Triple
T7935767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monochrome Display Adapter |
E184284
|
entity |
| Predicate | textResolution |
P30785
|
FINISHED |
| Object | 720×350 effective text resolution |
—
|
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: 720×350 effective text resolution | Statement: [Monochrome Display Adapter, textResolution, 720×350 effective text resolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textResolution Context triple: [Monochrome Display Adapter, textResolution, 720×350 effective text resolution]
-
A.
mainResolution
Indicates that one resolution is the primary or most important resolution associated with a given context or entity.
-
B.
displayResolution
Indicates the relationship specifying the width and height dimensions at which visual content is rendered or shown on a display.
-
C.
typicalResolution
Indicates the usual or standard level of detail or clarity at which something (such as an image, display, or representation) is normally rendered or presented.
-
D.
characterCellResolution
chosen
Indicates a relationship where the visual or logical representation of characters is determined or adjusted at the level of individual cells (e.g., grid units or display slots).
-
E.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
- 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_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3aec394081909a9569c02ac372af |
completed | March 31, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69cae9335f288190ba96781fd6576a2b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:08 p.m.