Triple

T13813877
Position Surface form Disambiguated ID Type / Status
Subject Snow Flower and the Secret Fan E331962 entity
Predicate starring P1507 FINISHED
Object Jiang Wu E1066006 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: Jiang Wu | Statement: [Snow Flower and the Secret Fan, starring, Jiang Wu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jiang Wu
Context triple: [Snow Flower and the Secret Fan, starring, Jiang Wu]
  • A. Jiang Wu chosen
    Jiang Wu is a Chinese actor known for his roles in both mainstream and art-house films, often portraying intense and complex characters.
  • B. Jiang Wan
    Jiang Wan was a prominent statesman and regent of the Shu Han kingdom during China’s Three Kingdoms period, known for succeeding Zhuge Liang in overseeing state affairs.
  • C. Jiang Baili
    Jiang Baili was a prominent early 20th-century Chinese military strategist and reformer who played a key role in modernizing China's armed forces and influencing Republican-era military thought.
  • D. Liang Congjie
    Liang Congjie was a prominent Chinese historian and environmental activist who founded Friends of Nature, one of China’s first environmental NGOs.
  • E. Luo Jin
    Luo Jin is a Chinese actor known for his roles in popular television dramas and films.
  • 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de027198f8819095da3e714ac241f5 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fbac802b7081909b36eebe85374594 completed May 6, 2026, 9:02 p.m.
Created at: April 9, 2026, 10:12 p.m.