Triple
T567899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gypsy text editor |
E13594
|
entity |
| Predicate | visualModel |
P2006
|
FINISHED |
| Object | what-you-see-is-what-you-get layout |
—
|
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: what-you-see-is-what-you-get layout | Statement: [Gypsy text editor, visualModel, what-you-see-is-what-you-get layout]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualModel Context triple: [Gypsy text editor, visualModel, what-you-see-is-what-you-get layout]
-
A.
visualForm
Indicates the visual appearance, shape, or structural pattern that characterizes how something looks.
-
B.
model
chosen
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
C.
visionOf
Indicates that one entity is a visual representation, image, or depiction of another entity.
-
D.
vision
Indicates that an entity perceives another entity or object visually, using sight.
-
E.
visualizationLibrary
Indicates that an entity uses, depends on, or is implemented with a particular visualization library for rendering or displaying visual data.
- 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_69a4933edcf08190b35ecfd6014caee6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b02da148190b6a9bad3a22d8ec5 |
completed | March 1, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69a494c183b081909304944aa3d0fe8f |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.