Recognises a fact table of mixed grain
A fact or wide table whose rows mean different things, or whose columns are true at different grains. Measures at the coarser grain repeat on every finer row and are counted again when summed.
Can AI do this?
AI does this well Most of the tested AI models master this. 7 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview | Flagship | 0% | Not shown |
| 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 | 0% | Not shown |
| gpt-6-luna | Low cost | 75% | Not shown |
| 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: The grains mixed and the measures double counted
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: fact_account_balance with one row per payment carrying ledger_balance_amount, which is per account per day: an account with three payments in a day reports three times its balance Acceptable: A grain sentence, and every column true at that grain; fact_payment at payment grain and daily balances on fact_account_balance_daily (PHY-013, PHY-028)
Also known as: Mixed grain (physical model)
Related knobits
Builds on
All 40 knobits of Design the physical data model.
Source: Review checklist 06-physical, row 26; C&B ("Data Warehousing Design": choosing the grain); E&N 29.3; S&W 16.6