Triple
T18016072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VOCSegmentation |
E431001
|
entity |
| Predicate | operatesOn |
P23
|
FINISHED |
| Object | PASCAL VOC 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: PASCAL VOC dataset | Statement: [VOCSegmentation, operatesOn, PASCAL VOC dataset]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PASCAL VOC dataset Context triple: [VOCSegmentation, operatesOn, PASCAL VOC dataset]
-
A.
PASCAL VOC
chosen
PASCAL VOC is a benchmark dataset and challenge in computer vision, widely used for evaluating algorithms on tasks like object detection and image segmentation.
-
B.
ImageNet
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
-
C.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
D.
SVRC
SVRC is the abbreviation for the Singapore Volunteer Rifle Corps, a 19th-century volunteer military unit formed to bolster the defense of colonial Singapore.
-
E.
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.
- 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_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.