Triple
T30481843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon EOS R5 |
E775608
|
entity |
| Predicate | effectiveMegapixels |
P103408
|
FINISHED |
| Object | 45 |
—
|
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: 45 | Statement: [Canon EOS R5, effectiveMegapixels, 45]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectiveMegapixels Context triple: [Canon EOS R5, effectiveMegapixels, 45]
-
A.
viewfinderResolution
Indicates the resolution or level of detail provided by a device’s viewfinder display.
-
B.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
-
C.
hasCameraResolution
chosen
Indicates that an entity is associated with a specific camera resolution value or specification.
-
D.
telephotoCameraResolution
Indicates the image resolution capability of a device’s telephoto camera in a given context.
-
E.
cropFactor
Indicates the ratio between a camera sensor’s dimensions and a reference format (typically 35mm/full-frame), expressing how much the field of view is effectively “cropped” compared to that standard.
- 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_69f22497341481909c21ba329fadaa6b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
Created at: April 29, 2026, 8:12 p.m.