Triple

T1094633
Position Surface form Disambiguated ID Type / Status
Subject Oslo City Hall E24244 entity
Predicate locatedIn P40 FINISHED
Object Oslo County E22828 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 County | Statement: [Oslo City Hall, locatedIn, Oslo County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo County
Context triple: [Oslo City Hall, locatedIn, Oslo County]
  • A. Oslo County chosen
    Oslo County is the administrative region that encompasses Norway’s capital city, Oslo, serving as a central hub for the country’s political, cultural, and academic institutions.
  • B. Viken county
    Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
  • C. Oppland
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • D. Kalmar County
    Kalmar County is an administrative region in southeastern Sweden that includes parts of the mainland and the island of Öland, known for its coastal landscapes and historical sites.
  • E. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • 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_69a4940542308190ac2a0b1f730b7cfc completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4b99d1e8c81909cf1178d68d38885 completed March 1, 2026, 10:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5ea6cc888190b06f640a1e4a5147 completed March 7, 2026, 5:21 p.m.
Created at: March 1, 2026, 7:42 p.m.