Triple

T5180002
Position Surface form Disambiguated ID Type / Status
Subject Notodden E116895 entity
Predicate previouslyPartOf P5057 FINISHED
Object Telemark county E465165 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: Telemark county | Statement: [Notodden, previouslyPartOf, Telemark county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Telemark county
Context triple: [Notodden, previouslyPartOf, Telemark county]
  • A. Telemark county chosen
    Telemark county was a former county in southeastern Norway known for its rich folk culture, traditional architecture, and varied landscape of mountains, forests, and coastal areas.
  • B. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • C. Akershus county
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • D. Hedmarken
    Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
  • E. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • 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_69bd446140f08190becb93c61158f27f completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd799a322c8190b8a590cfe70761f5 completed March 20, 2026, 4:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfd42f73b88190bb69bffa6a8b9efe completed March 22, 2026, 11:36 a.m.
Created at: March 20, 2026, 1:45 p.m.