Triple
T3075741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drew Bagnell |
E64130
|
entity |
| Predicate | hasNotableStudent |
P4838
|
FINISHED |
| Object | Sergey Levine |
E164770
|
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: Sergey Levine | Statement: [Drew Bagnell, hasNotableStudent, Sergey Levine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sergey Levine Context triple: [Drew Bagnell, hasNotableStudent, Sergey Levine]
-
A.
Sergey Levine
chosen
Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
-
B.
Pieter Abbeel
Pieter Abbeel is a Belgian-American computer scientist and professor at UC Berkeley known for his influential work in robotics and deep reinforcement learning.
-
C.
Shane Legg
Shane Legg is a computer scientist and AI researcher best known as a co-founder of DeepMind and for his influential work on artificial general intelligence.
-
D.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
E.
Volodymyr Mnih
Volodymyr Mnih is a computer scientist and deep learning researcher known for pioneering deep reinforcement learning methods that achieved human-level performance on Atari games.
- 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_69ad857a8aec8190bfdfd9c14554ac5a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada150d8e08190bde5f68e800e8feb |
completed | March 8, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f889e7fc8190858845221998f321 |
completed | March 11, 2026, 11:19 p.m. |
Created at: March 8, 2026, 3:02 p.m.