Explains the implementation options for subtypes
Roll up, roll down or one table per type, and the constraints each loses.
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 4 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview | Flagship | 50% | Not shown |
| gpt-6-astra | Flagship | 100% | Mastered |
| claude-fable-5-1 | Flagship | 92% | Not shown |
| gemini-3.8-flash | Mid tier | 50% | Not shown |
| gpt-6.1-sol | Mid tier | 100% | Mastered |
| claude-sonnet-5-5 | Mid tier | 49% | Not shown |
| gemini-3.5-flash-lite | Low cost | 44% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 50% | Not shown |
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: Roll up, roll down or one table per type, and the constraints each loses
How it is assessed: Chooses an implementation for sample subtypings and names the constraints to restore
Related knobits
Builds on
Needed for
- K-606 Recognises rolled-up subtypes losing conditional constraints
- K-607 Recognises rolled-down subtypes losing supertype constraints
All 40 knobits of Design the physical data model.
Source: Data-management practice used by the stage 6 reference and review checklist