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SANE is a novel schema-aware evaluation paradigm for natural-language (text-to-SQL) querying of biological/pharmacological datasets, enabling automatic benchmark generation tied to real experimental schemas. The study shows that few-shot LLMs with structured prompting can achieve accurate SQL generation without fine-tuning, with most failures stemming from ambiguous inputs rather than incorrect query generation.