Triple

T734646
Position Surface form Disambiguated ID Type / Status
Subject Overijssel E14902 entity
Predicate containsCity P294 FINISHED
Object Enschede E354840 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: Enschede | Statement: [Overijssel, containsCity, Enschede]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Enschede
Context triple: [Overijssel, containsCity, 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. Zwolle
    Zwolle is a historic Dutch city in the eastern Netherlands known for its medieval center, cultural heritage, and regional economic importance.
  • C. 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.
  • D. 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.
  • E. Utrecht
    Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural 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_69a4934d9930819099eed80096b0597d completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5d8c6148190a468f2d95f7ec91f completed March 1, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b36067f47881908de481e1350bcaff completed March 13, 2026, 12:55 a.m.
Created at: March 1, 2026, 7:37 p.m.