Triple

T3145036
Position Surface form Disambiguated ID Type / Status
Subject Innlandet E65742 entity
Predicate hasBorderWith P224 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: [Innlandet, hasBorderWith, Møre og Romsdal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Møre og Romsdal
Context triple: [Innlandet, hasBorderWith, 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. Rogaland
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • C. Trøndelag
    Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
  • D. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • E. Oppland
    Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
  • 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_69ad8582f564819088c27e1f96153938 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada595d4548190b720a6131817833b completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3340d5224819086e66d66e136e666 completed March 12, 2026, 9:45 p.m.
Created at: March 8, 2026, 3:05 p.m.