Ontology matching (OM) is an important process for enabling the interoperability of heterogeneous systems in general and in the Semantic Web in particular. OM takes ontologies as input and determines alignments as output, that is, a set of correspondences between the semantically related entities of the input ontologies. However, several OM systems returns alignments that need to be validated. This validation remains a challenge because benchmarks only exist in certain specific domains and need to be updated in view of ontological changes. In this paper, we propose a systematic solution, grounded on the exploitation of the power of LLMs and KGs, for automatic the validation of ontology alignments. We add ontology concepts and information from a knowledge graph to the prompt to improve LLM reasoning and provide correct validation while minimizing errors. We experimentally proved that our approach reduces significantly the need for human intervention in the validation process. We demonstrate the effectiveness of our solution with experiments on OAEI (Ontology Alignment Evaluation Initiative) campaign and additional alignment benchmarks.