You're not off base. There's no per-claim evidence trail. The Claude pipeline scored claims using model judgment and stored verdicts without the reasoning or sources behind them. Your Tesla example is a good one. There is real evidence for that causal claim (NHTSA investigations, documented crashes where Autopilot naming was cited as a factor), but the dataset doesn't show you any of it. Right now it's just "Claude said so." What the dataset is currently good for: extraction, clustering, timeline structure, and surfacing drift patterns across claim families. When 120 hallucination claims come back 57% supported and 63 bubble claims come back 41% contradicted, the distribution tells you something even if any single verdict is arguable. What it's not good for: claim-by-claim adjudication auditability. You're right that without provenance, this is closer to a structured first draft than a final verdict. Your approach of storing browsed pages and snippets per-claim is what auditable looks like.