Triple

T22939678
Position Surface form Disambiguated ID Type / Status
Subject Ibbenbüren E569686 entity
Predicate locatedIn P40 FINISHED
Object Tecklenburger Land NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tecklenburger Land | Statement: [Ibbenbüren, locatedIn, Tecklenburger Land]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tecklenburger Land
Context triple: [Ibbenbüren, locatedIn, Tecklenburger Land]
  • A. Boitzenburger Land
    Boitzenburger Land is a rural municipality in northeastern Germany’s Brandenburg state, known for its extensive forests, lakes, and historic manor estates.
  • B. Osnabrücker Land chosen
    Osnabrücker Land is a region in Lower Saxony, Germany, centered around the city of Osnabrück and known for its historical towns, varied landscapes, and cultural heritage.
  • C. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • D. Giessenlanden
    Giessenlanden was a former municipality in the Dutch province of South Holland that later became part of the newly formed municipality of Molenlanden.
  • E. Mühlenbecker Land
    Mühlenbecker Land is a municipality in the Oberhavel district of Brandenburg, Germany, known for its proximity to Berlin and its mix of forests, lakes, and residential areas.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e24590862c8190858f180ad302adab completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1813844b88190b05d3829b0c423c4 completed April 29, 2026, 3:55 a.m.
Created at: April 17, 2026, 3:45 p.m.