Triple

T14767101
Position Surface form Disambiguated ID Type / Status
Subject western Beijing E347024 entity
Predicate hasNotableArea P494 FINISHED
Object Xierqi E406595 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: Xierqi | Statement: [western Beijing, hasNotableArea, Xierqi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xierqi
Context triple: [western Beijing, hasNotableArea, Xierqi]
  • A. Xierqi chosen
    Xierqi is a major technology and business hub in Beijing, known for its concentration of high-tech companies and convenient transportation links.
  • B. Qiying
    Qiying was a Qing dynasty statesman and diplomat who played a key role in negotiating several unequal treaties with Western powers in the mid-19th century.
  • C. Xingqing
    Xingqing was the principal city and political center of the Tangut-led Western Xia dynasty in northwestern China.
  • D. Xiadu
    Xiadu was an ancient Chinese city that served as a major political and cultural center of the Warring States–period Yan kingdom.
  • E. Xiangmei
    Xiangmei is the Chinese given name of Anna Chennault, a prominent Chinese-American journalist, author, and influential political figure in mid-20th-century U.S.-China relations.
  • 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_69d822e8896c819091169882f9b20486 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7f576c881909da70627f5897c94 completed April 14, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0cf68d94819093567bc630f67b60 completed May 8, 2026, 4:19 p.m.
Created at: April 10, 2026, 1:30 a.m.