Triple

T1351701
Position Surface form Disambiguated ID Type / Status
Subject Ulsan E28895 entity
Predicate borderedBy P224 FINISHED
Object Yangsan E159717 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: Yangsan | Statement: [Ulsan, borderedBy, Yangsan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yangsan
Context triple: [Ulsan, borderedBy, Yangsan]
  • A. Yangsan chosen
    Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
  • B. Hanyang
    Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
  • C. Luyang
    Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
  • D. Ma’anshan
    Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
  • E. Xiantao
    Xiantao is a county-level city in central China’s Hubei province, known for its location on the Jianghan Plain and its role as a regional agricultural 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c26b1b4881908ae4b1b2c9b268a0 completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ace56298708190819518926b2b608f completed March 8, 2026, 2:56 a.m.
Created at: March 1, 2026, 7:56 p.m.