Triple
T11673126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yunmen Wenyan |
E277427
|
entity |
| Predicate | student |
P7251
|
FINISHED |
| Object | Dongshan Shouchu |
E277426
|
NE FINISHED |
How this triple was built (2 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: Dongshan Shouchu | Statement: [Yunmen Wenyan, student, Dongshan Shouchu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongshan Shouchu Context triple: [Yunmen Wenyan, student, Dongshan Shouchu]
-
A.
Dongshan Liangjie
chosen
Dongshan Liangjie was a 9th-century Chinese Chan master regarded as the founder of the Caodong (Sōtō) school of Zen Buddhism.
-
B.
Dongshen bu
Dongshen bu is a section of the Daoist Canon (Daozang), comprising texts focused on a particular subset of Daoist teachings and practices.
-
C.
Heze Shenhui
Heze Shenhui was an influential early Chinese Chan (Zen) Buddhist monk known for vigorously promoting the Southern School associated with Huineng and shaping the later development of Chan doctrine.
-
D.
Meidi Dadao
Meidi Dadao is a metro station in Guangzhou, China, serving as a terminus on Guangzhou Metro Line 7.
-
E.
Shui Xian
Shui Xian is a famous Chinese oolong tea, known for its rich, floral, and roasted notes, traditionally produced in the Wuyi Mountains of Fujian.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69f0192f790c8190a99d512b6f5c15aa |
completed | April 28, 2026, 2:19 a.m. |
Created at: April 8, 2026, 9:40 p.m.