Explains lossless-join decomposition
A split is lossless only if the shared attributes are a key of at least one part.
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 | 99% | Mastered |
| gemini-3.1-pro-preview | Flagship | 99% | Mastered |
| gpt-6-astra | Flagship | 98% | Mastered |
| claude-fable-5-1 | Flagship | 99% | Mastered |
| gemini-3.8-flash | Mid tier | 98% | Mastered |
| gpt-6.1-sol | Mid tier | 99% | Mastered |
| claude-sonnet-5-5 | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 70% | Not shown |
| gpt-6-luna | Low cost | 99% | Mastered |
| claude-haiku-5-5 | Low cost | 98% | 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: A split is lossless only if the shared attributes are a key of at least one part
How it is assessed: Decides for sample decompositions whether joining back produces spurious rows
Related knobits
Builds on
Needed for
- K-514 Recognises a lossy decomposition
- K-533 Decomposes an entity along a functional dependency without loss
- K-660 Explains vertical and horizontal partitioning of tables
All 56 knobits of Derive the logical data model.
Source: Normalisation theory and relational design (Codd, Fagin, Date), as used by the stage 5 reference and review checklist