Triple

T8627125
Position Surface form Disambiguated ID Type / Status
Subject Wusi Yundong E204305 entity
Predicate associatedWithConcept P531 FINISHED
Object Mr. Science E204303 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: Mr. Science | Statement: [Wusi Yundong, associatedWithConcept, Mr. Science]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mr. Science
Context triple: [Wusi Yundong, associatedWithConcept, Mr. Science]
  • A. Mr. Science chosen
    Mr. Science is a symbolic figure representing the ideals of modern scientific rationality and progress that Chinese intellectuals championed during the May Fourth Movement.
  • B. The Professor
    The Professor is the mastermind strategist and enigmatic leader who orchestrates the meticulously planned heists in the Spanish series "Money Heist."
  • C. The Professor
    The Professor is the nickname of Ghanaian former professional boxer Azumah Nelson, a legendary world champion widely regarded as one of Africa’s greatest fighters.
  • D. Sayanci
    Sayanci is a West Chadic language spoken in parts of northern Nigeria.
  • E. Mr. Science Fiction
    Mr. Science Fiction is the nickname of Forrest J Ackerman, a pioneering science fiction fan, editor, and literary agent renowned for his vast genre collection and influence on sci-fi fandom.
  • 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_69ca834a4ea0819094970dceb9e389f3 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc472ccc0c81908e0708c94a7cbe65 completed March 31, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69cecc9abd1081909d45af7498ec7c34 completed April 2, 2026, 8:07 p.m.
Created at: March 30, 2026, 6:26 p.m.