Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA) have been used to assist software engineers restructure class models, with the aim of factoring common elements (e.g., attributes, methods) and revealing new abstractions, e.g., superclasses. Their foundations, based on lattice theory, lack “common-sense” knowledge to assess whether derived concepts correspond to relevant abstractions and, if so, to name them appropriately. This paper presents a methodology that combines FCA/RCA with Large Language Models (LLMs) to provide such assistance. Our approach builds on the existing literature by taking advantage of LLMs to address the naming problem of an entity (e.g. a class) and assess its relevancy. The results of several LLMs are compared and are encouraging.