A Message Sequence Chart (MSC) is a visually comprehensible knowledge representation used for showing events of a narrative in their correct temporal order. However, to enable its use as a spatio-temporal knowledge representation, it needs to be enhanced with spatial knowledge present in the narrative. In this work, we aim at enriching MSCs with spatial knowledge, using their condition construct, to enable them for downstream spatio-temporal applications. We propose two Large Language Model (LLM) based approaches for MSC construction, one based on few-shot prompting and another involving fine-tuning. Further, we develop a few-shot prompting approach to enable the spatial enrichment of the constructed MSCs. We report on performance of the proposed approaches and also demonstrate the utility of the enrichment in an image-based visualization application.