Triple
T17748713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terence Yin |
E443054
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Terence Yin |
—
|
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: Terence Yin | Statement: [Terence Yin, name, Terence Yin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terence Yin Context triple: [Terence Yin, name, Terence Yin]
-
A.
Terence Yin
chosen
Terence Yin is a Hong Kong-based actor and singer known for his supporting roles in action and crime films across Hong Kong and mainland Chinese cinema.
-
B.
Terence Chang
Terence Chang is a Hong Kong-born film producer best known for his collaborations with director John Woo on action films in both Asian and Hollywood cinema.
-
C.
Roger Yuan
Roger Yuan is an American martial artist, fight choreographer, and actor known for his roles and stunt work in numerous action films.
-
D.
Terrence Cai
Terrence Cai is a researcher known for coauthoring academic work with prominent computer scientist Christian Szegedy, likely in the field of machine learning or theoretical computer science.
-
E.
Stephen Wang
Stephen Wang is an entrepreneur best known as a co-founder of the film and television review aggregation website Rotten Tomatoes.
- 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_69e47ad46a50819089c87f74efe3c7ca |
completed | April 19, 2026, 6:48 a.m. |
Created at: April 10, 2026, 10:10 a.m.