Triple

T10220902
Position Surface form Disambiguated ID Type / Status
Subject Oldambtmeer E242574 entity
Predicate partOf P40 FINISHED
Object Blauwestad project E242582 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: Blauwestad project | Statement: [Oldambtmeer, partOf, Blauwestad project]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blauwestad project
Context triple: [Oldambtmeer, partOf, Blauwestad project]
  • A. Blauwestad residential and recreational area chosen
    Blauwestad residential and recreational area is a planned lakeside living and leisure development in the Oldambt region of the Netherlands, featuring waterfront housing, nature areas, and recreational facilities.
  • B. Bloemfontein waterworks
    Bloemfontein waterworks was a strategically vital water supply installation for the city of Bloemfontein during the Second Boer War.
  • C. Bothasig
    Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
  • D. Sasolburg
    Sasolburg is an industrial town in South Africa’s Free State province, known primarily for its large petrochemical complex and proximity to the Vaal River.
  • E. Lichtstad
    Lichtstad is the Dutch nickname for the city of Eindhoven, reflecting its historic association with the lighting industry and companies like Philips.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa72b258819097d8d50a714e19dc completed April 6, 2026, 12:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6a82c98fc8190929b7b56f9a6e60d completed April 8, 2026, 7:10 p.m.
Created at: April 6, 2026, 11:09 a.m.