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.