Triple

T21963588
Position Surface form Disambiguated ID Type / Status
Subject Grønland E542400 entity
Predicate partOf P40 FINISHED
Object Oslo city center 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 city center | Statement: [Grønland, partOf, Oslo city center]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oslo city center
Context triple: [Grønland, partOf, Oslo city center]
  • A. Sentrum, Oslo chosen
    Sentrum is the central borough of Oslo, Norway, encompassing the city’s main downtown area, key commercial districts, and major transport hubs.
  • B. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • C. Oslo
    Oslo is a collection of shared libraries that provide common code and patterns used across various OpenStack projects.
  • D. Majorstuen, Oslo
    Majorstuen is a central neighborhood in Oslo, Norway, known for its busy transport hub, shopping streets, and cultural institutions.
  • E. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • 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_69e0c47fab1081908dc74a6545dbb051 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f12458e4488190a04f8d3958854b49 completed April 28, 2026, 9:19 p.m.
Created at: April 16, 2026, 8:01 p.m.