Triple

T7579527
Position Surface form Disambiguated ID Type / Status
Subject British Army of the Rhine E179449 entity
Predicate garrisonLocation P40 FINISHED
Object Bielefeld E279260 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: Bielefeld | Statement: [British Army of the Rhine, garrisonLocation, Bielefeld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bielefeld
Context triple: [British Army of the Rhine, garrisonLocation, Bielefeld]
  • A. Bielefeld chosen
    Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
  • B. Osnabrück
    Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
  • C. Paderborn
    Paderborn is a historic city in western Germany known for its medieval cathedral, role as a regional religious and cultural center, and strategic importance during World War II.
  • D. Gütersloh
    Gütersloh is a city in the German state of North Rhine-Westphalia known for being the headquarters of major companies like Bertelsmann and Miele.
  • E. Braunschweig
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
  • 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_69c69f327db881909a21ae3b156f8ded completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f975bbc08190aec30f902eaea494 completed March 27, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf9fc6bff48190b2f73e4c8c8b627a completed April 3, 2026, 11:08 a.m.
Created at: March 27, 2026, 3:52 p.m.