Triple
T1720033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Desktop Window Manager |
E37367
|
entity |
| Predicate | usesRenderingModel |
P15683
|
FINISHED |
| Object | composited off-screen surfaces |
—
|
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: composited off-screen surfaces | Statement: [Desktop Window Manager, usesRenderingModel, composited off-screen surfaces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRenderingModel Context triple: [Desktop Window Manager, usesRenderingModel, composited off-screen surfaces]
-
A.
renderingModel
chosen
Indicates the specific rendering engine or model used to generate the visual or graphical output for an entity.
-
B.
renderedBy
Indicates that something is produced, drawn, or visually generated by a particular agent, tool, or process.
-
C.
usedProgramModel
Indicates that an entity employed a specific program model as the basis or framework for its activities or operations.
-
D.
usesUniform
Indicates that one entity regularly wears or employs a standardized set of clothing or equipment designated as a uniform.
-
E.
notableRendering
Indicates that one entity is a significant or well-known visual or artistic depiction of another entity.
- 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_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab5c96db6c8190a745d6fef7bf2cdb |
completed | March 6, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69aa61bed2fc819086d912cd34285978 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.