Linguistic annotation of high-stakes narrative data is often constrained by data confidentiality, domain expertise, and the lack of large-scale multi-annotator pipelines. We present a human-in-the-loop framework for auditing annotation discrepancies in crash narratives, combining structured labels, narrative-based annotation, and expert adjudication. Using 9,387 crash reports, we conduct a multi-layer analysis of disagreement across annotation sources. Nearly half of the records (49.4%) exhibit discrepancies between structured and narrative labels, driven mainly by unsupported structured assignments. In contrast, narrative-based annotation achieves near-perfect agreement with adjudication (𝜅 = 0.990), indicating strong consistency when grounded in textual evidence. We introduce a taxonomy of discrepancies, showing refinement opportunities and missing details are the most common, while linguistic factors such as hedging and underspecification contribute to ambiguity. We further show that annotator-reported uncertainty strongly predicts annotation difficulty, with uncertain records nearly nine times more likely to disagree with structured labels. These findings highlight limitations of administrative coding and support a scalable, uncertainty-guided annotation paradigm for restricted-access domains.