Triple

T20018430
Position Surface form Disambiguated ID Type / Status
Subject Sande (Møre og Romsdal) E494782 entity
Predicate hasSettlement P1068 FINISHED
Object Larsnes 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: Larsnes | Statement: [Sande (Møre og Romsdal), hasSettlement, Larsnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Larsnes
Context triple: [Sande (Møre og Romsdal), hasSettlement, Larsnes]
  • A. Larsnes chosen
    Larsnes is a village in Møre og Romsdal county, Norway, known as a local hub for maritime industries and services in the Sande area.
  • B. Lysnes
    Lysnes is a small coastal village in northern Norway, situated within the former Lenvik municipality in Troms county.
  • C. Vangsnes
    Vangsnes is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and agricultural surroundings.
  • D. Nissedal
    Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
  • E. Vaksdal
    Vaksdal is a village in Vestland county, Norway, situated along the Veafjorden and known for its historic textile industry and railway connections.
  • 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_69da626bfd288190aa5d65098b6433ae completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6623e40748190b1abb0ead9acab4e completed April 20, 2026, 5:28 p.m.
Created at: April 11, 2026, 3:34 p.m.