Triple
T30358107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon EF lens mount |
E772200
|
entity |
| Predicate | usedWithSensorFormat |
P4791
|
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 EF lens mount, usedWithSensorFormat, full-frame]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedWithSensorFormat Context triple: [Canon EF lens mount, usedWithSensorFormat, full-frame]
-
A.
sensorFormat
Indicates the data encoding or representation standard used by a sensor to output its measurements.
-
B.
usedFormat
Indicates that one entity employs or applies a particular format or representation in relation to another entity.
-
C.
usedInFormat
Indicates that something (such as a method, standard, or component) is employed or applied within a particular format.
-
D.
hasSensor
Indicates that one entity is equipped with, contains, or uses a particular sensor.
-
E.
usedWith
chosen
Indicates that one entity is typically or appropriately employed together with another entity in a combined or complementary use.
- 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_69f2248c6f5c8190a6177842bf791a3c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f682417ec08190982dd9acf7219742 |
completed | May 2, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 7:57 p.m.