Triple
T31008976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fujifilm X-T30 |
E790152
|
entity |
| Predicate | supportsFilmSimulations |
P207334
|
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: [Fujifilm X-T30, supportsFilmSimulations, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsFilmSimulations Context triple: [Fujifilm X-T30, supportsFilmSimulations, yes]
-
A.
filmSimulationModes
Indicates the different film simulation modes or profiles that can be applied to an image or video in a camera or imaging system.
-
B.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
C.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
-
D.
appliesToFilm
Indicates that something (such as a rule, feature, attribute, or condition) is relevant or applicable to a particular film.
-
E.
hasCinematicFeature
Indicates that something possesses a specific cinematic characteristic, quality, or element related to film or visual storytelling.
- 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_69f224c73ca48190a1e46cb58ad4045b |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e5174c8190a0bdde7e381b7624 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 8:57 p.m.