Triple

T7290594
Position Surface form Disambiguated ID Type / Status
Subject Bomann Museum E164381 entity
Predicate operatedBy P86 FINISHED
Object City of Celle E540682 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: City of Celle | Statement: [Bomann Museum, operatedBy, City of Celle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Celle
Context triple: [Bomann Museum, operatedBy, City of Celle]
  • A. city of Celle chosen
    The city of Celle is a historic town in Lower Saxony, Germany, known for its well-preserved medieval old town and numerous timber-framed houses.
  • B. Calenberg
    Calenberg was a historic principality in what is now Lower Saxony, Germany, that formed a core territory of the Duchy of Brunswick-Lüneburg and later the Electorate and Kingdom of Hanover.
  • C. Seligenstadt
    Seligenstadt is a historic town in Hesse, Germany, known for its well-preserved medieval center and its association with the Carolingian scholar Einhard.
  • D. Calenberger Neustadt
    Calenberger Neustadt is a historic inner-city district of Hanover, Germany, known for its mix of residential areas, cultural sites, and proximity to the city center.
  • E. Lülsfeld
    Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • 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_69c6887a499881909dd23341399c59d8 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6eb6d4e308190af6b8c237988d7d8 completed March 27, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7db4a31608190ba465e22e7a81782 completed March 28, 2026, 1:44 p.m.
Created at: March 27, 2026, 3 p.m.