Triple
T21588282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khamzat Chimaev |
E532709
|
entity |
| Predicate | notableVictoryOver |
P34090
|
FINISHED |
| Object | Li Jingliang |
—
|
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: Li Jingliang | Statement: [Khamzat Chimaev, notableVictoryOver, Li Jingliang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Li Jingliang Context triple: [Khamzat Chimaev, notableVictoryOver, Li Jingliang]
-
A.
Li Jingliang
chosen
Li Jingliang is a Chinese mixed martial artist and UFC welterweight known for his aggressive striking style and knockout power.
-
B.
Liang Jingdong
Liang Jingdong is a Chinese actor known for his supporting roles in contemporary Chinese film and television dramas.
-
C.
Li Jinglong
Li Jinglong was a Ming dynasty general best known for his unsuccessful leadership of imperial forces against Zhu Di during the Jingnan campaign.
-
D.
Li Jiajun
Li Jiajun is a Chinese short track speed skater who was one of the sport’s leading competitors in the late 1990s and early 2000s, winning multiple Olympic and World Championship medals.
-
E.
Cui Mingliang
Cui Mingliang is an individual user or personality who is followed as a character on the Platform service.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb621ab88190a33a943424ffb306 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 16, 2026, 6:31 p.m.