Triple

T12150863
Position Surface form Disambiguated ID Type / Status
Subject Bardufoss E289447 entity
Predicate hasNearbyTown P3883 FINISHED
Object Finnsnes E315835 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: Finnsnes | Statement: [Bardufoss, hasNearbyTown, Finnsnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Finnsnes
Context triple: [Bardufoss, hasNearbyTown, Finnsnes]
  • A. Finnsnes chosen
    Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
  • B. Øystre Slidre
    Øystre Slidre is a rural municipality in Innlandet county, Norway, known for its mountainous landscapes, lakes, and traditional Norwegian cultural heritage.
  • C. Glåma
    Glåma is the longest and largest river in Norway, flowing through eastern parts of the country before emptying into the Oslofjord.
  • D. Stryn
    Stryn is a municipality in Vestland county, Norway, known for its dramatic fjord and mountain landscapes, glaciers, and popular outdoor tourism activities.
  • E. Snåsa
    Snåsa is a rural municipality in Trøndelag county, Norway, known for its large lakes, forests, and strong South Sámi cultural heritage.
  • 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_69d6ab4c6710819097a9d228382dde43 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915ae736c8190aaab05efb93c5854 completed April 10, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65556e718819092736cd89c326fb5 completed May 2, 2026, 7:49 p.m.
Created at: April 8, 2026, 9:49 p.m.