Triple
T15361079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivier Bousquet |
E367288
|
entity |
| Predicate | affiliation |
P10
|
FINISHED |
| Object | Google AI |
E46728
|
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 AI | Statement: [Olivier Bousquet, affiliation, Google AI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google AI Context triple: [Olivier Bousquet, affiliation, Google AI]
-
A.
Google AI Studio
Google AI Studio is a web-based development environment from Google that lets developers build, test, and integrate applications using Gemini and other Google AI models.
-
B.
Google Brain
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
-
C.
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.
-
D.
Meta AI
Meta AI is Meta Platforms’ artificial intelligence division, responsible for developing large-scale AI models, research, and consumer-facing tools like the Meta AI assistant integrated across its apps and services.
-
E.
Google Research
chosen
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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4607408190ab281a7f7a8012d3 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4a181c8190bffc1ac1a86e215d |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.