Triple
T11673127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yunmen Wenyan |
E277427
|
entity |
| Predicate | student |
P7251
|
FINISHED |
| Object |
Deshan Bensheng
Deshan Bensheng was a prominent 9th-century Chinese Chan (Zen) master of the Tang dynasty, known for his strict teaching style and influential role in the development of the Linji school.
|
E940275
|
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: Deshan Bensheng | Statement: [Yunmen Wenyan, student, Deshan Bensheng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deshan Bensheng Context triple: [Yunmen Wenyan, student, Deshan Bensheng]
-
A.
Shuheng
Shuheng is the given name of He Shuheng, an early Chinese Communist revolutionary and political figure.
-
B.
Shengzhi
Shengzhi is the given name of Tang Shengzhi, a prominent Chinese Nationalist general active during the early 20th century.
-
C.
Zheyuan
Zheyuan is a given name most notably borne by the Chinese general and politician Song Zheyuan.
-
D.
Weihan
Weihan is a Chinese given name most notably borne by Li Weihan, a prominent Chinese Communist revolutionary and politician.
-
E.
Zhongxiao Xinsheng
Zhongxiao Xinsheng is a key Taipei Metro station in central Taipei that serves as an important transfer point between multiple subway lines.
- 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: Deshan Bensheng Triple: [Yunmen Wenyan, student, Deshan Bensheng]
Generated description
Deshan Bensheng was a prominent 9th-century Chinese Chan (Zen) master of the Tang dynasty, known for his strict teaching style and influential role in the development of the Linji school.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Deshan Bensheng Target entity description: Deshan Bensheng was a prominent 9th-century Chinese Chan (Zen) master of the Tang dynasty, known for his strict teaching style and influential role in the development of the Linji school.
-
A.
Shuheng
Shuheng is the given name of He Shuheng, an early Chinese Communist revolutionary and political figure.
-
B.
Shengzhi
Shengzhi is the given name of Tang Shengzhi, a prominent Chinese Nationalist general active during the early 20th century.
-
C.
Zheyuan
Zheyuan is a given name most notably borne by the Chinese general and politician Song Zheyuan.
-
D.
Weihan
Weihan is a Chinese given name most notably borne by Li Weihan, a prominent Chinese Communist revolutionary and politician.
-
E.
Zhongxiao Xinsheng
Zhongxiao Xinsheng is a key Taipei Metro station in central Taipei that serves as an important transfer point between multiple subway lines.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a443b6848190a1eb6825fbc49d08 |
completed | April 10, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef13e1b3d8819085ea806280ed69d3 |
completed | April 27, 2026, 7:44 a.m. |
| NEDg | Description generation | batch_69ef3551b9a88190a9b30bcb2592628b |
completed | April 27, 2026, 10:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ef51c17078819083f05036f290ce09 |
completed | April 27, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:40 p.m.