Triple
T17505342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheng Wei |
E426299
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Cheng Wei |
—
|
NE NERFINISHED |
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: Cheng Wei | Statement: [Cheng Wei, name, Cheng Wei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cheng Wei Context triple: [Cheng Wei, name, Cheng Wei]
-
A.
Cheng Wei
chosen
Cheng Wei is a Chinese entrepreneur best known as the founder and CEO of ride-hailing giant Didi Chuxing.
-
B.
Cheng Pu
Cheng Pu was a prominent military general and veteran officer who served the warlord Sun Quan and played a key role in the early expansion of the Eastern Wu state during the late Eastern Han and Three Kingdoms period of China.
-
C.
Cui Hao
Cui Hao was a prominent poet of the Tang dynasty in China, best known for his evocative landscape and frontier poems.
-
D.
Chen Feng
Chen Feng is a Chinese entrepreneur best known as the co-founder and longtime chairman of the HNA Group conglomerate.
-
E.
Jiang Wu
Jiang Wu is a Chinese actor known for his roles in both mainstream and art-house films, often portraying intense and complex characters.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452159c28819084b2cba4313ddf28 |
completed | April 19, 2026, 3:55 a.m. |
Created at: April 10, 2026, 5:48 a.m.