Abstract Summary
HIPE-2026 is a CLEF evaluation lab that focuses on person--place relation extraction from noisy, multilingual historical texts. Building on the previous HIPE campaigns in 2020 and 2022, this lab introduces a new task that targets semantic relations between persons and places. Specifically, systems are asked to classify person--place relations across two relation types: 1. At---``Has the person been at this place?'' and 2. isAt---``Is the person at the given place?'' (relative to a document's publication date). The task requires an understanding of the text and the ability to reason about temporal and geographical information, and is designed to be tackled by both generative AI systems (large language models) and more traditional classification approaches. The lab promotes both accuracy and efficiency by offering dual evaluation profiles and testing generalisation capabilities with a surprise test set. HIPE-2026 aims to support downstream applications in knowledge graph construction, historical biography reconstruction, and spatial analysis in digital humanities. We present the motivation, task design, datasets, and evaluation framework for the lab.
