Approaches based solely on textual representations have limitations in capturing structural relations between legal entities, particularly in documents with high lexical similarity. This paper presents ongoing work on a dynamic clustering system for judicial decisions that integrates hybrid representations, combining semantic embeddings from legal-domain Portuguese models with knowledge graphs automatically constructed from documents. The architecture supports incremental clustering and generates cluster justifications using Large Language Models grounded on knowledge graph relations. Preliminary evaluation combines the quantitative metrics Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index.