Triple
T21211088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soo-Yung Han |
E522719
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Julia Hsu |
—
|
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: Julia Hsu | Statement: [Soo-Yung Han, portrayedBy, Julia Hsu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julia Hsu Context triple: [Soo-Yung Han, portrayedBy, Julia Hsu]
-
A.
Julia Hsu
chosen
Julia Hsu is an actress best known for her role as Soo-Yung, the kidnapped daughter of a Chinese consul, in the action-comedy film "Rush Hour."
-
B.
Stephanie Hsu
Stephanie Hsu is an American actress best known for her acclaimed, genre-bending performance as Joy/Jobu Tupaki in the film "Everything Everywhere All at Once."
-
C.
Laura Harrier
Laura Harrier is an American actress and model best known for her roles in films like "Spider-Man: Homecoming" and "BlacKkKlansman."
-
D.
Jessica Lu
Jessica Lu is an American actress best known for her television roles, including a main role on the sci-fi drama series "Reverie."
-
E.
Lisa Lu
Lisa Lu is a Chinese-American actress known for her distinguished film and television career spanning both Hollywood and Chinese cinema, including a prominent role in "Crazy Rich Asians."
- 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_69e0b5112d8881909510b2dcdc93106d |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7346eb20c8190aeb3c0cc0a24aaf9 |
completed | April 21, 2026, 8:25 a.m. |
Created at: April 16, 2026, 3:37 p.m.