This paper proposes a hybrid and decomposed approach to automate process model generation from textual descriptions using Large Language Models (LLMs). Leveraging LLMs with prompting techniques is promising due to the scarcity of training data. While recent approaches explore LLMs’ potential in process modeling, the inherent complexity of this task limits their applicability to real-world scenarios where descriptions by non-experts may be complex or incomplete. Our approach addresses these challenges by modularizing the task into distinct steps within a hybrid pipeline: the LLM analyzes, clarifies, and completes the textual description, and extracts process entities and relationships. The process model is then constructed using a structured algorithm. This hybrid methodology integrates LLMs’ natural language understanding with a deterministic approach for robust model creation. Evaluation results demonstrate that our approach uses less tokens, and generates more accurate and understandable models compared to existing methods.