Triple

T21775545
Position Surface form Disambiguated ID Type / Status
Subject Rauma Line E537563 entity
Predicate terminus P388 FINISHED
Object Åndalsnes 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: Åndalsnes | Statement: [Rauma Line, terminus, Åndalsnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Åndalsnes
Context triple: [Rauma Line, terminus, Åndalsnes]
  • A. Åndalsnes chosen
    Åndalsnes is a small Norwegian town known as a gateway to dramatic fjord and mountain landscapes, including popular hiking and climbing areas like Romsdalseggen and Trollveggen.
  • B. Norheimsund
    Norheimsund is a village in western Norway known as a regional center in the Hardanger region, noted for its scenic fjordside setting and proximity to the Steinsdalsfossen waterfall.
  • C. Skarsvåg
    Skarsvåg is a small fishing village in northern Norway often noted as one of the world’s northernmost permanently inhabited settlements.
  • D. Rennesøy
    Rennesøy is an island and former municipality in Rogaland county, southwestern Norway, known for its coastal landscape and proximity to the city of Stavanger.
  • E. Holsnøy
    Holsnøy is a large island in Vestland county, Norway, known for its rugged coastal landscape and proximity to the city of Bergen.
  • 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_69e0c470759c819094a215757113562b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f046291d808190b5111a8d4819909f completed April 28, 2026, 5:31 a.m.
Created at: April 16, 2026, 6:51 p.m.