Recognises a constraint deferred without a check
A rule that should be a constraint but cannot be created yet because legacy rows violate it, and that has become nothing in the meantime. Deferral without a check is retirement nobody announced.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 26% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 84% | Not shown |
| gpt-6-astra* | Flagship | 100% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 100% | Mastered |
| gpt-6.1-sol* | Mid tier | 100% | Mastered |
| claude-sonnet-5-5* | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 8% | Not mastered |
| gpt-6-luna | Low cost | 22% | Not shown |
| claude-haiku-5-5 | Low cost | 23% | Not shown |
* From the earlier benchmark of 2026-10: 10 data-modelling test tasks, 3 runs per task, graded by claude-opus-5-5. These models have not taken the current 123 tasks yet; the two methods differ, so compare their figures with care. They are left out of the “most models” counts and the best-model picks.
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The constraint and the check tracking it until created
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: BR-007: each account has an IBAN; 3 % of migrated rows have none, so account_identifier was made nullable "for now"; no DQ rule, no date Acceptable: DQ-009 at threshold: "97%", a comment naming the repair work and the date the column becomes NOT NULL and the threshold 100 %
Also known as: Constraint deferred because the data already breaks it (data quality rule)
Related knobits
Builds on
- K-356 Explains rule enforcement mechanisms and platform enforcement
- Q-353 Explains the population, measure and threshold of a check
All 30 knobits of Derive data quality rules.
Source: Review checklist 03-dq-rules (data-quality), row 3; DMBOK2; Loshin 2011; formerly data-modelling K-703