Recognises a fact type with clashing modalities
One fact type constrained both alethically (cannot be otherwise) and deontically (ought to be so). One of the two is misclassified.
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 2 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 90% | 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 | 8% | Not shown |
| gpt-6-luna | Low cost | 89% | Not shown |
| claude-haiku-5-5 | Low cost | 90% | 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: The two rules and the one misclassified
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: it is necessary that each IBAN is unique and it is obligatory that each IBAN is unique on the same fact type Acceptable: Decide using Witt's test: data can contain duplicates, so it is deontic — obligatory, and enforced (ANO-008)
Also known as: Clashing modality (data rule)
Related knobits
Builds on
All 23 knobits of Document data rules in SBVR.
Source: Review checklist 02-data-rules (data-quality), row 7; formerly data-modelling K-307