Triple
T17749429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Flying Daggers |
E443073
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Song Dandan |
—
|
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: Song Dandan | Statement: [House of Flying Daggers, castMember, Song Dandan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Song Dandan Context triple: [House of Flying Daggers, castMember, Song Dandan]
-
A.
Song Dandan
chosen
Song Dandan is a renowned Chinese actress and comedian best known for her work in television sketch comedy and sitcoms.
-
B.
Deng Yiji
Deng Yiji is the maternal grandmother of Chloe Murdoch.
-
C.
Huang Ying
Huang Ying is a screenwriter best known for contributing to the classic Hong Kong horror-comedy film "Encounters of the Spooky Kind."
-
D.
Ge You
Ge You is a renowned Chinese actor, widely acclaimed for his nuanced performances in film and television and recognized as one of China's most influential screen performers.
-
E.
Song Zuying
Song Zuying is a renowned Chinese soprano and folk singer celebrated for her powerful voice and frequent performances at major national events.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48418c0188190beb31809b40e4648 |
completed | April 19, 2026, 7:28 a.m. |
Created at: April 10, 2026, 10:10 a.m.