Triple

T1138513
Position Surface form Disambiguated ID Type / Status
Subject Oslo Opera House E23193 entity
Predicate region 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 Opera House, region, Oslo County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo County
Context triple: [Oslo Opera House, region, 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. 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. Viken county
    Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
  • D. Oppland
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • E. 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.
  • 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_69a493ec75988190b63a11bafaec29b4 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc25dda481909a26d726fdbdbb50 completed March 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac7f30222081909679902c6e0d4790 completed March 7, 2026, 7:40 p.m.
Created at: March 1, 2026, 7:44 p.m.