Triple

T15139959
Position Surface form Disambiguated ID Type / Status
Subject Arke E361657 entity
Predicate headquartersLocation P62 FINISHED
Object Enschede 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: Enschede | Statement: [Arke, headquartersLocation, Enschede]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Enschede
Context triple: [Arke, headquartersLocation, Enschede]
  • A. Enschede chosen
    Enschede is a major city in the eastern Netherlands known for its former textile industry, technical university, and location near the German border.
  • B. Hoorn
    Hoorn is a historic port city in the Netherlands known for its role in the Dutch Golden Age and as a former base of the Dutch East India Company.
  • C. Zwolle
    Zwolle is a historic Dutch city in the eastern Netherlands known for its medieval center, cultural heritage, and regional economic importance.
  • D. Apeldoorn
    Apeldoorn is a city in the province of Gelderland in the Netherlands, known for the royal palace Het Loo and its historical ties to the Dutch monarchy.
  • E. Tilburg
    Tilburg is a city in the southern Netherlands known historically as an industrial and textile center and now as a regional cultural and educational hub.
  • 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_69d85a0759908190b8a051d2e2a1cbe6 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e005c46a248190a2364092d40274f3 completed April 15, 2026, 9:40 p.m.
Created at: April 10, 2026, 3:07 a.m.