Triple
T30842854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leica M-mount |
E785557
|
entity |
| Predicate | supportsRangefinderFramelines |
P207311
|
FINISHED |
| Object | 28 mm |
—
|
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: 28 mm | Statement: [Leica M-mount, supportsRangefinderFramelines, 28 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsRangefinderFramelines Context triple: [Leica M-mount, supportsRangefinderFramelines, 28 mm]
-
A.
focalLengthRange
Indicates the range of focal lengths over which an optical device (such as a lens) can operate or be adjusted.
-
B.
canTrackTargetsSimultaneously
Indicates the ability of an entity to follow or monitor multiple targets at the same time.
-
C.
hasFramingDevice
Indicates that one entity serves as a narrative or structural framing device that contextualizes, introduces, or encloses the main content of another entity.
-
D.
hasFrontLineFeature
Indicates that an entity possesses a specific characteristic or element located on its front side or leading edge.
-
E.
viewfinderCoverage
Indicates the extent to which what is seen through a camera’s viewfinder matches the actual area captured in the final image.
- F. None of above. chosen
Provenance (4 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_69f224b850848190a4af4ccf8ddadcdf |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 8:45 p.m.