Triple

T3353733
Position Surface form Disambiguated ID Type / Status
Subject Ian McShane E70555 entity
Predicate characterPortrayed P1507 FINISHED
Object Tai Lung
Tai Lung is the powerful snow leopard martial artist who serves as the main antagonist in the first Kung Fu Panda film.
E351735 NE FINISHED

How this triple was built (4 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: Tai Lung | Statement: [Ian McShane, characterPortrayed, Tai Lung]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tai Lung
Context triple: [Ian McShane, characterPortrayed, Tai Lung]
  • A. 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.
  • B. Unryu
    Unryu was a World War II-era Imperial Japanese Navy aircraft carrier that served in the Pacific Theater.
  • C. Chimaji Appa
    Chimaji Appa was an 18th-century Maratha military commander best known for leading successful campaigns against the Portuguese in western India, including the capture of Vasai (Bassein).
  • D. Ka-chiu
    Ka-chiu is the given name of John Lee Ka-chiu, the Chief Executive of Hong Kong and a former security official.
  • E. Masaru
    Masaru is a Japanese given name commonly used for males and borne by various notable figures in fields such as technology, sports, and entertainment.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tai Lung
Triple: [Ian McShane, characterPortrayed, Tai Lung]
Generated description
Tai Lung is the powerful snow leopard martial artist who serves as the main antagonist in the first Kung Fu Panda film.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tai Lung
Target entity description: Tai Lung is the powerful snow leopard martial artist who serves as the main antagonist in the first Kung Fu Panda film.
  • A. 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.
  • B. Unryu
    Unryu was a World War II-era Imperial Japanese Navy aircraft carrier that served in the Pacific Theater.
  • C. Chimaji Appa
    Chimaji Appa was an 18th-century Maratha military commander best known for leading successful campaigns against the Portuguese in western India, including the capture of Vasai (Bassein).
  • D. Baol
    Baol was a precolonial Wolof kingdom in what is now Senegal, known for succeeding the Wolof Empire as a regional political and economic power.
  • E. Ka-chiu
    Ka-chiu is the given name of John Lee Ka-chiu, the Chief Executive of Hong Kong and a former security official.
  • F. None of above. chosen

Provenance (5 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_69ad85a4ef7c8190a29e2bbd6fa454e4 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb24036848190bac779d17dfdce3b completed March 8, 2026, 5:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b325373a1c8190b26d883e2f0dd92b completed March 12, 2026, 8:42 p.m.
NEDg Description generation batch_69b329016c7c819098b494ae5d712036 completed March 12, 2026, 8:58 p.m.
NED2 Entity disambiguation (via description) batch_69b329aca800819091f287a2f00557a2 completed March 12, 2026, 9:01 p.m.
Created at: March 8, 2026, 3:13 p.m.