LLM-Generated RDF Triples for Agricultural Species: A Comparative Evaluation Using AGROVOC Grounding

The construction of RDF knowledge bases for specialized domains is costly and requires close collaboration between domain experts and knowledge engineers. This paper evaluates three commercial LLMs — Claude, ChatGPT, and Gemini — in generating RDF Turtle triples for 38 plant species relevant to Brazilian agriculture. All models received an identical prompt combining five prompt engineering techniques, including few-shot exemplification and external file grounding via a CSV file with correct AGROVOC URIs. Outputs were assessed for AGROVOC URI precision, common name correctness against Embrapa reference sources, and syntactic conformance. Claude achieved perfect URI precision (100%) and the highest recall for common names (82.9%). ChatGPT reached the highest common name precision (96.1%) but poor URI precision (16.2%). Gemini showed similar recall (59.8%) and worse URI precision (8.1%). All models produced syntactically valid Turtle. Results indicate that grounding effectiveness varies across models and that programmatic URI validation is essential in LLM-assisted knowledge base construction.