Triple
T11003488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Show and Tell: A Neural Image Caption Generator |
E260056
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image captioning model |
C28999
|
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: image captioning model Context triple: [Show and Tell: A Neural Image Caption Generator, instanceOf, image captioning model]
-
A.
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.
-
B.
image recognition model
An image recognition model is a computational system that analyzes visual input to automatically identify, classify, and sometimes localize objects, patterns, or features within images.
-
C.
visual discovery engine
A visual discovery engine is a system that helps users explore and find relevant content, products, or ideas primarily through images and visual cues rather than text-based search.
-
D.
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.
-
E.
imager
An imager is a component or system that captures, generates, or processes visual representations of data, scenes, or objects into image form.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:25 p.m.