Triple
T6962021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adobe Firefly |
E161390
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Adobe Firefly Image Generation |
E161390
|
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: Adobe Firefly Image Generation | Statement: [Adobe Firefly, hasComponent, Adobe Firefly Image Generation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adobe Firefly Image Generation Context triple: [Adobe Firefly, hasComponent, Adobe Firefly Image Generation]
-
A.
Adobe Firefly (generative AI services)
chosen
Adobe Firefly is Adobe’s suite of generative AI tools designed to create and enhance images, text effects, and other creative assets within its digital media ecosystem.
-
B.
DALL·E
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
-
C.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
-
D.
Pictures Generation
Pictures Generation is a late-1970s and 1980s American art movement associated with appropriation and critical re-use of mass-media imagery to question authorship, originality, and representation.
-
E.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6daf07e3481909aa79b8e0f1b1be7 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7589c587c8190b97523b5ac2ab958 |
completed | March 28, 2026, 4:27 a.m. |
Created at: March 27, 2026, 2:30 p.m.