Triple
T9452076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crazy Rich Asians |
E227916
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object | Awkwafina |
E390743
|
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: Awkwafina | Statement: [Crazy Rich Asians, starredActor, Awkwafina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Awkwafina Context triple: [Crazy Rich Asians, starredActor, Awkwafina]
-
A.
Awkwafina
chosen
Awkwafina is an American actress, comedian, and rapper known for her breakout roles in films like "Crazy Rich Asians" and "The Farewell," as well as her distinctive comedic persona.
-
B.
Zazie Beetz
Zazie Beetz is a German-American actress known for her roles in the TV series "Atlanta" and films such as "Deadpool 2" and "Joker."
-
C.
Beanie Feldstein
Beanie Feldstein is an American actress known for her comedic and dramatic roles in films such as "Booksmart" and "Lady Bird," as well as on Broadway.
-
D.
Constance Wu
Constance Wu is an American actress best known for her starring roles in the TV series "Fresh Off the Boat" and the film "Crazy Rich Asians."
-
E.
Natasha Lyonne
Natasha Lyonne is an American actress, writer, and director known for her distinctive raspy voice and roles in projects like Russian Doll, the American Pie films, and various acclaimed independent movies.
- 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_69ca8439f8bc8190997f2ef40c9f0bc2 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f6796d8819088cbecc0588ae5fc |
completed | April 1, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cdd08c08190b9a140e2634c4cf9 |
completed | April 4, 2026, 3:23 p.m. |
Created at: March 30, 2026, 7:52 p.m.