Triple

T17744482
Position Surface form Disambiguated ID Type / Status
Subject Inland Norway University of Applied Sciences E442949 entity
Predicate hasCampus P116 FINISHED
Object Elverum 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: Elverum | Statement: [Inland Norway University of Applied Sciences, hasCampus, Elverum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elverum
Context triple: [Inland Norway University of Applied Sciences, hasCampus, Elverum]
  • A. Elverum chosen
    Elverum is a town and municipality in Innlandet county in eastern Norway, known for its forestry, military camp, and role in Norwegian World War II history.
  • B. Torshov
    Torshov is a residential neighborhood in Oslo, Norway, known for its early 20th-century architecture, green spaces, and vibrant local culture.
  • C. Sarpsborg
    Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
  • D. Hønefoss
    Hønefoss is a Norwegian town known as a regional commercial and transport hub, situated along the Begna River northwest of Oslo.
  • E. Larvik
    Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
  • 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e47ad0c5b481909059bfa868cc4001 completed April 19, 2026, 6:48 a.m.
Created at: April 10, 2026, 10:09 a.m.