Triple
T2993543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon Osindero |
E81011
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Google DeepMind |
E6331
|
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: Google DeepMind | Statement: [Simon Osindero, employer, Google DeepMind]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google DeepMind Context triple: [Simon Osindero, employer, Google DeepMind]
-
A.
DeepMind
chosen
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
-
B.
Google Brain
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
-
C.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
-
D.
Google Tensor
Google Tensor is Google's custom-designed system-on-a-chip (SoC) platform created to power Pixel devices with advanced AI and machine learning capabilities.
-
E.
Google Research
Google Research is the research division of Google focused on advancing the state of the art in computer science and artificial intelligence through fundamental and applied research.
- 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_69ad8b187fc8819085914d3c9ea3142d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99e291e0819089f81c0a7d7a6cd9 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b109061684819086777d3b871c94f8 |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:59 p.m.