This study addresses the challenges of performing narratological analysis on low-resource languages, with a focus on Old Church Slavonic. Understanding the roles, interactions, and networks of persons is central to narrative analysis, yet such investigation is hindered by the scarcity of experts and the limited availability of annotated resources. We explore both established natural language processing (NLP) methods and large language models (LLMs) for analyzing pre-modern Slavic Lives of Saints, including several Slavic versions, the Greek original, and an English translation. Pre-modern Slavic texts pose particular difficulties due to rich morphology, orthographic variation, and limited standardization, which complicate the application of both traditional NLP tools and off-the-shelf LLMs. Through experiments using annotated and non-annotated ground truth data, we demonstrate that while conventional NLP methods often reach their limits on such low-resource, highly variable texts, LLMs provide complementary capabilities that can support narratological insights, especially in tracking persons and their interactions, albeit with important caveats regarding accuracy and coverage.