Triple
T6581221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Windows-1252 |
E157300
|
entity |
| Predicate | characterRepertoireSize |
P58060
|
FINISHED |
| Object | 256 code points |
—
|
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: 256 code points | Statement: [Windows-1252, characterRepertoireSize, 256 code points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterRepertoireSize Context triple: [Windows-1252, characterRepertoireSize, 256 code points]
-
A.
characterSetSize
chosen
Indicates the total number of distinct characters contained in or allowed by a given character set.
-
B.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
C.
characterCoverage
Indicates that one entity provides or includes sufficient representation or support for the characters (e.g., glyphs, symbols, or scripts) required or used by another entity.
-
D.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
E.
hasGlyphRepertoireSize
Indicates the number of distinct glyphs included in an entity’s glyph repertoire.
- 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_69c6882b3a108190b3a9eb343ae4162c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6c07cdf048190945ca5810fb1de88 |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6acfb462481909cb7aff5af4bca9d |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:54 p.m.