Triple
T30358262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nikon F-mount DSLR system |
E772203
|
entity |
| Predicate | autofocusSupport |
P85625
|
FINISHED |
| Object | AF |
—
|
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: AF | Statement: [Nikon F-mount DSLR system, autofocusSupport, AF]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: autofocusSupport Context triple: [Nikon F-mount DSLR system, autofocusSupport, AF]
-
A.
autofocusSystem
Indicates that there is an autofocus mechanism or method used to automatically adjust focus in an imaging or optical system.
-
B.
hasAutofocusSystem
chosen
Indicates that an entity is equipped with a system capable of automatically adjusting focus.
-
C.
accessibilityFocus
Indicates that a particular user interface element is currently the primary target of accessibility tools, such as screen readers or keyboard navigation.
-
D.
canonicalFocus
Indicates that one entity is the primary or most representative focus or point of attention in relation to another entity.
-
E.
autofocusPoints
Indicates the relationship between a camera (or imaging device) and the specific focus points it can automatically select or use for focusing.
- 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_69f2248c6f5c8190a6177842bf791a3c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f682417ec08190982dd9acf7219742 |
completed | May 2, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 7:57 p.m.