Triple
T19626208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannah Quinlivan |
E471141
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Nezha
Nezha is a 2019 Chinese animated fantasy film that became a major box-office hit for its visually striking retelling of the classic mythological figure Nezha.
|
E1386597
|
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: Nezha | Statement: [Hannah Quinlivan, notableWork, Nezha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nezha Context triple: [Hannah Quinlivan, notableWork, Nezha]
-
A.
Zu Yi
Zu Yi was a Shang dynasty king of ancient China, remembered as an early ruler in the royal lineage preceding Wu Ding.
-
B.
Zexu
Zexu is the given name of Lin Zexu, the prominent Qing dynasty official known for his role in suppressing the opium trade in China.
-
C.
Xiaozong
Xiaozong is the temple name of the Hongzhi Emperor, a Ming dynasty ruler noted for his relatively peaceful and reform-minded reign in late 15th-century China.
-
D.
San Bao
San Bao is a Chinese composer best known for his film scores and music for director Zhang Yimou’s movies.
-
E.
Chenghuangshen
Chenghuangshen is a traditional Chinese city god deity believed to protect and oversee the affairs, justice, and welfare of a specific city and its inhabitants.
- 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: Nezha Triple: [Hannah Quinlivan, notableWork, Nezha]
Generated description
Nezha is a 2019 Chinese animated fantasy film that became a major box-office hit for its visually striking retelling of the classic mythological figure Nezha.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nezha Target entity description: Nezha is a 2019 Chinese animated fantasy film that became a major box-office hit for its visually striking retelling of the classic mythological figure Nezha.
-
A.
Zu Yi
Zu Yi was a Shang dynasty king of ancient China, remembered as an early ruler in the royal lineage preceding Wu Ding.
-
B.
Zexu
Zexu is the given name of Lin Zexu, the prominent Qing dynasty official known for his role in suppressing the opium trade in China.
-
C.
Xiaozong
Xiaozong is the temple name of the Hongzhi Emperor, a Ming dynasty ruler noted for his relatively peaceful and reform-minded reign in late 15th-century China.
-
D.
San Bao
San Bao is a Chinese composer best known for his film scores and music for director Zhang Yimou’s movies.
-
E.
Chenghuangshen
Chenghuangshen is a traditional Chinese city god deity believed to protect and oversee the affairs, justice, and welfare of a specific city and its inhabitants.
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640e9ff208190afb33c910ed2147b |
completed | April 20, 2026, 3:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0771bf2698819097690821e66b0d6a |
completed | May 15, 2026, 7:19 p.m. |
| NEDg | Description generation | batch_6a07736d8990819081b3ba34f1d3600e |
completed | May 15, 2026, 7:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0773f9f8148190822ed74d83fdac97 |
completed | May 15, 2026, 7:28 p.m. |
Created at: April 10, 2026, 1:44 p.m.