Triple

T15690321
Position Surface form Disambiguated ID Type / Status
Subject Voss Municipality E380310 entity
Predicate region P40 FINISHED
Object Voss district E740156 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: Voss district | Statement: [Voss Municipality, region, Voss district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Voss district
Context triple: [Voss Municipality, region, Voss district]
  • A. Voss district chosen
    Voss district is a traditional region in western Norway known for its mountainous landscapes, outdoor activities, and strong cultural heritage.
  • B. Hadeland district
    Hadeland district is a traditional rural region in southeastern Norway known for its historic farms, forests, and lakes north of Oslo.
  • C. Sagene district
    Sagene district is a central borough of Oslo, Norway, known for its historic industrial areas, riverside parks, and vibrant urban neighborhoods.
  • D. Ringerike district
    Ringerike district is a historic region in southeastern Norway known for its cultural heritage, distinctive landscape, and early medieval significance.
  • E. Bjerke district
    Bjerke district is a residential borough in the northeastern part of Oslo, Norway, known for its mix of apartment blocks, green areas, and local commercial centers.
  • 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_69d86d99e860819094b6957cde470f2c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04f4e59988190aaf12f6a07c8f0e4 completed April 16, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a011b31b3b4819095d0c24ee471e2a9 completed May 10, 2026, 11:56 p.m.
Created at: April 10, 2026, 4:44 a.m.