You are building a structured data extraction system using Claude.

Limited review capacity should be concentrated on records with the highest probability of semantic error. Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model-reported uncertainty are direct indicators that an extraction requires human judgment.

Anthropic’s reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination-reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations )

Option B may waste reviewer capacity on clear, well-supported values while overlooking ambiguous errors in other fields. Option C is reactive: downstream acceptance does not guarantee semantic correctness, and silent errors may never produce processing failures. Option D provides an unbiased estimate of overall quality and should be retained as a secondary audit mechanism, but random selection is not the most efficient way to intercept risky records when only 5% can be reviewed.

In production, model confidence should not be treated as a calibrated probability by itself. It should be combined with objective signals such as contradictory source spans, missing citations, OCR quality, validation warnings, document type, and business impact.

Official references/topics: Human-in-the-Loop Review, Uncertainty Handling, Source Grounding, Risk-Based Escalation.