Recognises a check scheduled at the wrong moment
A schedule that runs the check after the data has been used, before the data it depends on has arrived, or at a different rhythm from the load. Integrity checked before the parent table finishes loading reports orphans that are not there; a check after publication explains a defect instead of preventing it.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 82% | Not shown |
| gemini-3.1-pro-preview* | Flagship | 25% | Not shown |
| gpt-6-astra* | Flagship | 99% | Mastered |
| claude-fable-5-1* | Flagship | 99% | Mastered |
| gemini-3.8-flash* | Mid tier | 99% | Mastered |
| gpt-6.1-sol* | Mid tier | 99% | Mastered |
| claude-sonnet-5-5* | Mid tier | 26% | Not shown |
| gemini-3.5-flash-lite | Low cost | 33% | Not shown |
| gpt-6-luna | Low cost | 33% | Not shown |
| claude-haiku-5-5 | Low cost | 82% | Not shown |
* 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 schedule and the load or use it must follow
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: Integrity of payment.account_id scheduled "on payment load", while account loads an hour later; 2 % orphans every morning, none by noon Acceptable: schedule: "after the nightly batch has loaded account and payment"; publication gated on the checks that guard it
Also known as: Check runs at the wrong moment (data quality rule)
Related knobits
Builds on
All 30 knobits of Derive data quality rules.
Source: Review checklist 03-dq-rules (data-quality), row 24; Kimball & Caserta 2004; ISO 25012; formerly data-modelling K-724