Triple
T21843485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Wedding Banquet |
E539313
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | May Chin |
—
|
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: May Chin | Statement: [The Wedding Banquet, hasCastMember, May Chin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: May Chin Context triple: [The Wedding Banquet, hasCastMember, May Chin]
-
A.
May Chin
chosen
May Chin is a Taiwanese actress best known internationally for her role in Ang Lee’s acclaimed 1993 film "The Wedding Banquet."
-
B.
Lee-Chin
Lee-Chin is the surname of Jamaican-Canadian billionaire businessman and philanthropist Michael Lee-Chin.
-
C.
May Pang
May Pang is an American music industry professional best known for her relationship with John Lennon during his mid-1970s “Lost Weekend” period and her work as his personal assistant.
-
D.
Ching
Ching is the surname of Brian Ching, a retired American soccer player best known as a forward for the Houston Dynamo and the U.S. national team.
-
E.
Ching
Ching is the central protagonist of the Hong Kong romantic drama film "Love Battlefield," around whom the story’s emotional conflicts and relationship struggles revolve.
- 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_69e0c476c3c88190a92d08ebb59a128a |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0bd5248f08190ba208512eafbe7ad |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 16, 2026, 6:55 p.m.