Triple
T34613401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors) |
E888797
|
entity |
| Predicate | featureCountApertures |
—
|
GENERATED |
| Object | 7 |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureCountApertures Context triple: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountApertures, 7]
-
A.
hasApertureShape
Indicates that an entity’s aperture (opening) has a specific geometric or descriptive shape.
-
B.
hasAperture
chosen
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
C.
numberOfApses
Indicates the quantity of apses associated with a given structure or entity.
-
D.
numberOfHoles
Indicates the count of holes associated with or present in a given entity.
-
E.
numberOfFissures
Indicates the count of distinct fissures associated with a given entity or structure.
- F. None of above.
Provenance (1 batch)
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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 2:03 a.m.