Triple

T1481861
Position Surface form Disambiguated ID Type / Status
Subject Tøyen E30974 entity
Predicate locatedInAdministrativeTerritory 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: [Tøyen, locatedInAdministrativeTerritory, Oslo Municipality]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo Municipality
Context triple: [Tøyen, locatedInAdministrativeTerritory, 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 county
    Oslo county is Norway’s capital county, encompassing the city of Oslo and serving as the country’s political, economic, and cultural center.
  • C. Oslo chosen
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • D. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • E. Greater Oslo Region
    The Greater Oslo Region is the metropolitan area surrounding Norway’s capital, encompassing Oslo and its neighboring municipalities as a unified economic and commuter region.
  • 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_69a498fe55a88190ab7f9e40ace88e49 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c6782f088190930d25a56161e2b3 completed March 1, 2026, 11:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae95d90d2c8190b826a0b988537abf completed March 9, 2026, 9:41 a.m.
Created at: March 1, 2026, 8:11 p.m.