https://w3id.org/np/RAbFynIWzdiPXPK6t_6MrVWz_87lyeHg8f_rzDvnzYc54
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<https://doi.org/10.48550/arXiv.2408.07705> a prov:Entity;
dct:title "ENHANCING SUPPLY CHAIN VISIBILITY WITH KNOWLEDGE GRAPHS AND LARGE LANGUAGE MODELS";
<http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#KgLlmFrameworkForSupplyChainVisibility>;
<http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#Bert>,
<https://neverblink.eu/ontologies/llm-kg/methods#Gpt3>, <https://neverblink.eu/ontologies/llm-kg/methods#Gpt4>,
<https://neverblink.eu/ontologies/llm-kg/methods#Roberta> .
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rdfs:label "GPT-4" .
<https://neverblink.eu/ontologies/llm-kg/methods#KgLlmFrameworkForSupplyChainVisibility>
a <http://purl.org/spar/fabio/Workflow>;
dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGConstruction>;
rdfs:comment "This framework proposes a novel zero-shot, LLM-driven approach to construct knowledge graphs from public data. It specifically leverages LLMs (e.g., GPT-4) with carefully crafted prompts for Named Entity Recognition (NER), Relation Extraction (RE), and Entity Disambiguation. The extracted and structured information is then used to build a comprehensive knowledge graph to enhance supply chain visibility.";
rdfs:label "KG-LLM Framework for Supply Chain Visibility";
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