Triple
T9000492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony Alpha A-mount DSLR-style cameras |
E215026
|
entity |
| Predicate | exposureModes |
P85622
|
FINISHED |
| Object | program auto |
—
|
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: program auto | Statement: [Sony Alpha A-mount DSLR-style cameras, exposureModes, program auto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exposureModes Context triple: [Sony Alpha A-mount DSLR-style cameras, exposureModes, program auto]
-
A.
exposureType
Indicates the specific manner or context in which one entity is exposed to another entity, condition, or influence.
-
B.
exposureTime
Indicates the duration for which a subject or object is exposed to a particular condition, influence, or medium.
-
C.
visibleInLongExposureImages
Indicates that the subject can be detected or seen when images are captured using long exposure photography settings.
-
D.
supportsCameraControl
Indicates that one entity provides functionality for another entity to remotely manage or adjust camera settings or operations.
-
E.
hasAperture
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
- 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_69ca83a12d648190b1e4fe11e8a31890 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6954bb1881908d004a26ba7fe360 |
completed | April 1, 2026, 12:39 a.m. |
| PD | Predicate disambiguation | batch_69cc5edd6cb48190b4fc6d6ca0418056 |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc5f6dec4081909379bd57c02a5710 |
completed | March 31, 2026, 11:57 p.m. |
Created at: March 30, 2026, 7:05 p.m.