Triple

T13992342
Position Surface form Disambiguated ID Type / Status
Subject Beijing–Kowloon Railway E336610 entity
Predicate connectsCity P4245 FINISHED
Object Anyang E77170 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: Anyang | Statement: [Beijing–Kowloon Railway, connectsCity, Anyang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anyang
Context triple: [Beijing–Kowloon Railway, connectsCity, Anyang]
  • A. Anyang chosen
    Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
  • B. Anyang
    Anyang is a mid-sized South Korean city in the Seoul Capital Area known for its residential districts, light industry, and proximity to central Seoul.
  • C. Taian
    Taian is a prefecture-level city in eastern China's Shandong province, best known as the gateway to the sacred Mount Tai.
  • D. Hejin
    Hejin is a county-level city in southern Shanxi Province, China, situated along the Fen River near its confluence with the Yellow River.
  • E. Luoyang
    Luoyang is one of China’s oldest and most historically significant cities, renowned as an ancient imperial capital and cultural center along the Yellow River.
  • 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_69d81c639e808190a0e4b4f3d31c6a59 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2eb3b5d881909f15a1e08bb202f3 completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbac98ca448190b585ef69a4e4bfca completed May 6, 2026, 9:03 p.m.
Created at: April 9, 2026, 10:19 p.m.