Triple
T8577033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ke Jie |
E203072
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ke Jie |
E203072
|
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: Ke Jie | Statement: [Ke Jie, name, Ke Jie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ke Jie Context triple: [Ke Jie, name, Ke Jie]
-
A.
Ke Jie
chosen
Ke Jie is a Chinese professional Go player widely regarded as one of the strongest players of his generation.
-
B.
Lee Sedol
Lee Sedol is a South Korean professional Go player renowned as one of the strongest players in history and for his landmark 2016 match against DeepMind's AlphaGo.
-
C.
Ma Long
Ma Long is a Chinese table tennis player widely regarded as one of the greatest in the sport’s history, with multiple Olympic and World Championship titles.
-
D.
Wu Lei
Wu Lei is a prominent Chinese actor and former child star known for his roles in popular television dramas and films.
-
E.
Sun Yue
Sun Yue is a Chinese professional basketball player and former NBA champion known for his versatility and playmaking, notably starring for the Beijing Ducks in the Chinese Basketball Association.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea97787481909ebbaa45f59cbdaa |
completed | March 31, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce899dd7d48190b44338b92ad68bd0 |
completed | April 2, 2026, 3:22 p.m. |
Created at: March 30, 2026, 6:22 p.m.