Triple
T12165283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Panavision 70 |
E289817
|
entity |
| Predicate | comparedTo35mm |
P64127
|
FINISHED |
| Object | larger image area |
—
|
LITERAL 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: larger image area | Statement: [Super Panavision 70, comparedTo35mm, larger image area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comparedTo35mm Context triple: [Super Panavision 70, comparedTo35mm, larger image area]
-
A.
advantageOverStandard35mm
chosen
Indicates that something offers a benefit or improvement when compared to the standard 35mm format.
-
B.
viewfinderResolution
Indicates the resolution or level of detail provided by a device’s viewfinder display.
-
C.
isPhotographicSubject
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
D.
rearCameraSensorSize
Indicates the physical dimensions of the image sensor used by the device’s rear camera.
-
E.
hasLongFocalLength
Indicates that one entity possesses or is characterized by a focal length that is relatively long compared to a standard or reference.
- F. None of above.
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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91621ca6c81908365732f361aef13 |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150e85348190b9b47cda4a17dcd0 |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:50 p.m.