Since the 1968 NATO Conference on Software Engineering laid the groundwork for systematic development of software systems, much effort has been dedicated to automating software design including Computer-Aided Software Engineering, Model-Driven Development, low-code/no-code platforms, and AI-driven tools like ChatGPT from OpenAI. Today, Generative AI offers an unprecedented opportunity for converting user requirements in natural language into system specifications via a Large Language Model (LLM). In this paper, we validate LLM-based system design automation (LSDA) for modeling ontology, workflow, entity-relationship diagrams and other artifacts on real-world examples. We found that ChatGPT is able to produce these artifacts with surprisingly high accuracy and quality even with a zero-shot prompting approach and simple prompts with little prompt optimization.