We introduce an efficient, scalable, and ready-to-use approach for generating knowledge graphs (KGs) based on any given ontology. Our method leverages large language model (LLM)-based retrieval-augmented generation (RAG) as a source of high-quality text data. It employs two agents: the first extracts entities and triples from the text corpus maintained by the RAG, while the second links similar entities by applying LLM reasoning grounded in pragmatics. This approach requires no fine-tuning or additional AI training, relying solely on off-the-shelf technologies. Furthermore, its use of RAG enables it to handle a text corpus of arbitrary size. We evaluated our method in the context of high-pressure die-casting, focusing on defect classification. In the absence of annotated datasets, a manual evaluation of the resulting KG and confusion matrix revealed a threat score of approximately 85% for entity extraction and 68% after clustering, with the primary source of error being the RAG itself. Our findings indicate that this approach can significantly facilitate the rapid generation of customised KGs for domain-specific applications.