Triple

T18381569
Position Surface form Disambiguated ID Type / Status
Subject Årnes E446461 entity
Predicate region P40 FINISHED
Object Akershus og omegn NE NERFINISHED

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: Akershus og omegn | Statement: [Årnes, region, Akershus og omegn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Akershus og omegn
Context triple: [Årnes, region, Akershus og omegn]
  • A. Akershus area
    Akershus area is a historic fortified district in Oslo, Norway, centered around the medieval Akershus Fortress that has long served as a key military and administrative stronghold.
  • B. Akershus chosen
    Akershus is a historical county in southeastern Norway that encompassed areas around the capital Oslo and played a key role in the region’s administrative and military history.
  • C. Oslo East
    Oslo East is the eastern part of Norway’s capital city, often associated with working-class neighborhoods, cultural diversity, and a strong local football supporter culture.
  • D. Gamle Aker
    Gamle Aker is a historic district in Oslo, Norway, known for its medieval church and significant cultural heritage sites.
  • E. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f370b88190b1e5081c2c238e7f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e5179b60f88190adf39e85375bd11b completed April 19, 2026, 5:57 p.m.
Created at: April 10, 2026, 10:45 a.m.