Triple

T13808071
Position Surface form Disambiguated ID Type / Status
Subject Toten E331810 entity
Predicate partOf P40 FINISHED
Object Oppland traditional region E98763 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: Oppland traditional region | Statement: [Toten, partOf, Oppland traditional region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oppland traditional region
Context triple: [Toten, partOf, Oppland traditional region]
  • A. Oppland chosen
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • B. Trøndelag
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • C. Dovre region
    The Dovre region is a mountainous area in central Norway known for its rugged landscapes, national parks, and rich wildlife, including wild reindeer.
  • D. Hadeland district
    Hadeland district is a traditional rural region in southeastern Norway known for its historic farms, forests, and lakes north of Oslo.
  • E. Jæren region
    The Jæren region is a coastal area in southwestern Norway known for its flat, fertile farmland, long sandy beaches, and the city of Stavanger as its main urban center.
  • 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de026eae8481908b8880635e6a9152 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b08fbc348190a199c5d92e0e46be completed May 3, 2026, 8:31 p.m.
Created at: April 9, 2026, 10:12 p.m.