Triple

T16283917
Position Surface form Disambiguated ID Type / Status
Subject NRK Rogaland E395339 entity
Predicate serviceArea P82 FINISHED
Object Ryfylke E329313 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: Ryfylke | Statement: [NRK Rogaland, serviceArea, Ryfylke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ryfylke
Context triple: [NRK Rogaland, serviceArea, Ryfylke]
  • A. Ryfylke chosen
    Ryfylke is a traditional district in southwestern Norway known for its fjords, islands, and mountainous coastal landscape in Rogaland county.
  • B. Fjordane
    Fjordane is a traditional district in western Norway known for its dramatic fjord landscapes and coastal scenery.
  • C. Romsdal
    Romsdal is a traditional district in Møre og Romsdal county in western Norway, known for its dramatic fjords, mountains, and the town of Molde.
  • D. Nordmøre
    Nordmøre is a traditional district in the northern part of Møre og Romsdal county in western Norway, known for its coastal landscapes, fjords, and fishing communities.
  • E. Nordhordland
    Nordhordland is a traditional district in western Norway known for its coastal landscapes, fjords, 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 (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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24912c5808190a0d9c9f491315068 completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0242d952988190b9b3eaca8fa92431 completed May 11, 2026, 8:58 p.m.
Created at: April 10, 2026, 5:05 a.m.