Triple
T10023612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max Welling |
E200669
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Yee Whye Teh |
E80657
|
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: Yee Whye Teh | Statement: [Max Welling, coAuthorWith, Yee Whye Teh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yee Whye Teh Context triple: [Max Welling, coAuthorWith, Yee Whye Teh]
-
A.
Mark Chee
Mark Chee is a molecular biologist and entrepreneur best known as a co-founder of Illumina, a leading company in DNA sequencing and genomics technologies.
-
B.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
C.
Yee-Whye Teh
chosen
Yee-Whye Teh is a prominent statistician and machine learning researcher known for his influential work on Bayesian nonparametrics, probabilistic modeling, and deep learning.
-
D.
Tony Tan Keng Yam
Tony Tan Keng Yam is a Singaporean politician and former President of Singapore who previously served as Deputy Prime Minister and held several key ministerial portfolios.
-
E.
Ken Seng
Ken Seng is a cinematographer known for his visually distinctive work on films such as "Obsessed."
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd7c75548190aa604d90d63dc111 |
completed | April 2, 2026, 1:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26abb0ab08190b5bcf101c5680f3c |
completed | April 5, 2026, 1:59 p.m. |
Created at: March 30, 2026, 8:53 p.m.