Triple
T22023169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shang-Chi and the Legend of the Ten Rings |
E543891
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Fala Chen |
—
|
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: Fala Chen | Statement: [Shang-Chi and the Legend of the Ten Rings, starring, Fala Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fala Chen Context triple: [Shang-Chi and the Legend of the Ten Rings, starring, Fala Chen]
-
A.
Fala Chen
chosen
Fala Chen is a Chinese-American actress known for her work in both Asian television dramas and Hollywood films, including roles in major franchises.
-
B.
Danqi Chen
Danqi Chen is a prominent computer scientist and natural language processing researcher known for her work on neural reading comprehension and information retrieval.
-
C.
Renee Shin-Yi Chen
Renee Shin-Yi Chen was a child actress who was tragically killed during a helicopter accident on the set of *Twilight Zone: The Movie* in 1982.
-
D.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
E.
Kai Chen
Kai Chen is a researcher known for co-authoring influential work in natural language processing and word embeddings alongside Tomas Mikolov.
- 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_69e11e2e8ea4819084210fe06d3a1b8d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127c9959481908da6bed356199f75 |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:23 p.m.