Triple
T28505099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caja |
E721346
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | MATE project component |
C26474
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: MATE project component Context triple: [Caja, instanceOf, MATE project component]
-
A.
MATE desktop component
chosen
A MATE desktop component is an individual software element (such as a panel, applet, or system tool) that provides a specific piece of functionality within the MATE desktop environment.
-
B.
machine learning platform component
A machine learning platform component is a modular software element that provides specific functionality—such as data processing, model training, deployment, or monitoring—within an integrated ML lifecycle system.
-
C.
AmigaOS component
An AmigaOS component is a modular software or hardware element that provides specific functionality within the Amiga operating system environment, such as device drivers, libraries, or system tools.
-
D.
Android platform component
An Android platform component is a fundamental building block of an Android application (such as an Activity, Service, BroadcastReceiver, or ContentProvider) that interacts with the system and other apps to provide specific functionality within the Android operating environment.
-
E.
GNOME component
A GNOME component is a modular software element within the GNOME desktop environment that provides specific functionality or services, such as panels, applets, libraries, or system tools, to create a cohesive user experience.
- F. None of above.
Provenance (1 batch)
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_69f01a5c072081908c7b04bcf6478da9 |
completed | April 28, 2026, 2:24 a.m. |
Created at: April 28, 2026, 3:09 a.m.