Triple

T15510608
Position Surface form Disambiguated ID Type / Status
Subject Oldevatnet E368698 entity
Predicate locatedInMunicipality P40 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: [Oldevatnet, locatedInMunicipality, Stryn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stryn
Context triple: [Oldevatnet, locatedInMunicipality, 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_69d85a1794cc8190b0b428716296e63e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03fd008708190a3657863eb9ac626 completed April 16, 2026, 1:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff997b9c9081908f6a68e28a50a359 completed May 9, 2026, 8:30 p.m.
Created at: April 10, 2026, 3:55 a.m.