Triple
T8481920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Demis Hassabis |
E200539
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Demis Hassabis |
E39539
|
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: Demis Hassabis | Statement: [Demis Hassabis, name, Demis Hassabis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Demis Hassabis Context triple: [Demis Hassabis, name, Demis Hassabis]
-
A.
Demis Hassabis
chosen
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.
-
B.
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.
-
C.
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.
-
D.
Nicolas Heess
Nicolas Heess is a machine learning researcher known for his work in deep reinforcement learning, including contributions to algorithms such as Deep Deterministic Policy Gradient (DDPG).
-
E.
David Silver
David Silver is a central character on the teen drama series "Beverly Hills, 90210," known for his evolution from an awkward outsider to a popular DJ and radio host.
- 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53638c48190b742fc51d1b4442a |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a2b2e9081909f19712946c6ec20 |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:12 p.m.