Triple
T4469292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yunnan Military Academy |
E98453
|
entity |
| Predicate | notableAlumnus |
P304
|
FINISHED |
| Object | Li Weihan |
E75204
|
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: Li Weihan | Statement: [Yunnan Military Academy, notableAlumnus, Li Weihan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Li Weihan Context triple: [Yunnan Military Academy, notableAlumnus, Li Weihan]
-
A.
Li Weihan
chosen
Li Weihan was a prominent Chinese Communist revolutionary and politician who played key roles in party organization and United Front work in the early and mid-20th century.
-
B.
Li Jingxi
Li Jingxi was a Chinese politician and statesman who briefly served as premier during the early years of the Republic of China.
-
C.
Zhao Huanyu
Zhao Huanyu is an actor known for appearing in the Chinese film "Special ID."
-
D.
Wu Xiaohui
Wu Xiaohui is a Chinese football executive best known for leading Shanghai Shenhua F.C., one of the major clubs in the Chinese Super League.
-
E.
Li Jiayu
Li Jiayu was a Chinese military officer and general associated with the National Revolutionary Army during the Republican era.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3569cd03c8190927c596bedb45ac8 |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beb0bc858c8190b70709f5ed743ee6 |
completed | March 21, 2026, 2:52 p.m. |
Created at: March 12, 2026, 11:34 p.m.