Triple

T9610524
Position Surface form Disambiguated ID Type / Status
Subject NC State Wolfpack E232086 entity
Predicate mascot P52 FINISHED
Object Mr. Wuf E103296 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. Wuf | Statement: [NC State Wolfpack, mascot, Mr. Wuf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mr. Wuf
Context triple: [NC State Wolfpack, mascot, Mr. Wuf]
  • A. Mr. Wuf chosen
    Mr. Wuf is the costumed wolf mascot who represents North Carolina State University's athletic teams and school spirit.
  • B. Mr. Wong
    Mr. Wong is the enigmatic criminal mastermind and primary antagonist in the 1934 mystery film "The Mysterious Mr. Wong."
  • C. Chi-Fu
    Chi-Fu is the pompous and bureaucratic imperial advisor in Disney's 1998 animated film "Mulan," often serving as a comedic antagonist to the protagonist's efforts.
  • D. Mr. Gao
    Mr. Gao is the central protagonist of Ang Lee’s film "The Wedding Banquet," a Taiwanese immigrant in New York who stages a sham marriage to satisfy his traditional parents while secretly living with his male partner.
  • E. Lord Shen
    Lord Shen is the main peacock antagonist in Kung Fu Panda 2, a ruthless and power-obsessed warlord who seeks to conquer China using deadly fireworks-based weaponry.
  • 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_69ca8485a90c819094fe40b42fde9d70 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9a85d4c881909ccab2e972d97e68 completed April 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69d179513f9081909bcd9a456c640ba3 completed April 4, 2026, 8:49 p.m.
Created at: March 30, 2026, 8:08 p.m.