Triple

T14774030
Position Surface form Disambiguated ID Type / Status
Subject Midden-Groningen E347206 entity
Predicate hasSettlement P1068 FINISHED
Object Noordbroek E850279 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: Noordbroek | Statement: [Midden-Groningen, hasSettlement, Noordbroek]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Noordbroek
Context triple: [Midden-Groningen, hasSettlement, Noordbroek]
  • A. Noordbroek chosen
    Noordbroek is a village in the Dutch province of Groningen, known for its historic church and rural character.
  • B. Westerbroek
    Westerbroek is a village in the Dutch province of Groningen, known for its rural character and surrounding peat and nature areas.
  • C. Bredevoort
    Bredevoort is a small historic town in the Dutch province of Gelderland, known for its well-preserved medieval character and its reputation as a national "book town."
  • D. Zonnemaire
    Zonnemaire is a small village in the Dutch province of Zeeland, notable as the birthplace of Nobel Prize–winning physicist Pieter Zeeman.
  • E. Zuidbroek
    Zuidbroek is a village in the province of Groningen in the northeastern Netherlands, known historically as a small canal-side settlement in a largely rural landscape.
  • 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_69d822e9b9e08190bedcc31a163fda82 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec81485e08190be35baafcf22b6f2 completed April 14, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0cfd26fc81909fba39c8705437ed completed May 8, 2026, 4:19 p.m.
Created at: April 10, 2026, 1:31 a.m.