Triple

T1229347
Position Surface form Disambiguated ID Type / Status
Subject Sentrum E26400 entity
Predicate locatedInRegion 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: [Sentrum, locatedInRegion, Oslo County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo County
Context triple: [Sentrum, locatedInRegion, 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_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_69ac93bf346081908a36a25b6616009a completed March 7, 2026, 9:08 p.m.
Created at: March 1, 2026, 7:47 p.m.