Triple

T21692875
Position Surface form Disambiguated ID Type / Status
Subject Centre for Statistics and Data Science E535414 entity
Predicate location P40 FINISHED
Object Oslo, Norway NE NERFINISHED

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, Norway | Statement: [Centre for Statistics and Data Science, location, Oslo, Norway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo, Norway
Context triple: [Centre for Statistics and Data Science, location, Oslo, Norway]
  • A. Oslo chosen
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • B. Oslo
    Oslo is a collection of shared libraries that provide common code and patterns used across various OpenStack projects.
  • C. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • D. Bergen, Sweden
    Bergen, Sweden is a small locality in Västra Götaland County known for its rural character and twinning relationship with Bergen, Germany.
  • E. Bergen
    Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c46a6ee481908836e1420fb78c9b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef9b7816388190bc9ce93da4503ea0 completed April 27, 2026, 5:23 p.m.
Created at: April 16, 2026, 6:45 p.m.