✔ PASS
Temporal & Time-Series
task_117_bitemporal_retroactive_audit
Scaled: 1M+ Records
Ordered: Yes
Bitemporal Auditing: Retroactive Policy Revision Point-in-Time State
Candidate Latency (1M Rows)
0.13 ms
(1.2x slower than golden)
Golden Latency (1M Rows)
0.11 ms
DuckDB in-memory engine
Underlying Data Volume
1,000,000 rows
45.8 MiB across 1 table
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 4.5s
✔ Correctness Verified on 1M+ Records
Candidate SQL output matches ground truth golden query across all multiset rows and numeric tolerances (tol=1e-3). Zero data skew or anti-patterns detected on full dataset scale.
1. Analytical Significance & What It Means
Scenario & Frontier TrapBusiness Scenario & Problem
Reconstructs historical corporate insurance coverage on an effective business date, as known to the system on an audit date prior to a retroactive backdated modification.
⚡ The Frontier Trap (Why LLMs Fail)
Frontier models ignore the second time dimension (system transaction time) and only filter on valid_from / valid_to. This incorrectly matches the retroactive modification ($500), which was not entered into the database until May 1st, failing regulatory compliance auditing.
Prompt / User Request Given to Model
Determine customer 501's insurance deductible: 1. Effective as of business date: 2026-04-10 2. As known by the system on system audit date: 2026-04-15 (strictly BEFORE the retroactive update entered into the database on 2026-05-01). Required output columns: - customer_id (INT) - active_deductible (DECIMAL) Order by customer_id ASC.
2. Underlying Tables, Row Counts & Data Volume
1 Tables (1,000,000 rows • 45.8 MiB)Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:
📊 insurance_policy_bitemporal
1,000,000 rows
45.8 MiB Data Volume
policy_id (INTEGER) customer_id (INTEGER) deductible (DECIMAL(10,2)) valid_from (DATE) valid_to (DATE) system_from (TIMESTAMP) system_to (TIMESTAMP)Data Preview (showing sample rows from 1,000,000 total rows):
| # | policy_id | customer_id | deductible | valid_from | valid_to | system_from | system_to |
|---|---|---|---|---|---|---|---|
| 1 | 1 | 501 | 1,000 | 2026-01-01 00:00:00 | 2026-12-31 00:00:00 | 2026-01-01 00:00:00 | 2026-05-01 12:00:00 |
| 2 | 1 | 501 | 500 | 2026-04-01 00:00:00 | 2026-12-31 00:00:00 | 2026-05-01 12:00:00 | 9999-12-31 23:59:59 |
| 3 | 1 | 501 | 1,041.45 | 2026-01-01 00:00:00 | 2026-01-01 00:00:00 | 2026-03-01 09:30:00.000007 | 2026-03-01 09:30:00.000007 |
| 4 | 2 | 502 | 542.39 | 2026-01-02 00:00:00 | 2026-01-02 00:00:00 | 2026-03-01 09:30:00.000025 | 2026-03-01 09:30:00.000025 |
| 5 | 3 | 503 | 82.55 | 2026-01-03 00:00:00 | 2026-01-03 00:00:00 | 2026-03-01 09:30:00.000055 | 2026-03-01 09:30:00.000055 |
| 6 | 4 | 504 | 1,027.8 | 2026-01-04 00:00:00 | 2026-01-04 00:00:00 | 2026-03-01 09:30:00.000066 | 2026-03-01 09:30:00.000066 |
| 7 | 5 | 505 | 826.89 | 2026-01-05 00:00:00 | 2026-01-05 00:00:00 | 2026-03-01 09:30:00.000082 | 2026-03-01 09:30:00.000082 |
| 8 | 6 | 506 | 1,692.24 | 2026-01-06 00:00:00 | 2026-01-06 00:00:00 | 2026-03-01 09:30:00.000121 | 2026-03-01 09:30:00.000121 |
| 9 | 7 | 507 | 652.93 | 2026-01-07 00:00:00 | 2026-01-07 00:00:00 | 2026-03-01 09:30:00.000122 | 2026-03-01 09:30:00.000122 |
| 10 | 8 | 508 | 1,141.76 | 2026-01-08 00:00:00 | 2026-01-08 00:00:00 | 2026-03-01 09:30:00.000149 | 2026-03-01 09:30:00.000149 |
| 11 | 9 | 509 | 902.09 | 2026-01-09 00:00:00 | 2026-01-09 00:00:00 | 2026-03-01 09:30:00.000163 | 2026-03-01 09:30:00.000163 |
| 12 | 10 | 510 | 404.57 | 2026-01-10 00:00:00 | 2026-01-10 00:00:00 | 2026-03-01 09:30:00.000188 | 2026-03-01 09:30:00.000188 |
| 13 | 11 | 511 | 470.58 | 2026-01-11 00:00:00 | 2026-01-11 00:00:00 | 2026-03-01 09:30:00.000206 | 2026-03-01 09:30:00.000206 |
| 14 | 12 | 512 | 808.14 | 2026-01-12 00:00:00 | 2026-01-12 00:00:00 | 2026-03-01 09:30:00.000221 | 2026-03-01 09:30:00.000221 |
| 15 | 13 | 513 | 639.63 | 2026-01-13 00:00:00 | 2026-01-13 00:00:00 | 2026-03-01 09:30:00.000242 | 2026-03-01 09:30:00.000242 |
3. Graphical Query Execution Plan & Cost Bottlenecks
Physical Operator Tree (DuckDB 2.0 Profiler)Visual flowchart of the physical execution pipeline. Operators with high relative costs (≥ 20% of query runtime or heavy latency) are visually highlighted:
10.3%
Latency: 0.01 ms
Rows In/Out: 1
Data Vol: 12 B
📁 Projections: ['__internal_decomp...
