Triple

T3145074
Position Surface form Disambiguated ID Type / Status
Subject Oppland E65743 entity
Predicate borderedBy P224 FINISHED
Object Hedmark E102451 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: Hedmark | Statement: [Oppland, borderedBy, Hedmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hedmark
Context triple: [Oppland, borderedBy, Hedmark]
  • A. Hedmark chosen
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • B. Buskerud
    Buskerud is a former county in southeastern Norway known for its varied landscape of forests, rivers, and mountains, including parts of the Hallingdal valley and Hardangervidda plateau.
  • C. Vestfold og Telemark
    Vestfold og Telemark is a former county in southeastern Norway known for its coastal towns, industrial heritage, and varied landscapes from fjords to inland forests and mountains.
  • D. Akershus county
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • E. Rogaland
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • 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_69ad8582f564819088c27e1f96153938 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada59797788190a8d71262888c5df0 completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b354528b00819095fa9b8f28f2135c completed March 13, 2026, 12:03 a.m.
Created at: March 8, 2026, 3:05 p.m.