Triple
T34613403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors) |
E888797
|
entity |
| Predicate | featureCountMirrors |
P8981
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountMirrors, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureCountMirrors Context triple: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountMirrors, 2]
-
A.
mirrorCount
chosen
Indicates the number of mirrors associated with or present in relation to a given entity or context.
-
B.
hasNumberOfPrimaryMirrors
Indicates the relationship specifying how many primary mirrors are associated with a given entity.
-
C.
trialMirrors
Indicates that one trial serves as a mirror or counterpart to another trial, reflecting its conditions, structure, or outcomes for comparison or replication purposes.
-
D.
mirrorsStructureOf
Indicates that one entity’s overall organization, pattern, or arrangement closely corresponds to and reflects the structure of another entity.
-
E.
featureCountSpeeds
Indicates that an entity specifies or records the number of distinct speed options or speed levels associated with another entity.
- 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_6a034ca1a8d88190bd44f33814436e59 |
completed | May 12, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_6a034b591f2481908e05562841c9a3c2 |
completed | May 12, 2026, 3:46 p.m. |
Created at: May 1, 2026, 2:03 a.m.