# Data model: causal triples

IntelFactor’s core learning unit is a **defect → cause → outcome** triple.

### Example fields

* `defect_id`.
* defect type, severity, confidence.
* correlated parameter drift (JSON).
* cause hypothesis + cause confidence.
* bilingual explanation (when enabled).
* SOP-linked recommendation.
* operator action: accepted / rejected / modified.
* measured outcome: defect rate after, time to baseline.
* status: pending / verified / disputed.

### Why this matters

Detection datasets answer: “what does a defect look like?”

Triples answer: “what caused it, and what fixed it?”


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