Triple

T710637
Position Surface form Disambiguated ID Type / Status
Subject Gelderland E14197 entity
Predicate borders P224 FINISHED
Object Limburg E23233 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: Limburg | Statement: [Gelderland, borders, Limburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Limburg
Context triple: [Gelderland, borders, Limburg]
  • A. Limburg (Netherlands) chosen
    Limburg (Netherlands) is a southeastern Dutch province known for its hilly landscape, distinct Limburgish culture and language, and strategic position bordering Belgium and Germany.
  • 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. 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.
  • D. Flemish Brabant
    Flemish Brabant is a central Belgian province in the Flanders region, known for its mix of historic towns, rural landscapes, and proximity to Brussels.
  • 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_69a493494ec48190ae6751683625a9ba completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a55c99fc8190941c5fd18551792a completed March 1, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf00f0e08190b90a719952216204 completed March 8, 2026, 12:12 a.m.
Created at: March 1, 2026, 7:36 p.m.