Triple
T29328353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Row LSTM |
E743714
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | component of PixelRNN |
C46884
|
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: component of PixelRNN Context triple: [Row LSTM, instanceOf, component of PixelRNN]
-
A.
neural network component
chosen
A neural network component is a modular unit—such as a layer, activation function, or connection pattern—that processes and transforms input data as part of a larger neural architecture to enable learning and inference.
-
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.
image generation model
An image generation model is an AI system that creates new images from input data such as text prompts, reference images, or learned patterns, using techniques like deep neural networks and generative modeling.
-
D.
plate margin network
A plate margin network is the interconnected system of tectonic plate boundaries and their associated geological structures and processes that collectively govern the distribution and interaction of Earth’s lithospheric plates.
-
E.
Fairlight CMI model
A Fairlight CMI model is a conceptual representation of the pioneering digital sampling synthesizer system, encapsulating its hardware components, sound sampling and synthesis capabilities, user interface, and role in music production workflows.
- 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_69f09125f784819080f4e9fce9fe624f |
completed | April 28, 2026, 10:51 a.m. |
Created at: April 28, 2026, 1:28 p.m.