Triple

T22151581
Position Surface form Disambiguated ID Type / Status
Subject Gamlebyen Church E547425 entity
Predicate locatedIn P40 FINISHED
Object Old Town, Oslo E523927 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: Old Town, Oslo | Statement: [Gamlebyen Church, locatedIn, Old Town, Oslo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Old Town, Oslo
Context triple: [Gamlebyen Church, locatedIn, Old Town, Oslo]
  • A. Oslo East chosen
    Oslo East is the eastern part of Norway’s capital city, often associated with working-class neighborhoods, cultural diversity, and a strong local football supporter culture.
  • B. Marienlyst, Oslo
    Marienlyst, Oslo is a neighborhood in Norway’s capital city best known as the long-time home of the Norwegian Broadcasting Corporation’s main facilities.
  • C. Sentrum, Oslo
    Sentrum is the central borough of Oslo, Norway, encompassing the city’s main downtown area, key commercial districts, and major transport hubs.
  • D. Lyngseidet
    Lyngseidet is a small coastal village in northern Norway, known for its scenic fjord and mountain surroundings on the Lyngen Peninsula.
  • E. Majorstuen, Oslo
    Majorstuen is a central neighborhood in Oslo, Norway, known for its busy transport hub, shopping streets, and cultural institutions.
  • 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_69e11e3b52088190ad5df386d01eb2fb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f129f4b05c8190a824f55804f6d4d5 completed April 28, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0ae047da448190be302a3e28360a14 completed May 18, 2026, 9:47 a.m.
Created at: April 16, 2026, 8:33 p.m.