Triple

T1229371
Position Surface form Disambiguated ID Type / Status
Subject Sentrum E26400 entity
Predicate borders P224 FINISHED
Object Grünerløkka E126346 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: Grünerløkka | Statement: [Sentrum, borders, Grünerløkka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grünerløkka
Context triple: [Sentrum, borders, Grünerløkka]
  • A. Grünerløkka district chosen
    Grünerløkka district is a trendy, centrally located neighborhood in Oslo known for its vibrant street life, cafes, bars, and creative cultural scene.
  • B. Frogner district
    Frogner district is an affluent central borough of Oslo, Norway, known for its historic architecture, embassies, and the famous Frogner Park with the Vigeland sculpture installation.
  • C. 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.
  • D. 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.
  • 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 (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_69a4948571c88190a9191e451e6035fd completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4be3dac2c8190914ff27173bb6b34 completed March 1, 2026, 10:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69acd46eefa48190baebc12fdf916941 completed March 8, 2026, 1:44 a.m.
Created at: March 1, 2026, 7:47 p.m.