This paper explores the synergy between Knowledge Graphs (KGs), Graph Machine Learning (Graph ML), and Large Language Models (LLMs) for multilingual Named Entity Recognition (NER) and Relation Extraction (RE), specifically targeting software mentions within the SOMD 2025 challenge. We propose a methodology where documents are first transformed into heterogeneous KGs enriched with linguistic features (Universal Dependencies) and external knowledge (entity linking). An inductive GraphSAGE model, operating on PyTorch Geometric’s ‘HeteroData‘ structure with dynamically generated multilingual embeddings, performs node classification tasks. For NER, Graph ML identifies candidate entities and types, with an LLM (DeepSeek v3) acting as a validation layer. For RE, Graph ML predicts dependency path convergence points indicative of relations, while the LLM classifies the relation type and direction based on entity context. Our results demonstrate the potential of this hybrid approach, showing significant performance gains post-competition (NER Phase 2 Macro F1 improved to 0.4364 from 0.2953, RE Phase 1 0.3355 Macro F1), which are already described in this paper, and highlighting the benefits of integrating structured graph learning with LLM reasoning for information extraction.