Triple
T4459778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Police Story 2013 |
E98222
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Zhong Wen
Zhong Wen is the tough, determined police officer portrayed by Jackie Chan in the action film "Police Story 2013."
|
E443059
|
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: Zhong Wen | Statement: [Police Story 2013, character, Zhong Wen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhong Wen Context triple: [Police Story 2013, character, Zhong Wen]
-
A.
Zhou
Zhou is a common Chinese surname borne by many notable figures in Chinese history and politics.
-
B.
Zeng
Zeng is a Chinese surname and given name commonly rendered in pinyin and borne by numerous historical and contemporary figures in China.
-
C.
Yuan Tseh
Yuan Tseh is a Taiwanese chemist and Nobel laureate renowned for his pioneering work in chemical reaction dynamics.
-
D.
Zhu
Zhu is a common Chinese surname borne by many notable historical and contemporary figures in China.
-
E.
Wu Yi
Wu Yi is a Chinese politician who served as Vice Premier of the State Council and was widely known for her leadership in economic policy and public health crises such as the SARS outbreak.
- 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: Zhong Wen Triple: [Police Story 2013, character, Zhong Wen]
Generated description
Zhong Wen is the tough, determined police officer portrayed by Jackie Chan in the action film "Police Story 2013."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zhong Wen Target entity description: Zhong Wen is the tough, determined police officer portrayed by Jackie Chan in the action film "Police Story 2013."
-
A.
Zhou
Zhou is a common Chinese surname borne by many notable figures in Chinese history and politics.
-
B.
Zeng
Zeng is a Chinese surname and given name commonly rendered in pinyin and borne by numerous historical and contemporary figures in China.
-
C.
Yuan Tseh
Yuan Tseh is a Taiwanese chemist and Nobel laureate renowned for his pioneering work in chemical reaction dynamics.
-
D.
Zhu
Zhu is a common Chinese surname borne by many notable historical and contemporary figures in China.
-
E.
Wu Yi
Wu Yi is a Chinese politician who served as Vice Premier of the State Council and was widely known for her leadership in economic policy and public health crises such as the SARS outbreak.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567184f481908a2787e4ac9bb345 |
completed | March 13, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6284431c48190aa1553ff89f2239f |
completed | March 15, 2026, 3:32 a.m. |
| NEDg | Description generation | batch_69b629532cac8190b959adc0ef13305a |
completed | March 15, 2026, 3:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b62d9c287c8190a305f9d21517f913 |
completed | March 15, 2026, 3:55 a.m. |
Created at: March 12, 2026, 11:33 p.m.