Triple
T6581226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Windows-1252 |
E157300
|
entity |
| Predicate | usesCodePointRange |
P70659
|
FINISHED |
| Object | U+0000–U+00FF |
—
|
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: U+0000–U+00FF | Statement: [Windows-1252, usesCodePointRange, U+0000–U+00FF]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCodePointRange Context triple: [Windows-1252, usesCodePointRange, U+0000–U+00FF]
-
A.
usesCodePoints
chosen
Indicates that one entity represents, encodes, or operates using the specific set of Unicode code points defined by another entity.
-
B.
hasUnicodeCodePoint
Indicates that a character or symbol is associated with a specific numeric Unicode code point value.
-
C.
hasControlCharacterRange
Indicates that there exists a specified range of control characters associated with or applicable to an entity.
-
D.
unicodeRangeStart
Indicates the starting code point in Unicode for a specified range of characters or symbols.
-
E.
blockNumberOfCodePoints
Indicates the number of code points contained within a given block.
- 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.