Triple
T18255485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aditya Ramesh |
E437211
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | DALL·E 2 |
—
|
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: DALL·E 2 | Statement: [Aditya Ramesh, notableWork, DALL·E 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DALL·E 2 Context triple: [Aditya Ramesh, notableWork, DALL·E 2]
-
A.
DALL·E
chosen
DALL·E is an AI model developed by OpenAI that generates images from natural language descriptions, enabling text-to-image synthesis.
-
B.
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.
-
C.
PaLM 2
PaLM 2 is a large-scale language model developed by Google, known for powering various AI features across Google products before being succeeded by the Gemini family of models.
-
D.
Adobe Firefly (generative AI services)
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.
-
E.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd85ee548190a102611fcf709ad4 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 10:34 a.m.