure signal; uncalibrated means the available evidence does not justify a difficulty claim. Both remain in the default view. The default view is a difficulty-screened research inventory; held-out source lineages remain labeled and must still be separated for evaluation.

A curated inventory of 1,405,432 task groups, with 2,444,946 source annotation records retained across 20 source configurations. All source releases are revision-pinned where upstream provenance is known.

The tasks configuration contains one representative eligible annotation per exact prompt/context group. Environment tasks additionally include source and upstream environment identity in their task key. This is a public research inventory for rubric discovery. It does not contain newly accepted rubric training targets or certify that each task is solvable.

The audit quarantined 538,621 annotations. 9,742 eligible task groups have held-out source lineage and are marked source_split_status=held_out. No model inference, empirical difficulty calibration, or full task-environment execution was performed.

from datasets import load_dataset

tasks = load_dataset("asingh15/rubric-diversity-tasks", "qwen_core", split="collection", streaming=True)
example = next(iter(tasks))
# Full source annotations, including alternate representations:
source = load_dataset("asingh15/rubric-diversity-tasks", "gsm8k", split="collection")

Configurations

  • qwen_core (default): curated task groups excluding the foundational screen; includes explicitly labeled uncalibrated tasks.

  • foundational: basic/easy task candidates retained as controls and for calibration.

  • tasks: one representative per eligible task group. Prefer native rubrics, then supplied references and verifiers; alternate references remain in source configurations.

  • quarantine: all excluded annotations, with explicit reasons. These also remain in their source configurations.

  • Source configurations: every normalized annotation from the selected complete upstream releases, without privacy redaction. Source response expansions and alternate representations are preserved.

All configurations use the neutral split name collection. Original split names remain in source_split; they have not been relabeled as training data. source_split_status is held_out, source_train, or unspecified. Held-out protection propagates to exact shared prompts and declared upstream task identities until stable. It is a conservative overlap check, not a comprehensive benchmark decontamination guarantee.

Curation

Every shard is checked for schema, row counts, SHA-256, source coordinates, record-ID uniqueness, prompt/task fingerprints, JSON fields, and reference/verifier flags. Source adapters audit extraction and preserve references separately from task-visible inputs. Missing prompts, unresolved upstream provenance, source-invalid problems/solutions/rubrics, known defective references, external-image dependencies, unavailable internal-registry environments and records without reference evidence are excluded from the default task view. See curation_reasons, quality_flags, and audit/quality_report.json.

Deduplication is exact and preserves case, code indentation and internal whitespace. It normalizes Unicode NFC, line endings and surrounding whitespace only. Task counts are exact-input groups, not claims of semantically distinct problems. The per-source annotations retain all grading perspectives, responses and representations.

Fields

  • prompt, context: task input, with source roles/tools retained where applicable.
  • rubric_markdown, rubric_json: source rubric material; absence does not trigger an invented generic rubric.
  • reference_solution, reference_answer, verifier_json: source evidence, not independently certified outcomes.
  • source_repo, source_revision, source_file, source_row, source_row_id, source_url, source_record_sha256: provenance.
  • task_id, prompt_context_id, record_id, annotation_count, eligible_annotation_count, group_sources: stable identities and exact grouping. Source configuration rows use the same task key as the default view.
  • family, leaf, taxonomy_status, taxonomy_reason: provisional taxonomy. Unassigned tasks remain unassigned.
  • source_difficulty, estimated_difficulty, difficulty_basis, difficulty_confidence, empirical_difficulty: source labels/heuristics are distinct from unmeasured solver difficulty.
  • curation_status, curation_reasons, source_split_status, quality_flags, validation_status: eligibility and limitations. curated_candidate means structurally admitted for research, not accepted for training.

Source coverage

Configuration Annotations Eligible annotations Quarantined
auto_rubric 38,459 35,408 3,051
deepresearch_bench_ii 132 132 0
facet_terminal 6,020 6,020 0
gretel_sql 105,851 105,851 0
gsm8k 17,584 17,584 0
helpsteer3 132,937 0 132,937
junkai 13,045 11,076 1,969
mbpp 1,401 1,399 2
mimo 7,780 7,780 0
nemotron_science 150,644 150,644 0
nemotron_structured 62,696 62,696 0
nemotron_terminal 254,702 5 254,697
numinamath 896,215 761,733 134,482
prometheus_feedback 99,952 99,952 0
prometheus_preference 199,760 199,760 0
researchrubrics 101 101 0
rubrichub 364,260 364,260 0
swe_rebench 32,079 32,033 46
swe_smith 50,908 39,471 11,437
arxivmath 10,420 10,420 0

Limits and attribution

Task-level taxonomy, rubric grounding, alternative-solution acceptance and difficulty remain unverified unless specifically documented by a source audit. Terminal/repository tasks retain environment pointers and verifier text; complete executable environments are not bundled. Original source benchmarks, licenses, author attribution and repository dependencies remain applicable. DeepResearch Bench II is pinned to its original GitHub release; all its tasks retain evaluation-source protection and per-record licenses, including two noncommercial records.

See LICENSES.md, source audits, manifests, and the reproducible curation scripts. The small interactive Rubric Diversity Atlas is a separate illustrative gallery.

Difficulty for Qwen3.6-27B

Each curated task has difficulty_tier, difficulty_evidence, difficulty_tier_confidence, difficulty_target_model, and difficulty_validation. Original source difficulty metadata is preserved. Source configurations retain their original difficulty columns; the new screen is applied to unique curated task groups.

GSM8K and MBPP supply foundational benchmark controls. Explicit basic/easy source labels and conservative task-structure checks provide additional foundational candidates. Competition/source labels alone do not establish target-model difficulty. Missing labels stay uncalibrated; prompt length, rubric count and schema size are not treated as difficulty measurements. See the difficulty audit and the exact rules.

A calibrated revision would record a pinned Qwen checkpoint, multiple independent attempts, outcome-verified success criteria, and per-task pass rates. No such model run is claimed by this release.

MathArena outputs and source-model evidence

arxivmath preserves all 10,420 captured Qwen3.6-35B attempts from the pinned outputs release, grouped into 1,649 distinct questions. An upstream indexing bug repeated one question under several problem indices; 956 questions in the parent training set have no outputs. The outputs source is complete as published, but it does not cover every parent question.

Gold answers are separate from sampled responses and conversations. source_metadata_json.source_reported_baseline and empirical_difficulty retain per-question source-reported attempt counts, correctness counts and pass rates. These describe Qwen3.6-35B, with no claim of independent samples or measured Qwen3.6-27B performance. Each question has 4 or 12 captured attempts; 725 questions have zero source-reported successes and 378 have all attempts reported correct.

Output annotations carry CC-BY-4.0. Matched article URLs, article-license terms and restriction flags remain in source_metadata_json.licensing, source_metadata_json.source_article, and quality_flags. See the MathArena audit.

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