Large language models (LLMs) have materially changed natural language processing (NLP).While LLMs have shifted focus from traditional semantic-based resources, structured linguistic databases such as WordNet remain essential for precise knowledge retrieval, decision making and aiding LLM development.WordNet organizes concepts through synonym sets (synsets) and semantic links but suffers from inconsistencies, including redundant or erroneous relations.This paper investigates an approach using LLMs to aid the refinement of structured language resources, specifically WordNet, by an automation for multiple hypernymy resolution, leveraging the LLMs semantic knowledge to produce tools for aiding and evaluating manual resource improvement.