Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Engineering and materials‐science projects increasingly depend on interoperable data, yet building the ontologies that enable this interoperability remains manual, slow, and expensive. We benchmark large language models (LLMs) on two early‐stage taxonomy‐creation tasks: candidate term extraction and binary subclass decisions against the basic formal ontology (BFO) and common core ontologies (CCO). Thirteen LLMs (10 proprietary, three open‐weight) and six non‐LLM baselines ( spaCy , KeyBERT , WordNet , NLI ) are evaluated on two engineering articles covering additive manufacturing and carbon‐fiber‐reinforced polymer (CFRP)–metal hybrid bonding. For extraction, F 1 follows an inverted‐U in chunk length, peaking at 750–1,000 characters, and Claude‐Opus‐4.7 reaches about 73% versus at most 28% for the best baseline. For subclass decisions, a Set‐Inclusion prompt (“Is every x a y ?”) is most balanced, and Claude‐Opus‐4.7 attains about 94% combined accuracy on low‐level terms, with GPT‐5.4 at comparable accuracy and the best baseline ( WordNet ) at 64%. Moderate hierarchy depth aids acceptance, whereas free‐text examples reduce it. GPT‐4o‐mini delivers 5,940 F 1 ‐points per dollar versus 19 for GPT‐4‐32k . Six workflow paths map these results to model and review‐policy choices. Absolute F 1 , TPR, TNR, and model rankings are expected to shift with corpus, ontology family, and model release date.