Navigating historical narratives poses a challenge in unveiling the spatial
intricacies of past landscapes. The proposed work addresses this challenge
within the context of the English Lake District, employing the Corpus of the
Lake District Writing. The method utilizes a generative pre-trained transformer
model to extract spatial relations from the textual descriptions in the corpus.
The study applies this large language model to understand the spatial
dimensions inherent in historical narratives comprehensively. The outcomes are
presented as semantic triples, capturing the nuanced connections between
entities and locations, and visualized as a network, offering a graphical
representation of the spatial narrative. The study contributes to a deeper
comprehension of the English Lake District's spatial tapestry and provides an
approach to uncovering spatial relations within diverse historical contexts.

使用生成式预训练转换模型从语料库中提取空间关系，以图形化方式呈现语境内条时关联，深入理解英国湖区的空间构架并揭示多样历史背景下的空间关系。