Linking-based geocoding is the task of linking location mentions in text to their corresponding entries in a geographic database (Geo-DB) and assigning precise coordinates. Although the task and its technology are essential for spatial information extraction, existing datasets are manually curated and lack sufficient data for training accurate models. To address this limitation, we automatically construct a large-scale dataset for linking-based geocoding by leveraging publicly available resources to generate data efficiently at scale. Specifically, we align location mentions in the first paragraphs of Japanese Wikipedia articles with their associated Wikidata entries containing geographic attributes. Wikipedia provides natural textual contexts, while Wikidata offers structured data such as coordinates, place types, and administrative divisions, which can serve as rich metadata for future extensions. Our experiments show that models trained on our dataset achieve strong performance not only on in-domain data, i.e., Wikipedia, but also on out-of-domain newspaper articles, and further confirm that hard negative mining substantially improves disambiguation among confusable candidates. Although the dataset focuses on Japanese, the construction method is language-agnostic and can be extended to other languages with sufficient Wikipedia and Wikidata coverage.

Corporate history in corporate annual reports includes events related to organizational changes, which can provide useful cues for a comprehensive understanding of corporate actions.However, extracting organizational changes requires identifying differences in companies before and after an event, raising concerns about whether existing information extraction systems can accurately capture the relations.This work introduces JaCorpTrack, a novel event extraction task designed to identify events related to organizational changes.JaCorpTrack defines five event types related to organizational changes and is designed to identify the company names before and after each event, as well as the corresponding date.Experimental results indicate that large language models (LLMs) exhibit notable disparities in performance across event types.Our analysis reveals that these systems face challenges in identifying company names before and after events, and in interpreting event types expressed under ambiguous terminology.We will publicly release our dataset and experimental code at https://github.com/naist-nlp/JaCorpTrack