Triple

T8046367
Position Surface form Disambiguated ID Type / Status
Subject Molde FK E187563 entity
Predicate region P40 FINISHED
Object Møre og Romsdal E114915 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: Møre og Romsdal | Statement: [Molde FK, region, Møre og Romsdal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Møre og Romsdal
Context triple: [Molde FK, region, Møre og Romsdal]
  • A. Møre og Romsdal chosen
    Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
  • B. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • C. Rogaland
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • D. Stjørdal
    Stjørdal is a Norwegian town and municipality in Trøndelag county, known as a regional transport hub near Trondheim and for its location at the mouth of the Stjørdalselva river.
  • E. Hordaland
    Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and 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_69ca82b00cb48190b59a300f70e97bd7 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3f4d9ddc8190a7dcf85ed47ee6c3 completed March 31, 2026, 3:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd340abffc8190bbc17aa9b775a9cb completed April 1, 2026, 3:04 p.m.
Created at: March 30, 2026, 5:24 p.m.