Triple
T28584025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luna theme |
E723443
|
entity |
| Predicate | supportsFontSmoothing |
P203049
|
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: [Luna theme, supportsFontSmoothing, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsFontSmoothing Context triple: [Luna theme, supportsFontSmoothing, true]
-
A.
supportsHardwareAcceleration
Indicates that one entity enables or provides hardware-based acceleration capabilities for another entity’s operations or processes.
-
B.
supportsGraphicsSwitching
Indicates that an entity can dynamically switch between different graphics hardware or rendering modes, typically to balance performance and power usage.
-
C.
supportsRasterization
Indicates that one entity provides the capability or functionality for another entity to perform rasterization operations.
-
D.
supports2DGraphicsAcceleration
Indicates that an entity provides hardware or software capabilities to accelerate the processing and rendering of two-dimensional graphics operations.
-
E.
supportsWindowSystem
Indicates that one entity provides compatibility with, or operational support for, a particular windowing system used to manage graphical user interfaces.
- 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_69f01d7f92e481909847f5f3f3174a89 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_6a0119132e848190820a688d139fbf75 |
completed | May 10, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_6a01188dfdec8190b7f675264a281733 |
completed | May 10, 2026, 11:45 p.m. |
| PDg | Predicate description generation | batch_6a0119127ca481909e921e7d95716b00 |
completed | May 10, 2026, 11:47 p.m. |
Created at: April 28, 2026, 4:16 a.m.