Triple

T1464977
Position Surface form Disambiguated ID Type / Status
Subject IJssel E27000 entity
Predicate region P40 FINISHED
Object Gelderland region E14197 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: Gelderland region | Statement: [IJssel, region, Gelderland region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gelderland region
Context triple: [IJssel, region, Gelderland region]
  • A. Gelderland chosen
    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 Brabant
    North Brabant is a southern province of the Netherlands known for its historic cities, Catholic cultural heritage, and role as a key battleground during World War II.
  • C. Rijnmond region
    The Rijnmond region is an urban and industrial area in the western Netherlands centered around the port city of Rotterdam and its surrounding municipalities.
  • D. Zuid-Holland
    Zuid-Holland is a densely populated coastal province in the western Netherlands that includes major cities such as Rotterdam and The Hague.
  • E. Southwestern Netherlands
    Southwestern Netherlands is a coastal region of the Netherlands characterized by its delta landscapes, estuaries, and extensive water management and flood protection works.
  • 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_69a496d25d6881909dbd84f86d763992 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c5ba2d5c81909ee85713de961fcb completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c6c1bb08819086668e6c1f0cce03 completed March 14, 2026, 8:36 p.m.
Created at: March 1, 2026, 8:01 p.m.