Triple

T13118841
Position Surface form Disambiguated ID Type / Status
Subject Vadsø Museum – Ruija kvenmuseum E311670 entity
Predicate locatedIn P40 FINISHED
Object Finnmark E398878 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: Finnmark | Statement: [Vadsø Museum – Ruija kvenmuseum, locatedIn, Finnmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Finnmark
Context triple: [Vadsø Museum – Ruija kvenmuseum, locatedIn, Finnmark]
  • A. Finnmark chosen
    Finnmark is a sparsely populated, historically Norwegian region in the far northeast of Scandinavia, known for its Arctic climate, Sami culture, and dramatic coastal and tundra landscapes.
  • B. Troms og Finnmark
    Troms og Finnmark is Norway’s northernmost and largest county, known for its Arctic landscapes, Sami culture, and phenomena like the midnight sun and northern lights.
  • C. Nordland
    Nordland is a long coastal county in northern Norway known for its dramatic fjords, islands like the Lofoten archipelago, and Arctic landscapes.
  • D. Nordlandet
    Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
  • E. Nordland county
    Nordland county is a long, coastal region in northern Norway known for its dramatic fjords, islands, and Arctic landscapes.
  • 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_69d806a9fe888190b081e2d9ea665d6c completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98182011c8190a504678affbb7787 completed April 10, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f70a25c3b4819090511525babcd2ba completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:06 p.m.