Organizations often use unstructured text to represent and exchange crucial information, such as product model descriptions, system requirements, and documentation. Even though the information within such unstructured text is precious, extracting it in more structured forms and exchanges is cumbersome, time-consuming, error-prone, and mostly reserved for experts. Combining neural and symbolic techniques, leveraging the integration of Large Language Models and Knowledge Graphs, can boost knowledge extraction and post-processing. While many approaches exist for transforming text into structured knowledge, they mostly lack automation, user integration, visual communication, and, consequently, trust and explainability. To address this gap, we present a user-driven hybrid neuro-symbolic approach that puts users in the loop to steer the transformation of unstructured text into highly semantically structured Knowledge Graphs. We evaluated our approach quantitatively and qualitatively to show that inexperienced users can create high-quality Knowledge Graphs with the same quality as experts, but in a fraction of the time. Our approach demonstrates that human interactions significantly enhance quality. Additionally, we confirmed our approach’s high usability, as measured by the System Usability Scale, and strengthened the trust-building and explainability-contributing aspects through expert interviews.