Recognises redundant data rules
Two rules of the same classification and subtype on the same fact type, differing only in wording. Either duplicates, or they differ by a condition nobody recorded.
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 | 82% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 100% | Mastered |
| 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 | 46% | Not shown |
| gpt-6-luna | Low cost | 84% | Not shown |
| claude-haiku-5-5 | Low cost | 83% | 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 redundant pair and the one to retire, or the unrecorded condition
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: BR-3: each payment must specify an account; BR-7: a payment must always identify its account Acceptable: One rule, one id, cited by both requirements (ANO-002)
Also known as: Redundancy (data rule)
Related knobits
Builds on
Usually followed by
All 23 knobits of Document data rules in SBVR.
Source: Review checklist 02-data-rules (data-quality), row 3; formerly data-modelling K-303