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 |
P12152
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountApertures, 7]
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 (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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0349d158b881908bfbdb501ea565ee |
completed | May 12, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_6a034750e3d48190a88ee3604a36b46d |
completed | May 12, 2026, 3:29 p.m. |
Created at: May 1, 2026, 2:03 a.m.