Triple
T3995366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Steinhardt |
E87085
|
entity |
| Predicate | coAuthor |
P398
|
FINISHED |
| Object |
Zhong-Ying Wang
Zhong-Ying Wang is a physicist known for collaborative work in theoretical and cosmological physics, including research conducted with Paul Steinhardt.
|
E403653
|
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: Zhong-Ying Wang | Statement: [Paul Steinhardt, coAuthor, Zhong-Ying Wang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhong-Ying Wang Context triple: [Paul Steinhardt, coAuthor, Zhong-Ying Wang]
-
A.
Yanluo Wang
Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
-
B.
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
C.
Jun-Yan Zhu
Jun-Yan Zhu is a computer scientist and researcher known for his influential work in computer vision and generative models, particularly in image-to-image translation.
-
D.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
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: Zhong-Ying Wang Triple: [Paul Steinhardt, coAuthor, Zhong-Ying Wang]
Generated description
Zhong-Ying Wang is a physicist known for collaborative work in theoretical and cosmological physics, including research conducted with Paul Steinhardt.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zhong-Ying Wang Target entity description: Zhong-Ying Wang is a physicist known for collaborative work in theoretical and cosmological physics, including research conducted with Paul Steinhardt.
-
A.
Yanluo Wang
Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
-
B.
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
C.
Jun-Yan Zhu
Jun-Yan Zhu is a computer scientist and researcher known for his influential work in computer vision and generative models, particularly in image-to-image translation.
-
D.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa1f0fb88190aafbfdc98bc8652d |
completed | March 9, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5403c703081908070625ebfb6fb5f |
completed | March 14, 2026, 11:02 a.m. |
| NEDg | Description generation | batch_69b540ec36a4819082a9cbefc99bd683 |
completed | March 14, 2026, 11:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5416d182c81908b1ae43ed097d288 |
completed | March 14, 2026, 11:07 a.m. |
Created at: March 9, 2026, 3:34 p.m.