61.8%
Latency: 0.08 ms
Rows In/Out: 1
Data Vol: 9 B
2.3%
Latency: 0.00 ms
Rows In/Out: 1
Data Vol: 9 B
📁 Projections: ['__internal_compre...
25.6%
Latency: 0.03 ms
Rows In/Out: 1
Data Vol: 12 B
📁 memory.main.insurance_policy_bit...
| Operator | Cost % | Latency | Processed Rows | Data Volume | Details / Table | Status |
|---|---|---|---|---|---|---|
| ORDER_BY | 61.8% | 0.08 ms | 1 | 0.00 MB | - | Normal |
| TABLE_SCAN | 25.6% | 0.03 ms | 1 | 0.00 MB | memory.main.insurance_policy_bitemporal | Normal |
| PROJECTION | 10.3% | 0.01 ms | 1 | 0.00 MB | Projections: ['__internal_decompress_integral_in | Normal |
| PROJECTION | 2.3% | 0.00 ms | 1 | 0.00 MB | Projections: ['__internal_compress_integral_utin | Normal |
4. Queries Executed (Candidate vs Golden Ground Truth)
Beautified & Syntax HighlightedSELECT customer_id,
deductible AS active_deductible
FROM insurance_policy_bitemporal
WHERE customer_id = 501 -- Business valid-time check as of 2026-04-10
AND DATE '2026-04-10' >= valid_from
AND DATE '2026-04-10' < valid_to -- System audit / transaction-time check as of 2026-04-15
AND TIMESTAMP '2026-04-15 00:00:00' >= system_from
AND TIMESTAMP '2026-04-15 00:00:00' < system_to
ORDER BY customer_id ASC;
SELECT customer_id,
deductible AS active_deductible
FROM insurance_policy_bitemporal
WHERE customer_id = 501 -- Business valid time: 2026-04-10
AND DATE '2026-04-10' >= valid_from
AND DATE '2026-04-10' < valid_to -- System knowledge time: 2026-04-15
AND TIMESTAMP '2026-04-15 00:00:00' >= system_from
AND TIMESTAMP '2026-04-15 00:00:00' < system_to
ORDER BY customer_id ASC;
CREATE TABLE insurance_policy_bitemporal (policy_id INT, customer_id INT, deductible decimal(10, 2),
valid_from DATE, valid_to DATE, system_from TIMESTAMP, system_to TIMESTAMP);
INSERT INTO insurance_policy_bitemporal
VALUES -- Original policy entered on 2026-01-01 ($1000 deductible)
(1, 501, 1000.00, '2026-01-01', '2026-12-31', '2026-01-01 00:00:00', '2026-05-01 12:00:00'),
-- Retroactive change entered on 2026-05-01 backdating deductible to $500 effective from 2026-04-01
(1, 501, 500.00, '2026-04-01', '2026-12-31', '2026-05-01 12:00:00', '9999-12-31 23:59:59');
5. Query Results & Expected Ground Truth
Tabular Comparison
Actual Candidate Output
(1 rows × 2 cols)
| # | customer_id | active_deductible |
|---|---|---|
| 1 | 501 | 1,000 |
Expected Golden Output
(1 rows × 2 cols)
| # | customer_id | active_deductible |
|---|---|---|
| 1 | 501 | 1,000 |