Triple

T2934556
Position Surface form Disambiguated ID Type / Status
Subject Vadsø E79233 entity
Predicate region P40 FINISHED
Object Øst-Finnmark E81316 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: Øst-Finnmark | Statement: [Vadsø, region, Øst-Finnmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Øst-Finnmark
Context triple: [Vadsø, region, Øst-Finnmark]
  • A. Troms og Finnmark chosen
    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.
  • B. Møre og Romsdal
    Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
  • 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. Trøndelag
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • E. Oppland
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • 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_69ad8b0fbab081908f6a61567c045d8d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad983c84688190aa7ed5b8091fb140 completed March 8, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b12e181898819087ed4acfd24c00a1 completed March 11, 2026, 8:55 a.m.
Created at: March 8, 2026, 2:56 p.m.