Triple

T3882381
Position Surface form Disambiguated ID Type / Status
Subject European route E6 E92854 entity
Predicate passesThrough P225 FINISHED
Object Tromsø E56624 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: Tromsø | Statement: [European route E6, passesThrough, Tromsø]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tromsø
Context triple: [European route E6, passesThrough, Tromsø]
  • A. Tromsø chosen
    Tromsø is a city in northern Norway known for its Arctic location, vibrant cultural scene, and prominence as a viewing spot for the Northern Lights.
  • B. Bodø
    Bodø is a coastal city in northern Norway known as a regional hub for culture, transport, and access to Arctic nature.
  • 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. Ålesund
    Ålesund is a coastal Norwegian city renowned for its distinctive Art Nouveau architecture and location across several islands in Western Norway.
  • E. Kristiansund
    Kristiansund is a coastal city in western Norway known for its historic clipfish industry, distinctive layout across several islands, and picturesque harbor.
  • 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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec8e8b3481909617ca0e37f8a6d4 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd678c2c088190a67867248f89c46a completed March 20, 2026, 3:28 p.m.
Created at: March 9, 2026, 3:20 p.m.