Triple
T11003404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Łukasz Kaiser |
E260054
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Google Brain |
E7923
|
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 Brain | Statement: [Łukasz Kaiser, employer, Google Brain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google Brain Context triple: [Łukasz Kaiser, employer, Google Brain]
-
A.
Google Brain
chosen
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
-
B.
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.
-
C.
DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
-
D.
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.
-
E.
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.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797546f448190946ee6442d657dc5 |
completed | April 9, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3453d181081908cb58a957f4d1295 |
completed | April 18, 2026, 8:47 a.m. |
Created at: April 8, 2026, 9:25 p.m.