Triple
T12207438
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wasserstein GAN |
E290870
|
entity |
| Predicate | proposedInPaper |
P35415
|
FINISHED |
| Object | Wasserstein GAN |
E290870
|
NE FINISHED |
How this triple was built (3 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: Wasserstein GAN | Statement: [Wasserstein GAN, proposedInPaper, Wasserstein GAN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wasserstein GAN Context triple: [Wasserstein GAN, proposedInPaper, Wasserstein GAN]
-
A.
Wasserstein GAN
chosen
Wasserstein GAN is a variant of generative adversarial networks that improves training stability and sample quality by optimizing the Wasserstein (Earth Mover’s) distance between real and generated data distributions.
-
B.
Conditional GAN
A Conditional GAN is a type of generative adversarial network that produces data samples conditioned on auxiliary information such as class labels or input images, enabling controlled and targeted generation.
-
C.
Progressive GAN
Progressive GAN is a generative adversarial network architecture that grows both the generator and discriminator layers progressively during training to produce high-resolution, high-quality synthetic images.
-
D.
Deep Convolutional GAN
Deep Convolutional GAN is a widely used GAN architecture that replaces fully connected layers with deep convolutional layers to generate high-quality, realistic images.
-
E.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proposedInPaper Context triple: [Wasserstein GAN, proposedInPaper, Wasserstein GAN]
-
A.
proposedInContextOf
Indicates that something (such as an idea, action, or change) was proposed specifically within, and in relation to, a particular situation, setting, or surrounding circumstances.
-
B.
proposedPart
Indicates that one entity has been suggested or put forward to serve as a component or element of another entity.
-
C.
proposedInYear
Indicates that something, such as a plan, idea, or piece of legislation, was formally put forward or suggested in a specific calendar year.
-
D.
publishedWhitePaperOn
Indicates that an entity has authored and released a formal white paper on a specified topic or subject.
-
E.
includedInPublication
chosen
Indicates that one entity (such as a work, section, or item) appears as part of, or is contained within, a specific publication.
- F. None of above.
Provenance (4 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e5666f48190a28eed761e7b9210 |
completed | May 2, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.