Triple

T4169596
Position Surface form Disambiguated ID Type / Status
Subject NL-GE E84527 entity
Predicate appliesToTerritory P647 FINISHED
Object Gelderland 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 | Statement: [NL-GE, appliesToTerritory, Gelderland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gelderland
Context triple: [NL-GE, appliesToTerritory, Gelderland]
  • 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. Zuid-Holland
    Zuid-Holland is a densely populated coastal province in the western Netherlands that includes major cities such as Rotterdam and The Hague.
  • C. 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.
  • D. Central Netherlands
    Central Netherlands is a geographic region in the heart of the Netherlands that includes the city of Utrecht and serves as an important hub for commerce, transportation, and culture.
  • 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_69aed932cab48190b80ffe35f7029ae1 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02c730b081908b19e6a4aea1549b completed March 9, 2026, 5:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c2fa16c8190b03ed4d47b6090e7 completed March 28, 2026, 8:38 p.m.
Created at: March 9, 2026, 3:44 p.m.