Triple

T1328700
Position Surface form Disambiguated ID Type / Status
Subject Weespertrekvaart E28390 entity
Predicate endPoint P390 FINISHED
Object Weesp E548815 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: Weesp | Statement: [Weespertrekvaart, endPoint, Weesp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Weesp
Context triple: [Weespertrekvaart, endPoint, Weesp]
  • A. Weesp chosen
    Weesp is a historic town in the province of North Holland in the Netherlands, known for its canals, fortified structures, and traditional Dutch architecture.
  • B. Oosterhout
    Oosterhout is a town and municipality in the southern Netherlands known for its historic monasteries and proximity to the city of Breda.
  • C. Roosendaal
    Roosendaal is a city in the southern Netherlands known as a regional center for commerce and transport near the Belgian border.
  • D. Zoeterwoude
    Zoeterwoude is a small Dutch municipality and village known for its rural character and location near Leiden in the province of South Holland.
  • E. Barendrecht
    Barendrecht is a suburban town in the western Netherlands, located just south of Rotterdam and known for its residential character and logistics industry.
  • 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_69a498540a2481909e807a762280d3ba completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1c1d8188190b15a641a08345adc completed March 1, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b069d3fc8190bac9d178a571b72d completed March 23, 2026, 3:15 a.m.
Created at: March 1, 2026, 7:55 p.m.