Triple
T13653210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phillip Isola |
E326790
|
entity |
| Predicate | coAuthor |
P398
|
FINISHED |
| Object |
Tinghui Zhou
Tinghui Zhou is a computer vision and machine learning researcher known for influential work on unsupervised learning and image-to-image translation.
|
E1056175
|
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: Tinghui Zhou | Statement: [Phillip Isola, coAuthor, Tinghui Zhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tinghui Zhou Context triple: [Phillip Isola, coAuthor, Tinghui Zhou]
-
A.
Tingye Li
Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
-
B.
Hong-Kun Zhang
Hong-Kun Zhang is a mathematician known for her work in dynamical systems and ergodic theory, and for being a doctoral student of Lai-Sang Young.
-
C.
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
D.
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.
-
E.
Hongbo Zhang
Hongbo Zhang is a software engineer best known for creating BuckleScript, a compiler that translates OCaml/ReasonML code to efficient JavaScript.
- 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: Tinghui Zhou Triple: [Phillip Isola, coAuthor, Tinghui Zhou]
Generated description
Tinghui Zhou is a computer vision and machine learning researcher known for influential work on unsupervised learning and image-to-image translation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tinghui Zhou Target entity description: Tinghui Zhou is a computer vision and machine learning researcher known for influential work on unsupervised learning and image-to-image translation.
-
A.
Tingye Li
Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
-
B.
Hong-Kun Zhang
Hong-Kun Zhang is a mathematician known for her work in dynamical systems and ergodic theory, and for being a doctoral student of Lai-Sang Young.
-
C.
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
D.
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.
-
E.
Hongbo Zhang
Hongbo Zhang is a software engineer best known for creating BuckleScript, a compiler that translates OCaml/ReasonML code to efficient JavaScript.
- 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_69d8076d8270819092afc2f0e9c359a8 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60ace048190a4b92310ba272bd1 |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7943610488190838719ad31207c52 |
completed | May 3, 2026, 6:30 p.m. |
| NEDg | Description generation | batch_69f7955fce288190a7e426f467517a91 |
completed | May 3, 2026, 6:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7996cddf08190973e493fb788ce7a |
completed | May 3, 2026, 6:52 p.m. |
Created at: April 9, 2026, 9:52 p.m.