Data model: causal triples

The learning unit that connects defects to verified fixes.

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?”