Triple

T21205475
Position Surface form Disambiguated ID Type / Status
Subject Dissen am Teutoburger Wald E522565 entity
Predicate locatedNear P294 FINISHED
Object Borgholzhausen 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: Borgholzhausen | Statement: [Dissen am Teutoburger Wald, locatedNear, Borgholzhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borgholzhausen
Context triple: [Dissen am Teutoburger Wald, locatedNear, Borgholzhausen]
  • A. Borgholzhausen chosen
    Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
  • B. Gelnhausen
    Gelnhausen is a historic town in the German state of Hesse, known for its well-preserved medieval architecture and former status as a Free Imperial City of the Holy Roman Empire.
  • C. Münchholzhausen
    Münchholzhausen is a district of the city of Wetzlar in the German state of Hesse.
  • D. Barsinghausen
    Barsinghausen is a town in Lower Saxony, Germany, located near Hanover and known historically for its mining industry and proximity to the Deister hills.
  • E. Suhl
    Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
  • 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_69e0b5112d8881909510b2dcdc93106d completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e734342e9081909e241bed54dbc0b4 completed April 21, 2026, 8:24 a.m.
Created at: April 16, 2026, 3:20 p.m.