The integration of Geographic Information Retrieval (GIR) with advancements in Natural Language Processing (NLP) and Large Language Models (LLM) has revolutionized the utilization of unstructured text as a data source for Geographic Information Systems (GIS). Historically, unstructured text, unlike structured text such as XML documents or SQL queries, was predominantly leveraged by search engines and within the broader field of Information Science. However, the ubiquity of user-generated content on social media, combined with accessible online news outlet APIs, has prompted the integration of textual data in GIS applications. The fundamental shift in NLP technologies, particularly the advent of LLMs like GPT models and the evolution of text recognition algorithms, has enhanced the reliability of place name recognition, a subset of Named Entity Recognition (NER). These technologies enable the effective extraction of geographic references from vast quantities of textual data, offering substantial potential for enriching GIS databases. The primary challenges in this field include resolving place name ambiguities and vagueness, and adapting to the dynamic nature of geographic names and boundaries. Despite these challenges, GIR promises to unlock powerful new dimensions of spatial analysis and decision-making by integrating textual and geographic data.