Triple
T18731289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Farewell (2019 film) |
E458040
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | Lulu Wang |
—
|
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: Lulu Wang | Statement: [The Farewell (2019 film), director, Lulu Wang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lulu Wang Context triple: [The Farewell (2019 film), director, Lulu Wang]
-
A.
Lulu Wang
chosen
Lulu Wang is a Chinese-American filmmaker best known for writing and directing the critically acclaimed film "The Farewell."
-
B.
Alice Wu
Alice Wu is a Chinese American filmmaker best known for her groundbreaking queer-themed films such as "Saving Face" and "The Half of It."
-
C.
Sophia Takal
Sophia Takal is an American filmmaker and actress known for her work in independent cinema, including directing the 2019 horror remake "Black Christmas."
-
D.
Chloé Zhao
Chloé Zhao is an acclaimed Chinese-born filmmaker known for her naturalistic, character-driven dramas and her Academy Award–winning work on the film "Nomadland."
-
E.
Chloe Domont
Chloe Domont is an American filmmaker and screenwriter best known for writing and directing the 2023 psychological thriller film "Fair Play."
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d778cf8819083500600b9ac0744 |
completed | April 20, 2026, 12:04 a.m. |
Created at: April 10, 2026, 11:51 a.m.