Triple
T1922981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shane Legg |
E40165
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Shane Legg |
E40165
|
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: Shane Legg | Statement: [Shane Legg, name, Shane Legg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shane Legg Context triple: [Shane Legg, name, Shane Legg]
-
A.
Shane Legg
chosen
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.
-
B.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
C.
Sergey Levine
Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
-
D.
David Silver
David Silver is a leading artificial intelligence researcher best known for his work at DeepMind on reinforcement learning and the development of the AlphaGo system.
-
E.
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.
- 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb23459ac819088ded5bfac9d4aad |
completed | March 7, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3e458e8819098ea1c2d5598f890 |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.