Triple

T831355
Position Surface form Disambiguated ID Type / Status
Subject Trøndelag E17971 entity
Predicate contains P35 FINISHED
Object Levanger E129647 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: Levanger | Statement: [Trøndelag, contains, Levanger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Levanger
Context triple: [Trøndelag, contains, Levanger]
  • A. Steinkjer
    Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
  • B. Porsgrunn
    Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
  • C. Gjøvik
    Gjøvik is a town and municipality in Innlandet county, Norway, known for its location along Lake Mjøsa and its mix of industrial heritage and modern sports and cultural facilities.
  • D. Verdal chosen
    Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
  • E. Skien
    Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
  • 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_69a4937c9c188190aaa216f6b466f452 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4abb4be948190ae757df85bdc40e4 completed March 1, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac762a53b48190a4d98b0416b4697c completed March 7, 2026, 7:02 p.m.
Created at: March 1, 2026, 7:38 p.m.