Triple
T11003105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sequence to Sequence Learning with Neural Networks |
E260048
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | neural networks paper |
C6844
|
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: neural networks paper Context triple: [Sequence to Sequence Learning with Neural Networks, instanceOf, neural networks paper]
-
A.
landmark paper in machine learning
chosen
A landmark paper in machine learning is a highly influential publication that introduces foundational theories, algorithms, or empirical results that significantly shape subsequent research and practice in the field.
-
B.
recurrent artificial neural network
A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
-
C.
machine learning book
A machine learning book is a structured, written resource that explains the theories, algorithms, and practical applications of machine learning to help readers understand and apply data-driven modeling techniques.
-
D.
landmark paper in nonlinear science
A landmark paper in nonlinear science is a seminal research work that fundamentally advances understanding of complex, nonlinear phenomena and significantly shapes subsequent theory, methods, or applications in the field.
-
E.
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.
- F. None of above.
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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:25 p.m.