Triple
T30481907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony FE 85mm f/1.4 GM |
E775609
|
entity |
| Predicate | manualFocusRing |
P169491
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Sony FE 85mm f/1.4 GM, manualFocusRing, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: manualFocusRing Context triple: [Sony FE 85mm f/1.4 GM, manualFocusRing, yes]
-
A.
accessibilityFocus
Indicates that a particular user interface element is currently the primary target of accessibility tools, such as screen readers or keyboard navigation.
-
B.
canonicalFocus
Indicates that one entity is the primary or most representative focus or point of attention in relation to another entity.
-
C.
importFocus
Indicates that attention, priority, or emphasis is being brought into or concentrated on a particular entity or aspect.
-
D.
lessFocusOn
Indicates that one entity directs reduced attention, emphasis, or priority toward another entity or activity compared to alternatives.
-
E.
hasRDFocus
Indicates that something has a specific region of interest or focal area within an image, scene, or dataset that is being emphasized or analyzed.
- 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_69f68742cc6481908be525603fb6ba97 |
completed | May 2, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f67d31cc60819084f64bd056e1ea4d |
completed | May 2, 2026, 10:39 p.m. |
Created at: April 29, 2026, 8:12 p.m.