Triple
T6082264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeffrey D. Ullman |
E135550
|
entity |
| Predicate | coauthor |
P2389
|
FINISHED |
| Object | Jiawei Han |
E356897
|
NE FINISHED |
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: Jiawei Han | Statement: [Jeffrey D. Ullman, coauthor, Jiawei Han]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jiawei Han Context triple: [Jeffrey D. Ullman, coauthor, Jiawei Han]
-
A.
Jiawei Han
chosen
Jiawei Han is a prominent computer scientist renowned for his pioneering contributions to data mining and knowledge discovery.
-
B.
Kaiming He
Kaiming He is a prominent Chinese computer scientist known for pioneering deep learning architectures and techniques, including the influential ResNet model for image recognition.
-
C.
Hongbo Zhang
Hongbo Zhang is a software engineer best known for creating BuckleScript, a compiler that translates OCaml/ReasonML code to efficient JavaScript.
-
D.
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
E.
Xiangyu Zhang
Xiangyu Zhang is a computer vision and deep learning researcher known for his contributions to convolutional neural network architectures and large-scale visual recognition.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05774bc948190a446b27e83f7079b |
completed | March 22, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d57b5f481908d7df374837a486a |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:11 p.m.