Relational concept analysis (RCA) computes and represents relational datasets as lattices of concepts and implications. This approach relies on the computation of relational attributes which capture quantified relationships between objects and concepts. However, interpreting this representation is challenging for end-users. FCA experts must combine relational attributes from different concept lattices to obtain a complete formulation, while domain experts (e.g., software engineers, agronomists) face the additional difficulty of extracting meaning from these mathematical structures. This paper proposes an approach to address this issue by leveraging Large Language Models (LLMs). It comprises two main components: (1) the generation of a textual description of relational attributes; (2) the design of a generic prompt that enables LLM to produce a natural-language description of RCA artifacts using the generated textual description. The approach is illustrated using representative implications from a real-world agroecological dataset.