Triple
T15103469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raghu Ramakrishnan |
E360726
|
entity |
| Predicate | coAuthor |
P398
|
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: [Raghu Ramakrishnan, coAuthor, Jiawei Han]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jiawei Han Context triple: [Raghu Ramakrishnan, 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.
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.
-
D.
Yifeng Liu
Yifeng Liu is a mathematician recognized for his significant contributions to number theory and arithmetic geometry, particularly in the study of automorphic forms and L-functions.
-
E.
Tinghui Zhou
Tinghui Zhou is a computer vision and machine learning researcher known for influential work on unsupervised learning and image-to-image translation.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00551521c8190b48d1a074bb4bdfc |
completed | April 15, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feae274f6881908931569efc09996e |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 10, 2026, 3:05 a.m.