Triple
T3249173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulan (1998 film) |
E68134
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Fa Zhou
Fa Zhou is Mulan’s elderly, honorable father and a former soldier whose injury and devotion to family drive much of the film’s emotional conflict.
|
E342387
|
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: Fa Zhou | Statement: [Mulan (1998 film), character, Fa Zhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fa Zhou Context triple: [Mulan (1998 film), character, Fa Zhou]
-
A.
Zhou
Zhou is a common Chinese surname borne by many notable figures in Chinese history and politics.
-
B.
Kun Huang
Kun Huang was a prominent Chinese physicist and crystallographer known for his influential work in solid-state physics and lattice dynamics.
-
C.
Wang Jian
Wang Jian was a prominent Qin dynasty general whose military campaigns were crucial in the unification of China under Qin rule.
-
D.
Li Shang
Li Shang is a disciplined and honorable Chinese army captain who becomes Mulan’s commanding officer and eventual love interest in Disney’s animated film "Mulan."
-
E.
Wei Lihuang
Wei Lihuang was a prominent Nationalist Chinese general best known for his leadership of Chinese forces against Japan during the Second Sino-Japanese War and World War II.
- 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: Fa Zhou Triple: [Mulan (1998 film), character, Fa Zhou]
Generated description
Fa Zhou is Mulan’s elderly, honorable father and a former soldier whose injury and devotion to family drive much of the film’s emotional conflict.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fa Zhou Target entity description: Fa Zhou is Mulan’s elderly, honorable father and a former soldier whose injury and devotion to family drive much of the film’s emotional conflict.
-
A.
Zhou
Zhou is a common Chinese surname borne by many notable figures in Chinese history and politics.
-
B.
Kun Huang
Kun Huang was a prominent Chinese physicist and crystallographer known for his influential work in solid-state physics and lattice dynamics.
-
C.
Wang Jian
Wang Jian was a prominent Qin dynasty general whose military campaigns were crucial in the unification of China under Qin rule.
-
D.
Li Shang
Li Shang is a disciplined and honorable Chinese army captain who becomes Mulan’s commanding officer and eventual love interest in Disney’s animated film "Mulan."
-
E.
Wei Lihuang
Wei Lihuang was a prominent Nationalist Chinese general best known for his leadership of Chinese forces against Japan during the Second Sino-Japanese War and World War II.
- 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_69ad858e4c708190aa31d486cfee8a6a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf3fc3c8819080ac95974581ca0e |
completed | March 8, 2026, 5:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28eb55734819093f470caacc3e29c |
completed | March 12, 2026, 10 a.m. |
| NEDg | Description generation | batch_69b28f9e12488190b93355b783300264 |
completed | March 12, 2026, 10:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2c092063481909982dea3f71c00c1 |
completed | March 12, 2026, 1:33 p.m. |
Created at: March 8, 2026, 3:09 p.m.