Triple

T3351021
Position Surface form Disambiguated ID Type / Status
Subject Dilan Yeşilgöz-Zegerius E70491 entity
Predicate workLocation P7 FINISHED
Object The Hague E5547 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: The Hague | Statement: [Dilan Yeşilgöz-Zegerius, workLocation, The Hague]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Hague
Context triple: [Dilan Yeşilgöz-Zegerius, workLocation, The Hague]
  • A. The Hague chosen
    The Hague is a major Dutch city known as the seat of the Netherlands’ government and home to numerous international courts and organizations, including the International Court of Justice.
  • B. Amsterdam
    Amsterdam is the largest city in the Netherlands, renowned as a historic commercial and cultural center characterized by its canals, trading heritage, and role as the country’s principal metropolis.
  • C. Leeuwarden
    Leeuwarden is a historic city in the northern Netherlands, known as the capital of the province of Friesland and for its rich cultural and architectural heritage.
  • D. Rotterdam
    Rotterdam is a major Dutch port city known for having one of the world’s largest harbors and striking modern architecture.
  • E. Leiden
    Leiden is a historic Dutch city in South Holland known for its prestigious university, rich cultural heritage, and well-preserved canals and old town.
  • 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_69ad85a4ef7c8190a29e2bbd6fa454e4 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb220721c81909eb4d8d35c923927 completed March 8, 2026, 5:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38ba897188190bf3ee24cb8a7384f completed March 13, 2026, 3:59 a.m.
Created at: March 8, 2026, 3:12 p.m.