Triple
T36490397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tensor2Tensor for Neural Machine Translation |
E899036
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | machine learning research |
C62875
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: machine learning research Context triple: [Tensor2Tensor for Neural Machine Translation, instanceOf, machine learning research]
-
A.
machine learning research institute
A machine learning research institute is an organization dedicated to advancing the theory, algorithms, and applications of machine learning through systematic research, experimentation, and collaboration.
-
B.
machine learning researcher
A machine learning researcher is a specialist who develops, analyzes, and improves algorithms and models that enable computers to learn from data and make predictions or decisions.
-
C.
machine learning paradigm
A machine learning paradigm is a conceptual framework that defines how models learn from data, including the assumptions, learning objectives, and training procedures that guide the development and application of algorithms.
-
D.
artificial intelligence
Artificial intelligence is a field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
-
E.
machine learning division
The machine learning division is an organizational unit responsible for researching, developing, and deploying data-driven algorithms and models to solve complex problems and enhance products or services.
- F. None of above. chosen
Provenance (1 batch)
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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.