Triple

T15461509
Position Surface form Disambiguated ID Type / Status
Subject Hardanger Bridge E371911 entity
Predicate connects P390 FINISHED
Object Ullensvang E373324 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: Ullensvang | Statement: [Hardanger Bridge, connects, Ullensvang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ullensvang
Context triple: [Hardanger Bridge, connects, Ullensvang]
  • A. Ullensvang chosen
    Ullensvang is a scenic municipality in Vestland county, Norway, known for its fruit orchards, fjord landscapes, and location along the Hardangerfjord.
  • B. Nissedal
    Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
  • C. Verdal
    Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
  • D. Trysil
    Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive outdoor recreation opportunities.
  • E. Bjerkreim
    Bjerkreim is a rural municipality in southwestern Norway known for its rivers, salmon fishing, and agricultural landscape.
  • 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_69d85cc8bd308190886949510b42e764 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f17663c8190b995c7c3129c90d6 completed April 16, 2026, 1:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00bafb761881908e7a891ef390982a completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 3:32 a.m.