Triple

T205474
Position Surface form Disambiguated ID Type / Status
Subject University of Oslo E4601 entity
Predicate locatedIn P40 FINISHED
Object Oslo Municipality E3654 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: Oslo Municipality | Statement: [University of Oslo, locatedIn, Oslo Municipality]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo Municipality
Context triple: [University of Oslo, locatedIn, Oslo Municipality]
  • A. Oslo City Council
    Oslo City Council is the elected municipal legislature responsible for setting policies, budgets, and regulations for the city of Oslo, Norway.
  • B. Oslo chosen
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • C. Lillehammer
    Lillehammer is a Norwegian town in the Gudbrandsdalen valley, best known internationally for staging the 1994 Winter Olympics.
  • D. Copenhagen
    Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
  • E. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading 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_69a25737567c81908f9c505300239181 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c04e42481909e957cb34dc02731 completed Feb. 28, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69a36475d2608190ac11703d8b5e8107 completed Feb. 28, 2026, 9:56 p.m.
Created at: Feb. 28, 2026, 2:51 a.m.