Triple

T3207745
Position Surface form Disambiguated ID Type / Status
Subject Cathedral chapter of Hildesheim E67204 entity
Predicate locatedIn P40 FINISHED
Object Hildesheim E74604 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: Hildesheim | Statement: [Cathedral chapter of Hildesheim, locatedIn, Hildesheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hildesheim
Context triple: [Cathedral chapter of Hildesheim, locatedIn, Hildesheim]
  • A. Hildesheim chosen
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • B. 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.
  • C. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • D. Nordhausen
    Nordhausen is a historic town in central Germany known for its medieval architecture, former role as a key trading center, and association with the nearby Mittelbau-Dora concentration camp site.
  • E. Wetzlar
    Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
  • 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_69ad858ac36c81909962589cd277d6e2 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaa58340881908347d772cfa0ac4c completed March 8, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b73c409081909c583019d7ec1d4a completed March 14, 2026, 7:30 p.m.
Created at: March 8, 2026, 3:07 p.m.