Triple

T15726487
Position Surface form Disambiguated ID Type / Status
Subject Bundesministerium der Verteidigung E381230 entity
Predicate hasOffice P1268 FINISHED
Object Hardthöhe Bonn E381231 NE FINISHED

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: Hardthöhe Bonn | Statement: [Bundesministerium der Verteidigung, hasOffice, Hardthöhe Bonn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hardthöhe Bonn
Context triple: [Bundesministerium der Verteidigung, hasOffice, Hardthöhe Bonn]
  • A. Hardthöhe, Bonn chosen
    Hardthöhe, Bonn is a district in the German city of Bonn that serves as the main headquarters complex of Germany’s Federal Ministry of Defence.
  • B. Wachtberg
    Wachtberg is a municipality in the Rhein-Sieg district of North Rhine-Westphalia, Germany, known for its scenic location near Bonn and the Siebengebirge hills.
  • C. Benrath
    Benrath is a German surname most notably associated with the late actor Martin Benrath.
  • D. Venusberg, Bonn
    Venusberg, Bonn is a district of the German city of Bonn known for its large medical and research campus, including the University Hospital Bonn.
  • E. Calenberg hill
    Calenberg hill is a prominent elevation in Lower Saxony, Germany, historically significant as the site of the former Calenberg Castle and namesake of the surrounding region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d86d9cdb648190bf3171be0bd7d872 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fb357a88190a92641c8a8c20573 completed April 16, 2026, 2:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff82f8b88081909855d3da0346fa25 completed May 9, 2026, 6:54 p.m.
Created at: April 10, 2026, 4:46 a.m.