Triple
T36491442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CIDEr |
E899060
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | automatic 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: automatic evaluation metric Context triple: [CIDEr, instanceOf, automatic evaluation metric]
-
A.
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.
-
B.
training evaluation model
A training evaluation model is a structured framework used to systematically assess the effectiveness, impact, and quality of a training program against its objectives using defined criteria and metrics.
-
C.
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.
-
D.
metric-based meta-learning method
A metric-based meta-learning method is an approach that learns a similarity measure or embedding space so that new tasks can be solved by comparing query examples to a small set of labeled support examples using distance-based inference.
-
E.
benchmark in artificial intelligence
A benchmark in artificial intelligence is a standardized task, dataset, or evaluation protocol used to quantitatively compare and assess the performance of AI models and algorithms.
- 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.