Triple
T19376696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | He Kexin |
E484686
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | He Kexin |
E484686
|
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: He Kexin | Statement: [He Kexin, name, He Kexin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: He Kexin Context triple: [He Kexin, name, He Kexin]
-
A.
He Kexin
chosen
He Kexin is a Chinese artistic gymnast best known for winning multiple Olympic gold medals on the uneven bars.
-
B.
Fan Kexin
Fan Kexin is a Chinese short track speed skater known for winning multiple world championship titles and Olympic medals in relay and individual events.
-
C.
Xue Yue
Xue Yue was a prominent Nationalist Chinese general renowned for his leadership in key battles against Japanese forces during the Second Sino-Japanese War.
-
D.
Xiao Ke
Xiao Ke was a prominent Chinese military leader and general of the People’s Liberation Army who played key roles in the Chinese Civil War and early PRC military development.
-
E.
Li Jingxi
Li Jingxi was a Chinese politician and statesman who briefly served as premier during the early years of the Republic of China.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a5cfbf48190ac60e3ffa6baa263 |
completed | April 20, 2026, 12:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07241dfc208190896705ec6890b6f7 |
completed | May 15, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:35 p.m.