Incident monitoring is critical in industrial settings to prevent disruptions and optimize operations. While traditional equipment logs are often converted into XES-like event logs, these formats typically associate each event with a single case object and overlook valuable information from other sources, particularly, pre- and post-incident process logs. These additional logs frequently describe activities involving multiple related objects (e.g., hardware, software). OCEL (Object-Centric Event Log) standard can be used to represent events involving multiple, interconnected objects, thus offering a more comprehensive view of incident-related processes. However, pre- and post-incident data are often recorded in unstructured textual formats, whereas OCEL requires well-structured data in order to be properly populated. To bridge this gap, we introduce a method to extract events and objects from unstructured pre- and post-incident textual content that leverages Large Language Models (LLMs). Our approach is evaluated on real-world data from the data center domain demonstrating its effectiveness in enriching incident monitoring and providing a structured foundation for advanced incident prediction and analysis.