Triple
T36491441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CIDEr |
E899060
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image captioning evaluation metric |
C31098
|
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 evaluation metric Context triple: [CIDEr, instanceOf, image captioning evaluation metric]
-
A.
image captioning model
An image captioning model is a system that automatically generates descriptive natural language sentences that explain the content of an input image.
-
B.
image generation quality metric
An image generation quality metric is a quantitative measure used to evaluate how realistic, coherent, and faithful generated images are to a given prompt or reference, often combining perceptual similarity, diversity, and semantic alignment.
-
C.
evaluation metric
chosen
An evaluation metric is a quantitative measure used to assess the performance, quality, or effectiveness of a model, system, or process against defined criteria or ground truth.
-
D.
image quality metric
An image quality metric is a quantitative measure used to assess how closely a processed or transmitted image matches a reference or desired visual standard, often reflecting perceived visual fidelity or distortion.
-
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.
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.