Triple

T347430
Position Surface form Disambiguated ID Type / Status
Subject 2002 Winter Olympics E6971 entity
Predicate biddingCity P7900 FINISHED
Object Östersund E38859 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: Östersund | Statement: [2002 Winter Olympics, biddingCity, Östersund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Östersund
Context triple: [2002 Winter Olympics, biddingCity, Östersund]
  • A. Östersund chosen
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • B. Trollhättan
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • C. Uppsala
    Uppsala is a historic Swedish city north of Stockholm, known for its prestigious university, medieval cathedral, and role as a cultural and ecclesiastical center.
  • D. Lidingö Municipality
    Lidingö Municipality is a suburban island municipality in eastern Sweden, forming part of the Stockholm urban area and known for its affluent residential character and natural surroundings.
  • E. Gothenburg
    Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb1a37c08190b1380f6bf8513a37 completed Feb. 28, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e57497948190918b163378036fd4 completed March 1, 2026, 7:06 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.