Triple
T18953133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai Theatre Academy |
E463704
|
entity |
| Predicate | hasNotableAlumni |
P51
|
FINISHED |
| Object | Yao 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: Yao Chen | Statement: [Shanghai Theatre Academy, hasNotableAlumni, Yao Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yao Chen Context triple: [Shanghai Theatre Academy, hasNotableAlumni, Yao Chen]
-
A.
Yao Chen
chosen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
B.
Kai Chen
Kai Chen is a researcher known for co-authoring influential work in natural language processing and word embeddings alongside Tomas Mikolov.
-
C.
Xue Chen
Xue Chen is a prominent Chinese beach volleyball player who has represented China in multiple international competitions, including the Olympic Games.
-
D.
Sun Chen
Sun Chen was a powerful and ultimately tyrannical regent of Eastern Wu during the Three Kingdoms period of China, whose rule ended in his execution after a failed attempt to consolidate power.
-
E.
Tianqi Chen
Tianqi Chen is a computer scientist and machine learning researcher best known for creating the widely used gradient boosting library XGBoost.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d544fef8819091147189ddd89617 |
completed | April 20, 2026, 7:27 a.m. |
Created at: April 10, 2026, noon