Triple
T18016566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RetinaNet |
E431010
|
entity |
| Predicate | evaluatedOn |
P82415
|
FINISHED |
| Object | COCO dataset |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: COCO dataset | Statement: [RetinaNet, evaluatedOn, COCO dataset]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: COCO dataset Context triple: [RetinaNet, evaluatedOn, COCO dataset]
-
A.
COCO captioning challenge
The COCO captioning challenge is a computer vision and natural language processing competition where systems generate descriptive text captions for images from the COCO dataset.
-
B.
CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
-
C.
COCO object detection benchmarks
chosen
COCO object detection benchmarks are widely used large-scale evaluation standards for measuring and comparing the performance of object detection algorithms on the COCO dataset.
-
D.
LSUN dataset
The LSUN dataset is a large-scale image collection focused on scenes and objects, widely used to train and evaluate deep learning models for image generation and recognition.
-
E.
PASCAL VOC
PASCAL VOC is a benchmark dataset and challenge in computer vision, widely used for evaluating algorithms on tasks like object detection and image segmentation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9be5d0c819097e006f32d98753a |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 10:24 a.m.