Triple
T16891473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Relief Map of Guatemala |
E424182
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | large-scale model |
C37717
|
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: large-scale model Context triple: [Relief Map of Guatemala, instanceOf, large-scale model]
-
A.
large language model family
A large language model family is a group of related neural network models that share a common architecture and training paradigm but vary in size, capabilities, and specialization to handle diverse natural language understanding and generation tasks.
-
B.
multimodal large language model family
A multimodal large language model family is a group of related neural models that can jointly process and generate multiple data modalities—such as text, images, audio, or video—using shared architectures, training objectives, and parameterizations.
-
C.
deep learning model
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
-
D.
former model
A former model is an individual who previously worked professionally in modeling but has since left the industry or no longer does it as their primary occupation.
-
E.
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.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:29 a.m.