Triple
T14845137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 俞敏洪 |
E349066
|
entity |
| Predicate | alternateName |
P39
|
FINISHED |
| Object | Michael Yu |
E349065
|
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: Michael Yu | Statement: [俞敏洪, alternateName, Michael Yu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Yu Context triple: [俞敏洪, alternateName, Michael Yu]
-
A.
Michael Yu
chosen
Michael Yu is a prominent Chinese entrepreneur and educator best known as the founder of New Oriental Education & Technology Group, one of China’s largest private education providers.
-
B.
Daniel Zhang
Daniel Zhang is a Chinese business executive best known for leading Alibaba Group through a major period of global expansion and for creating the Singles’ Day shopping festival.
-
C.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
D.
Michael Wong
Michael Wong is a Hong Kong-based actor and singer known for his roles in action and crime films across Asian cinema.
-
E.
William Li
William Li is a Chinese entrepreneur best known as the founder and CEO of the electric vehicle company NIO.
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded291103c8190a64cfe700bfee197 |
completed | April 14, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe65010190819083ec051eb5d82839 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:53 a.m.