Triple
T30481858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon EOS R5 |
E775608
|
entity |
| Predicate | hasAnimalDetectionAF |
P181433
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Canon EOS R5, hasAnimalDetectionAF, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnimalDetectionAF Context triple: [Canon EOS R5, hasAnimalDetectionAF, true]
-
A.
aimsToDetect
Indicates an intention or designed purpose to discover, identify, or recognize the presence or characteristics of something.
-
B.
hasDigitalFocus
Indicates that an entity is primarily oriented toward or centered on digital technologies, channels, or activities.
-
C.
hasModeOfDetection
Indicates that one entity is identified, measured, or observed using a specified method or technique of detection.
-
D.
binaryDetectionMethod
Indicates a method or technique used to detect or distinguish between two possible states, conditions, or outcomes.
-
E.
hasIntruderDetectionSystem
Indicates that an entity is equipped with a system designed to detect unauthorized or intruding entities.
- 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_69f22497341481909c21ba329fadaa6b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7764ab1fc81909f9348db87bd7692 |
completed | May 3, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69f76905d9c88190b1ee810bc9ab644f |
completed | May 3, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69f77648979c8190b6cdbb835ab8987c |
completed | May 3, 2026, 4:22 p.m. |
Created at: April 29, 2026, 8:12 p.m.