Triple

T21882877
Position Surface form Disambiguated ID Type / Status
Subject Waterland E540332 entity
Predicate containsSettlement P847 FINISHED
Object Ilpendam 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: Ilpendam | Statement: [Waterland, containsSettlement, Ilpendam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ilpendam
Context triple: [Waterland, containsSettlement, Ilpendam]
  • A. Ilpendam chosen
    Ilpendam is a small village in the Dutch province of North Holland, known for its traditional rural character and proximity to Amsterdam.
  • B. Spaarndam
    Spaarndam is a historic Dutch village in North Holland, known for its old sluices and dikes along the river Spaarne and its traditional waterfront charm.
  • C. Gaasperdam
    Gaasperdam is a residential neighborhood in the southeastern part of Amsterdam known for its green spaces and proximity to the Gaasperplas recreational area.
  • D. Monnickendam
    Monnickendam is a historic fishing town in North Holland, Netherlands, known for its well-preserved old harbor and traditional Dutch architecture.
  • E. Veenendaal
    Veenendaal is a Dutch town and municipality in the central Netherlands, known for its location between Utrecht and the Veluwe and its mix of residential, commercial, and light industrial areas.
  • 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_69e0c479a98081908ce333853fdd4348 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f118e890088190aa3023c78a99a536 completed April 28, 2026, 8:30 p.m.
Created at: April 16, 2026, 7:05 p.m.