Triple
T17025840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedicab Driver |
E413060
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Wu Ma |
—
|
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: Wu Ma | Statement: [Pedicab Driver, starring, Wu Ma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wu Ma Context triple: [Pedicab Driver, starring, Wu Ma]
-
A.
Wu Ma
chosen
Wu Ma was a prolific Hong Kong actor and film director, best known for his character roles in martial arts and supernatural comedies throughout the 1970s and 1980s.
-
B.
Wu Yi
Wu Yi is a Chinese politician who served as Vice Premier of the State Council and was widely known for her leadership in economic policy and public health crises such as the SARS outbreak.
-
C.
Wu Yuan
Wu Yuan, better known as Wu Zixu, was a famed statesman and military strategist of the Spring and Autumn period in ancient China, renowned for his role in the rise of the State of Wu.
-
D.
Meng Wu
Meng Wu was a prominent Qin general who played a key role in the military campaigns that unified China under the Qin dynasty.
-
E.
Wu Mi
Wu Mi was a prominent Chinese scholar and educator known for his contributions to comparative literature and his influential role in modern Chinese higher education.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
Created at: April 10, 2026, 5:33 a.m.