Language Technologies for Crisis Preparedness and Response (LT4CPR) encompass a broad range of natural language applications designed to support disaster response and preparedness. Within this domain, our shared task focuses specifically on leveraging social media data to extract actionable information and generate structured situation reports. To establish a formal benchmark, we introduce a domain-specific taxonomy for categorizing critical crisis information, alongside a novel pipeline for generating synthetic datasets to support task training and evaluation. Furthermore, we develop two baseline systems and propose an evaluation methodology designed to leverage the structural framework of situation reports. The shared task attracted participation from nine teams, whose diverse approaches offer valuable preliminary insights into the feasibility of structured situation report generation and the effectiveness of our evaluation setup. Finally, we highlight key open challenges (such as data access, operational alignment, noise filtering) which needs to be addressed in future research.