Explains logical relations between rules
Contradiction, redundancy, subsumption and vacuity in a rule set.
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| 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 | 96% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
* 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: Contradiction, redundancy, subsumption and vacuity in a rule set
How it is assessed: Finds the logical relations between sample rules
Related knobits
Builds on
Needed for
- Q-201 Recognises contradicting data rules
- Q-202 Recognises a data rule no valid data can satisfy
- Q-203 Recognises redundant data rules
- Q-204 Recognises a data rule implied by another
- Q-205 Recognises overlapping value-set rules on one fact type
- Q-237 Resolves contradicting rules by adding the missing condition
All 23 knobits of Document data rules in SBVR.
Source: Data-management practice used by the stage 2 reference and review checklist; formerly data-modelling K-353