Triple

T17038072
Position Surface form Disambiguated ID Type / Status
Subject Pyeongchang E413371 entity
Predicate hasRegionCode P3446 FINISHED
Object Gangwon E551121 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: Gangwon | Statement: [Pyeongchang, hasRegionCode, Gangwon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gangwon
Context triple: [Pyeongchang, hasRegionCode, Gangwon]
  • A. Gangwon Province chosen
    Gangwon Province is a mountainous region in northeastern South Korea known for its natural scenery, ski resorts, and role as a host area for the 2018 Pyeongchang Winter Olympics.
  • B. Goesan-gun
    Goesan-gun is a rural county in central South Korea known for its mountainous landscapes, agriculture, and traditional cultural heritage.
  • C. Gyeongbuk
    Gyeongbuk is a province in eastern South Korea known for its historical sites, cultural heritage, and scenic rural landscapes.
  • D. Ryanggang Province
    Ryanggang Province is a mountainous administrative region in northern North Korea that includes part of the Korean Peninsula’s highest and most sacred peak, Paektu Mountain.
  • E. Minho region
    The Minho region is a historic and culturally rich area in northwest Portugal, known for its lush green landscapes, traditional cuisine, and production of Vinho Verde wine.
  • 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_69d886cd18288190b006abab23f811b7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d8f45f84819092cfb27cc33da026 completed April 18, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012ed2ad708190a250762997611569 completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:33 a.m.