Triple
T28898640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shift JIS |
E732895
|
entity |
| Predicate | characterWidthModel |
P70658
|
FINISHED |
| Object | variable-width encoding |
—
|
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: variable-width encoding | Statement: [Shift JIS, characterWidthModel, variable-width encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterWidthModel Context triple: [Shift JIS, characterWidthModel, variable-width encoding]
-
A.
encodingWidth
chosen
Indicates the width dimension used when encoding a signal, image, or data stream.
-
B.
characterSetSize
Indicates the total number of distinct characters contained in or allowed by a given character set.
-
C.
widthInColumns
Indicates the number of column units that an element or item spans within a grid or layout.
-
D.
typicalWidth
Indicates the usual or characteristic width associated with an entity, as opposed to an exact or measured width in a specific instance.
-
E.
cellLength
Indicates the measured extent of a cell from one end to the other, typically along its longest axis.
- 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_69f05b08c2008190ac426a035a2ed66d |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69fd19f791f48190bbb6f6047f9ddc59 |
completed | May 7, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69fd0df365948190bc9bfc7ffd46acd8 |
completed | May 7, 2026, 10:10 p.m. |
Created at: April 28, 2026, 8:01 a.m.