Recognises a data-update rule with no enforcing or detecting mechanism
A state-transition or monotonic rule in a declarative schema, which cannot express it.
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 | 86% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 98% | Mastered |
| gpt-6-astra* | Flagship | 99% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 99% | Mastered |
| gpt-6.1-sol* | Mid tier | 98% | Mastered |
| claude-sonnet-5-5* | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 49% | Not shown |
| gpt-6-luna | Low cost | 36% | Not shown |
| claude-haiku-5-5 | Low cost | 45% | 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 rule and the mechanism to enforce it or detect a breach
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: "Status may go Open → Closed but never back", enforcement: model Acceptable: enforcement: application, etl or dq-check, naming what implements it
Also known as: Data-update rule with no mechanism (data rule); Update rule handed to a mechanism nobody verifies (data quality rule)
Related knobits
Builds on
- K-355 Explains Witt's classification of business rules
- K-356 Explains rule enforcement mechanisms and platform enforcement
All 23 knobits of Document data rules in SBVR.
Source: Review checklist 02-data-rules (data-quality), row 14; formerly data-modelling K-314