Triple

T15647832
Position Surface form Disambiguated ID Type / Status
Subject Volda E376225 entity
Predicate hasNeighbouringMunicipality P224 FINISHED
Object Stryn E375474 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: Stryn | Statement: [Volda, hasNeighbouringMunicipality, Stryn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stryn
Context triple: [Volda, hasNeighbouringMunicipality, Stryn]
  • A. Stryn chosen
    Stryn is a municipality in Vestland county, Norway, known for its dramatic fjord and mountain landscapes, glaciers, and popular outdoor tourism activities.
  • B. Sørreisa
    Sørreisa is a small coastal municipality and village area in northern Norway known for its fjords and rural Arctic landscape.
  • C. Vennesla
    Vennesla is a municipality in Agder county in southern Norway, known for its industrial heritage and scenic river valley setting.
  • D. Nissedal
    Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
  • E. Sørenga
    Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
  • 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_69d85cd1564c8190991adda63bfab4b0 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ed7212c8190be6ff76afa25f7ca completed April 16, 2026, 2:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0139e10e94819092b71606dbe4f5d5 completed May 11, 2026, 2:07 a.m.
Created at: April 10, 2026, 4:15 a.m.