Triple

T1481864
Position Surface form Disambiguated ID Type / Status
Subject Tøyen E30974 entity
Predicate partOf P40 FINISHED
Object Borough of Gamle Oslo E128382 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: Borough of Gamle Oslo | Statement: [Tøyen, partOf, Borough of Gamle Oslo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borough of Gamle Oslo
Context triple: [Tøyen, partOf, Borough of Gamle Oslo]
  • A. Gamle Oslo district chosen
    Gamle Oslo district is a central borough of Norway’s capital city known for its historic neighborhoods, diverse population, and rapidly developing waterfront areas.
  • B. St. Hanshaugen district
    St. Hanshaugen district is a central borough of Oslo, Norway, known for its large public park, historic architecture, and vibrant urban neighborhoods.
  • C. Vestre Aker district
    Vestre Aker district is a largely affluent, residential borough in the western part of Oslo, Norway, known for its green areas and suburban character.
  • D. Østensjø district
    Østensjø district is a residential borough in the southeastern part of Oslo, Norway, known for its lakes, green spaces, and suburban character.
  • E. Grünerløkka district
    Grünerløkka district is a trendy, centrally located neighborhood in Oslo known for its vibrant street life, cafes, bars, and creative cultural scene.
  • 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_69ad15b1bea08190a1e21ddc15148d3a completed March 8, 2026, 6:22 a.m.
Created at: March 1, 2026, 8:11 p.m.