Triple

T15647833
Position Surface form Disambiguated ID Type / Status
Subject Volda E376225 entity
Predicate hasNeighbouringMunicipality P224 FINISHED
Object Gloppen E385011 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: Gloppen | Statement: [Volda, hasNeighbouringMunicipality, Gloppen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gloppen
Context triple: [Volda, hasNeighbouringMunicipality, Gloppen]
  • A. Gloppen chosen
    Gloppen is a municipality in Vestland county, Norway, known for its fjord landscapes, agriculture, and the village of Sandane as its administrative center.
  • B. Gisundet
    Gisundet is a narrow strait in northern Norway that separates the island of Senja from the mainland and connects the Malangen fjord to the Gisundet sound.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Bjorli
    Bjorli is a Norwegian village known for its ski resort and scenic mountain surroundings in Innlandet county.
  • E. Gjerdrum
    Gjerdrum is a small rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to the Oslo metropolitan area.
  • 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_6a014134601c81909f7f4a95d558e067 completed May 11, 2026, 2:38 a.m.
Created at: April 10, 2026, 4:15 a.m.