Triple
T34613402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors) |
E888797
|
entity |
| Predicate | featureCountSpeeds |
P204218
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountSpeeds, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureCountSpeeds Context triple: [A Camera Recording Its Own Condition (7 apertures, 10 speeds, 2 mirrors), featureCountSpeeds, 10]
-
A.
speedDescribedAs
Indicates that one entity characterizes or labels the speed of another entity using a particular description or term.
-
B.
hasAverageSurfaceSpeed
Indicates the typical or mean speed at which something moves across a surface over a given period or distance.
-
C.
featuresSample
Indicates that an entity includes or presents a particular sample as one of its components or examples.
-
D.
speedupType
Indicates the kind or category of performance improvement achieved relative to a baseline.
-
E.
designedServiceSpeed
Indicates the intended or specified operational speed at which a service is designed to function.
- 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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a034af7066c81908719bff353d77b16 |
completed | May 12, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_6a034a2fc2308190b0b8fbd458a39323 |
completed | May 12, 2026, 3:41 p.m. |
| PDg | Predicate description generation | batch_6a034af656d88190881820908f6d2958 |
completed | May 12, 2026, 3:44 p.m. |
Created at: May 1, 2026, 2:03 a.m.