Triple
T12746298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | iPhone 13 Pro |
E304611
|
entity |
| Predicate | cameraFeature |
P29799
|
FINISHED |
| Object | Deep Fusion |
E961189
|
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: Deep Fusion | Statement: [iPhone 13 Pro, cameraFeature, Deep Fusion]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deep Fusion Context triple: [iPhone 13 Pro, cameraFeature, Deep Fusion]
-
A.
Deep Fusion
chosen
Deep Fusion is an Apple computational photography technology that uses machine learning to combine multiple exposures for sharper, more detailed photos with improved texture and reduced noise.
-
B.
Deep Image
Deep Image is a mid-20th-century American poetic movement emphasizing vivid, often surreal imagery to evoke deep psychological and emotional resonance.
-
C.
Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
-
D.
DenseNet
DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
-
E.
Reformer architecture
The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
- 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96bd42fe08190a85467b1a998d2af |
completed | April 10, 2026, 9:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c94265481908ace9cac757df890 |
completed | May 2, 2026, 10:37 p.m. |
Created at: April 9, 2026, 5:27 p.m.