Triple

T23037252
Position Surface form Disambiguated ID Type / Status
Subject Évreux E573634 entity
Predicate hasTwinTown P919 FINISHED
Object Hildesheim, Germany NE NERFINISHED

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, Germany | Statement: [Évreux, hasTwinTown, Hildesheim, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hildesheim, Germany
Context triple: [Évreux, hasTwinTown, Hildesheim, Germany]
  • A. Lüneburg, Germany
    Lüneburg, Germany is a historic Hanseatic town in Lower Saxony known for its medieval architecture, former salt trade wealth, and well-preserved old town.
  • B. Hildesheim chosen
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • C. Hamm, Germany
    Hamm is a city in the German state of North Rhine-Westphalia, known as an industrial and transportation hub in the eastern Ruhr area.
  • D. Minden, Germany
    Minden, Germany is a historic town in North Rhine-Westphalia known for its strategic location on the Weser River and its role in significant military events such as the Battle of Minden.
  • E. Brunswick, Germany
    Brunswick, Germany is a historic city in Lower Saxony known for its medieval architecture, former status as a ducal residence, and role as an important commercial and cultural center in northern Germany.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245b911188190bc3d96326c847969 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1850fe5348190b42259595d82cff4 completed April 29, 2026, 4:12 a.m.
Created at: April 17, 2026, 3:53 p.m.