Triple

T22748748
Position Surface form Disambiguated ID Type / Status
Subject Hamme E562631 entity
Predicate borderedBy P224 FINISHED
Object Waasmunster NE NERFINISHED

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: Waasmunster | Statement: [Hamme, borderedBy, Waasmunster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Waasmunster
Context triple: [Hamme, borderedBy, Waasmunster]
  • A. Waasmunster chosen
    Waasmunster is a municipality in East Flanders, Belgium, known for its green, semi-rural character and location between Ghent and Antwerp.
  • B. Ravensburg
    Ravensburg is a historic town in southern Germany’s Baden-Württemberg state, known for its well-preserved medieval old town and as the namesake of the Ravensburger game and puzzle company.
  • C. Hüniken
    Hüniken is a small rural municipality in the canton of Solothurn in northern Switzerland.
  • D. Hintonburg
    Hintonburg is a historic, arts-oriented urban neighbourhood in west-central Ottawa, known for its mix of residential streets, independent shops, and cultural venues.
  • E. Fontanestadt
    Fontanestadt is the honorary nickname of the German town Neuruppin, referencing its association with the writer Theodor Fontane.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e24551ec7881909a9c924dbea155f6 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f179b822988190b5368ac1f4e1d70a completed April 29, 2026, 3:23 a.m.
Created at: April 17, 2026, 3:24 p.m.