Triple
T7724116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Chinese |
E175085
|
entity |
| Predicate | reconstructedBy |
P972
|
FINISHED |
| Object |
Pan Wuyun
Pan Wuyun is a Chinese historical linguist and philologist known for his influential reconstructions of Old Chinese phonology.
|
E687426
|
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: Pan Wuyun | Statement: [Old Chinese, reconstructedBy, Pan Wuyun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pan Wuyun Context triple: [Old Chinese, reconstructedBy, Pan Wuyun]
-
A.
Pan Wenhua
Pan Wenhua was a Chinese military officer and general active during the Republican era, known for his service in regional armies and his role in early 20th-century Chinese military affairs.
-
B.
Zhu Zhanyong
Zhu Zhanyong was a Ming dynasty imperial prince, known primarily as a son of the Hongxi Emperor of China.
-
C.
Wu Jingyu
Wu Jingyu is a Chinese taekwondo athlete and multiple-time Olympic gold medalist renowned as one of the sport’s most successful competitors.
-
D.
Wang Yupu
Wang Yupu was a prominent Chinese petroleum engineer and business executive who served as chairman of Sinopec and later as head of the China Insurance Regulatory Commission.
-
E.
Zhu Yawen
Zhu Yawen is a Chinese actor known for his roles in film and television dramas, particularly in military and historical series.
- 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: Pan Wuyun Triple: [Old Chinese, reconstructedBy, Pan Wuyun]
Generated description
Pan Wuyun is a Chinese historical linguist and philologist known for his influential reconstructions of Old Chinese phonology.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pan Wuyun Target entity description: Pan Wuyun is a Chinese historical linguist and philologist known for his influential reconstructions of Old Chinese phonology.
-
A.
Pan Wenhua
Pan Wenhua was a Chinese military officer and general active during the Republican era, known for his service in regional armies and his role in early 20th-century Chinese military affairs.
-
B.
Zhu Zhanyong
Zhu Zhanyong was a Ming dynasty imperial prince, known primarily as a son of the Hongxi Emperor of China.
-
C.
Wu Jingyu
Wu Jingyu is a Chinese taekwondo athlete and multiple-time Olympic gold medalist renowned as one of the sport’s most successful competitors.
-
D.
Wang Yupu
Wang Yupu was a prominent Chinese petroleum engineer and business executive who served as chairman of Sinopec and later as head of the China Insurance Regulatory Commission.
-
E.
Zhu Yawen
Zhu Yawen is a Chinese actor known for his roles in film and television dramas, particularly in military and historical series.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7031279708190a3a5fb64f9206974 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7beffc48190b39048b6afc1d644 |
completed | March 29, 2026, 6:33 a.m. |
| NEDg | Description generation | batch_69c8c8698a388190a47d6636fe5d2bb4 |
completed | March 29, 2026, 6:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c8f3873481908ef6efb2e39272db |
completed | March 29, 2026, 6:38 a.m. |
Created at: March 27, 2026, 4:05 p.m.