Explains SBVR fact types and rule statements
Fact types relate glossary terms; rule statements constrain fact types.
Can AI do this?
AI does this well Most of the tested AI models master this. 9 of 10 models 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 | 100% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
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: Fact types relate glossary terms; rule statements constrain fact types
How it is assessed: Writes sample rules as a fact type and a rule statement
Related knobits
Needed for
- K-308 Recognises one relationship stated as two fact types
- K-352 Explains alethic and deontic modality of rules
- K-353 Explains logical relations between rules
- K-907 Explains fact-based modelling
All 28 knobits of Document data rules as SBVR fact types and rule statements.
Source: Data-management practice used by the stage 3 reference and review checklist