Triple
T30481842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon EOS R5 |
E775608
|
entity |
| Predicate | sensorSize |
P101719
|
FINISHED |
| Object | full-frame |
—
|
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: full-frame | Statement: [Canon EOS R5, sensorSize, full-frame]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sensorSize Context triple: [Canon EOS R5, sensorSize, full-frame]
-
A.
rearCameraSensorSize
chosen
Indicates the physical dimensions of the image sensor used by the device’s rear camera.
-
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.
opticalSize
Indicates that one entity has a particular visual or perceived size relative to another, often dependent on viewing conditions or context.
-
D.
viewfinderResolution
Indicates the resolution or level of detail provided by a device’s viewfinder display.
-
E.
modelSize
Indicates the quantitative measure of how large or complex a model is, typically in terms of parameters, layers, or memory footprint.
- 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_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:12 p.m.