Triple

T5994650
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Health Sciences, Oslo Metropolitan University E133437 entity
Predicate city P40 FINISHED
Object Oslo E3654 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 | Statement: [Faculty of Health Sciences, Oslo Metropolitan University, city, Oslo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo
Context triple: [Faculty of Health Sciences, Oslo Metropolitan University, city, Oslo]
  • A. Oslo chosen
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • B. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • C. Bergen
    Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
  • D. Bergen
    Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
  • E. Bergens
    The Bergens are a race of gloomy, troll-eating creatures who serve as the primary villains in the animated film "Trolls."
  • 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_69c00870ddbc81909880fa3864f4f38d completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04e92e1448190bbf961a8243082ee completed March 22, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c107bf6994819089e1211ffe343094 completed March 23, 2026, 9:28 a.m.
Created at: March 22, 2026, 4:05 p.m.