We describe an experimental implementation of a logic-based end-to-end pipeline of giving explained answers to questions posed in natural language. The main components of the pipeline are semantic parsing, integration with large knowledge bases, automated reasoning using extended first order logic, and finally the translation of proofs back to natural language. While able to answer relatively simple questions on its own, the implementation is targeting research into building hybrid neurosymbolic systems for gaining trustworthiness and explainability. The end goal is to combine machine learning and large language models with the components of the implementation and to use the automated reasoner as an interface between natural language and external tools like database systems and scientific calculations.