Triple
T13653207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phillip Isola |
E326790
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | pix2pix |
E971752
|
NE FINISHED |
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: pix2pix | Statement: [Phillip Isola, notableWork, pix2pix]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: pix2pix Context triple: [Phillip Isola, notableWork, pix2pix]
-
A.
Pix2Pix
chosen
Pix2Pix is a conditional generative adversarial network (cGAN) framework for paired image-to-image translation tasks, such as turning sketches into photos or maps into satellite images.
-
B.
CycleGAN
CycleGAN is a type of generative adversarial network designed for unpaired image-to-image translation, enabling conversion between visual domains without requiring matched training examples.
-
C.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
-
D.
INTERPOL Diffusion
INTERPOL Diffusion is a decentralized alert mechanism used within the INTERPOL network to rapidly share information about wanted persons, threats, or criminal activity among selected member countries.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8076d8270819092afc2f0e9c359a8 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60ace048190a4b92310ba272bd1 |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78affa3c481909dba71e2ce9f44c1 |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:52 p.m.