Process mining holds substantial potential to discover and optimize processes utilizing event log data. However, current applications primarily rely on (semi-)structured data from process-aware information systems, limiting their capacity to incorporate multimodal data from diverse sources, particularly in domains like IT service management (ITSM). While existing stand-alone approaches can extract event log data from unstructured sources such as videos, documents, or bot logs, they fall short of leveraging the full range of real-world data available in ITSM. To address this gap, our research focuses on developing a reference architecture for constructing event logs from multimodal data. This architecture integrates diverse data types, construction functions, and process mining use cases. Following a design science research methodology, we aim to evaluate the architecture through a software artifact leveraging real-world ITSM data and incorporating state-of-the-art generative AI. In this study, we present the preliminary reference architecture and share early insights from expert evaluations.