Triple

T50324
Position Surface form Disambiguated ID Type / Status
Subject Amsterdam E989 entity
Predicate locatedIn P40 FINISHED
Object Western Netherlands E24160 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: Western Netherlands | Statement: [Amsterdam, locatedIn, Western Netherlands]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Western Netherlands
Context triple: [Amsterdam, locatedIn, Western Netherlands]
  • A. Gelderland
    Gelderland is a large province in the eastern Netherlands known for its varied landscapes, including the forested Veluwe region and the river areas along the Rhine, Waal, and IJssel.
  • B. North Holland chosen
    North Holland is a province in the western Netherlands known for encompassing the national capital, Amsterdam, as well as historic towns and North Sea coastline.
  • C. Drenthe, Netherlands
    Drenthe, Netherlands is a rural northeastern Dutch province known for its prehistoric dolmen tombs, extensive nature reserves, and quiet agricultural landscapes.
  • D. Friesland
    Friesland is a northern province of the Netherlands known for its distinct Frisian language, rich maritime history, and unique cultural traditions.
  • E. Overijssel
    Overijssel is a province in the eastern Netherlands known for its historic Hanseatic cities, rivers, and varied landscapes of forests, heathlands, and farmland.
  • 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_69a2480baefc81909951b14058479aa2 completed Feb. 28, 2026, 1:42 a.m.
NER Named-entity recognition batch_69a24af56cc88190a898f8bf2a283820 completed Feb. 28, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69a30cc17d088190ab19e1b36d159bad completed Feb. 28, 2026, 3:41 p.m.
Created at: Feb. 28, 2026, 1:47 a.m.