Recognises overlapping value-set rules on one fact type
Two value-set rules on one fact type. The permitted set becomes their intersection, which is never what either author meant.
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 | 72% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 100% | Mastered |
| gpt-6-astra* | Flagship | 100% | Mastered |
| claude-fable-5-1* | Flagship | 84% | Not shown |
| 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 | 42% | Not shown |
| gpt-6-luna | Low cost | 71% | Not shown |
| claude-haiku-5-5 | Low cost | 71% | 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 two sets and their unintended intersection
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: BR-6: account status is one of OPN, DOR, CLS; BR-8: account status is one of OPN, DOR, CLS, BLK Acceptable: One value set, versioned, owned by a steward; if the narrower set is a project-phase decision, say so in the rule (ANO-005)
Also known as: Overlapping value sets (data rule)
Related knobits
Builds on
- Q-253 Explains logical relations between rules
- Q-1803 Explains the meaning of the set operations union and intersection
All 23 knobits of Document data rules in SBVR.
Source: Review checklist 02-data-rules (data-quality), row 5; formerly data-modelling K-305