Legacy assembly diagrams, often provided as machine-unreadable images, hinder integration into the Industry 4.0 (I4.0) ecosystem. We propose a multi-step pipeline leveraging Large Language Models as a single simple-to-operate tool to recognize parts, relationships, and global identifiers, segment parts in images, and structure the data into Asset Administration Shell as I4.0 digital twins. Targeted prompts are designed for each step and evaluated on 15 diverse real-world diagrams. Results show that while the LLM reliably recognizes parts and link identifiers, there are still some open challenges with relationship extraction and semantic segmentation. Despite these limitations, LLMs provide a viable tool for semi-automated digitalization. Our end-to-end pipeline thus enables seamless integration of legacy diagrams into I4.0 systems.