Triple

T5455080
Position Surface form Disambiguated ID Type / Status
Subject 宋子文 E122459 entity
Predicate 出生地 P1 FINISHED
Object 中国上海 E5256 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: 中国上海 | Statement: [宋子文, 出生地, 中国上海]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 中国上海
Context triple: [宋子文, 出生地, 中国上海]
  • A. Shanghai Guoji Saichechang
    Shanghai Guoji Saichechang is a major motorsport race track in Shanghai, China, best known for hosting the Formula One Chinese Grand Prix.
  • B. Shenzhen, China
    Shenzhen, China is a major southern Chinese metropolis known for its rapid transformation into a global technology and manufacturing hub bordering Hong Kong.
  • C. Canton, China
    Canton, China is the former English name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • D. Shanghai chosen
    Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
  • E. Shanghai
    Shanghai is a major Ethereum network upgrade that introduced key changes such as enabling staked ETH withdrawals and improving the protocol’s efficiency and flexibility.
  • 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_69bd46424248819085282ddf50a565f3 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd91ed9a388190967e7ffaf9dbadc6 completed March 20, 2026, 6:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf41433abc8190998bdb0fa8b18041 completed March 22, 2026, 1:09 a.m.
Created at: March 20, 2026, 2:08 p.m.