Triple

T4434735
Position Surface form Disambiguated ID Type / Status
Subject European route E134 E95620 entity
Predicate passesThrough P225 FINISHED
Object Rogaland county E139000 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: Rogaland county | Statement: [European route E134, passesThrough, Rogaland county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rogaland county
Context triple: [European route E134, passesThrough, Rogaland county]
  • A. Rogaland chosen
    Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
  • B. Hordaland
    Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and the city of Bergen.
  • C. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • D. Oslo county
    Oslo county is Norway’s capital county, encompassing the city of Oslo and serving as the country’s political, economic, and cultural center.
  • E. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • 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_69b3453ea2b48190a26f154b3b8fece5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35588e99881908fea7b71a33e2bb6 completed March 13, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69bf410bd6b08190a0ad4af6e5f4b0b8 completed March 22, 2026, 1:08 a.m.
Created at: March 12, 2026, 11:31 p.m.