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This paper presents a systematic human audit of NL-to-FOL datasets FOLIO and MALLS, finding 39% and 36% incorrect formalizations respectively. It releases corrected ground truths and an LLM-assisted framework to focus human relabeling, reducing the review workload to under 24% of instances for 90% accuracy.