This study proposes a structured story generation system based on traditional narrative theory using a large language model (LLM). The proposed system sequentially constructs a three-act structure and a 15-step narrative structure of "Save the Cat!" based on the log line, character information, and genre input by the user, and each step is embodied through hierarchical generation and prompt chaining. In particular, when user modifications occur, the changes are designed to be automatically reflected in the upper and lower stages to maintain narrative consistency and logic. To verify the effectiveness of this system, human-written narratives, single-prompt-based generative narratives, and generative narratives from the proposed system were compared and evaluated, yielding excellent results in terms of narrative structure fidelity and logic. By demonstrating the structural controllability of LLM-based story generation, this study suggests applicability in the field of digital content production in the future.