Triple

T10711664
Position Surface form Disambiguated ID Type / Status
Subject Höxter E252551 entity
Predicate hasSubdivision P747 FINISHED
Object Brenkhausen
Brenkhausen is a village and district of the town of Höxter in North Rhine-Westphalia, Germany.
 E888444 NE FINISHED

How this triple was built (4 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: Brenkhausen | Statement: [Höxter, hasSubdivision, Brenkhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Brenkhausen
Context triple: [Höxter, hasSubdivision, Brenkhausen]
  • A. Haselbach
    Haselbach is a small municipality in the Straubing-Bogen district of Lower Bavaria in southeastern Germany.
  • B. Heppenbach
    Heppenbach is a village in the municipality of Amel in the German-speaking Community of eastern Belgium.
  • C. Hohne
    Hohne is a village in Lower Saxony, Germany, historically notable for its military garrison and association with British Army units.
  • D. Kühnhausen
    Kühnhausen is a locality in Germany known historically as the place where Nazi education minister Bernhard Rust died.
  • E. Beyenburg
    Beyenburg is a historic district in the eastern part of Wuppertal, Germany, known for its medieval monastery, reservoir, and well-preserved village character.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Brenkhausen
Triple: [Höxter, hasSubdivision, Brenkhausen]
Generated description
Brenkhausen is a village and district of the town of Höxter in North Rhine-Westphalia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Brenkhausen
Target entity description: Brenkhausen is a village and district of the town of Höxter in North Rhine-Westphalia, Germany.
  • A. Haselbach
    Haselbach is a small municipality in the Straubing-Bogen district of Lower Bavaria in southeastern Germany.
  • B. Heppenbach
    Heppenbach is a village in the municipality of Amel in the German-speaking Community of eastern Belgium.
  • C. Hohne
    Hohne is a village in Lower Saxony, Germany, historically notable for its military garrison and association with British Army units.
  • D. Kühnhausen
    Kühnhausen is a locality in Germany known historically as the place where Nazi education minister Bernhard Rust died.
  • E. Beyenburg
    Beyenburg is a historic district in the eastern part of Wuppertal, Germany, known for its medieval monastery, reservoir, and well-preserved village character.
  • F. None of above. chosen

Provenance (5 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_69d6aa5cbabc8190973e683950d89faf completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fe523de08190a82c8f057fe8baf6 completed April 9, 2026, 1:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb08d63d481908ab1d5038424dab6 completed April 14, 2026, 9:24 p.m.
NEDg Description generation batch_69deb384fb588190ae5d11a60fec0f53 completed April 14, 2026, 9:37 p.m.
NED2 Entity disambiguation (via description) batch_69deb4a2d4c48190a828262b1cc05b37 completed April 14, 2026, 9:41 p.m.
Created at: April 8, 2026, 9:13 p.m.