Triple

T15763522
Position Surface form Disambiguated ID Type / Status
Subject Ellingsøya E382157 entity
Predicate hasOfficialName P66 FINISHED
Object Ellingsøya 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: Ellingsøya | Statement: [Ellingsøya, hasOfficialName, Ellingsøya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ellingsøya
Context triple: [Ellingsøya, hasOfficialName, Ellingsøya]
  • A. Ellingsøya chosen
    Ellingsøya is an island in Møre og Romsdal county, Norway, known for its close connection to the coastal town of Ålesund and its scenic fjord landscape.
  • B. Ytterøya
    Ytterøya is an island in Trøndelag county, central Norway, known for its rural landscape and location within the Trondheimsfjord.
  • C. Magerøya
    Magerøya is a remote Norwegian island in the Arctic Ocean, best known as the location of the North Cape, a popular tourist destination often considered the northernmost point of mainland Europe accessible by road.
  • D. Tysnesøya
    Tysnesøya is a large island in western Norway known for its scenic coastal landscapes and location within the municipality of Tysnes.
  • E. Moskenesøya
    Moskenesøya is a rugged island in Norway’s Lofoten archipelago, known for its dramatic mountains, fishing villages, and scenic coastal landscapes.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b6c9fc8190a1bcf763c4b04b12 completed April 16, 2026, 3 a.m.
Created at: April 10, 2026, 4:47 a.m.