Triple
T18205126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swin Transformer |
E435882
|
entity |
| Predicate | coAuthor |
P398
|
FINISHED |
| Object |
Yue Cao
Yue Cao is a computer vision researcher known for influential work on transformer-based architectures and large-scale visual recognition models.
|
E1326082
|
NE FINISHED |
How this triple was built (4 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: Yue Cao | Statement: [Swin Transformer, coAuthor, Yue Cao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yue Cao Context triple: [Swin Transformer, coAuthor, Yue Cao]
-
A.
Luyu Yang
Luyu Yang is an entrepreneur best known as a co-founder of the short-form video platform Musical.ly, which later merged into TikTok.
-
B.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
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.
Gao-Yang Yue
Gao-Yang Yue is a regional variety of Yue Chinese spoken primarily in parts of Guangdong province in southern China.
-
E.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yue Cao Triple: [Swin Transformer, coAuthor, Yue Cao]
Generated description
Yue Cao is a computer vision researcher known for influential work on transformer-based architectures and large-scale visual recognition models.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yue Cao Target entity description: Yue Cao is a computer vision researcher known for influential work on transformer-based architectures and large-scale visual recognition models.
-
A.
Luyu Yang
Luyu Yang is an entrepreneur best known as a co-founder of the short-form video platform Musical.ly, which later merged into TikTok.
-
B.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
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.
Gao-Yang Yue
Gao-Yang Yue is a regional variety of Yue Chinese spoken primarily in parts of Guangdong province in southern China.
-
E.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
- F. None of above. chosen
Provenance (5 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a043f126c9881908d7fae71fe125164 |
completed | May 13, 2026, 9:06 a.m. |
| NEDg | Description generation | batch_6a04403450f88190b4461502afdde13f |
completed | May 13, 2026, 9:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a044095d4b48190aeb30ecb37609da3 |
completed | May 13, 2026, 9:12 a.m. |
Created at: April 10, 2026, 10:32 a.m.