Ontologies are nowadays widely used to organize information across specific domains, being effective due to their hierarchical structure and the ability to explicitly represent relationships between concepts. Knowledge engineering, like compiling companies’ vast bodies of knowledge into these structures, however, still represents a time-consuming, largely manually performed process, esp. with significant amounts of knowledge often only recorded within unstructured text documents. Since the recently introduced Large Language Models (LLMs) excel on text summarization, this raises the question whether these could be exploited within dedicated knowledge fusion architectures to assist human knowledge engineers by automatically suggesting relevant classes, instances and relations extracted from textual corpora. We therefore propose a novel approach that leverages the taxonomic structure of a partially defined ontology to prompt LLMs for hierarchical knowledge organization. Unlike conventional methods that rely solely on static ontologies, our methodology dynamically generates prompts based on the ontology’s existing class taxonomy, prompting the LLM to generate responses that extract supplementary information from unstructured documents. It thus introduces the concept of using ontologies as scaffolds for guiding LLMs, in order to realize a mutual interplay between structured ontological knowledge and the soft fusion capabilities of LLMs. We evaluate our proposed algorithm on a real-world case study, performing a knowledge fusion task on heterogeneous technical documentation from a medical prosthesis manufacturer.