Triple

T4083986
Position Surface form Disambiguated ID Type / Status
Subject Midden-Delfland E87543 entity
Predicate locatedNear P294 FINISHED
Object Maassluis E119115 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: Maassluis | Statement: [Midden-Delfland, locatedNear, Maassluis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maassluis
Context triple: [Midden-Delfland, locatedNear, Maassluis]
  • A. Maassluis chosen
    Maassluis is a historic port town in the province of South Holland in the Netherlands, situated along the Nieuwe Waterweg west of Rotterdam.
  • B. Hellevoetsluis
    Hellevoetsluis is a historic Dutch port town known for its maritime heritage and coastal location in the western Netherlands.
  • C. Hulst
    Hulst is a historic fortified town and municipality in the Dutch province of Zeeland, near the border with Belgium.
  • D. Onderdendam
    Onderdendam is a small historic village in the Dutch province of Groningen, known for its canals, bridges, and traditional architecture.
  • E. Bleiswijk
    Bleiswijk is a village in the Dutch province of South Holland, known for its greenhouse horticulture and location within the municipality of Lansingerland.
  • 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_69aed9435cf48190ad1da737c962d19d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefc7a4b488190ab466e2c50329ab3 completed March 9, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69d2281e332c819093444685d37b9251 completed April 5, 2026, 9:15 a.m.
Created at: March 9, 2026, 3:39 p.m.