Triple

T15751161
Position Surface form Disambiguated ID Type / Status
Subject Rana E381846 entity
Predicate borderedBy P224 FINISHED
Object Hemnes 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: Hemnes | Statement: [Rana, borderedBy, Hemnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hemnes
Context triple: [Rana, borderedBy, Hemnes]
  • A. Hemnes chosen
    Hemnes is a municipality in Nordland county, Norway, known for its mountainous landscapes, fjords, and outdoor recreation opportunities.
  • B. Tvedestrand
    Tvedestrand is a coastal town and municipality in southern Norway known for its wooden houses, maritime heritage, and picturesque archipelago.
  • C. Holmestrand
    Holmestrand is a coastal town and municipality in Vestfold, Norway, known for its harbor, steep hillsides, and role as a regional transport hub along the Oslofjord.
  • D. Sagene
    Sagene is a central district in Oslo, Norway, known for its historic industrial heritage along the Akerselva river and its mix of old workers’ housing and modern urban development.
  • E. Nittedal
    Nittedal is a municipality in Viken county, Norway, known for its forested landscapes and role as a commuter area north of Oslo.
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e05030e31081908c307a8dc7067db4 completed April 16, 2026, 2:57 a.m.
Created at: April 10, 2026, 4:47 a.m.