Triple
T22608757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Data Mining: Concepts and Techniques |
E566635
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Jiawei Han |
—
|
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: Jiawei Han | Statement: [Data Mining: Concepts and Techniques, author, Jiawei Han]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jiawei Han Context triple: [Data Mining: Concepts and Techniques, author, 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.
Jia Deng
Jia Deng is a computer scientist known for his influential work in computer vision and machine learning, particularly as a co-creator of the large-scale image dataset ImageNet.
-
C.
Yinhan Liu
Yinhan Liu is a natural language processing researcher best known for co-developing the RoBERTa language model and related advances in large-scale pretraining.
-
D.
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.
-
E.
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.
- 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f167e86794819097e9c1ea83db52e6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 2:55 p.m.