Triple
T24151127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ISO/IEC 8859-14 |
E598535
|
entity |
| Predicate | includesCharacterSet |
P7661
|
FINISHED |
| Object | ASCII (in lower half) |
—
|
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: ASCII (in lower half) | Statement: [ISO/IEC 8859-14, includesCharacterSet, ASCII (in lower half)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesCharacterSet Context triple: [ISO/IEC 8859-14, includesCharacterSet, ASCII (in lower half)]
-
A.
usesCharacterSet
chosen
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
B.
containsCharacter
Indicates that one entity includes a specific character as part of its content or composition.
-
C.
hasDistinctCharacterSet
Indicates that two compared items use different sets of characters, with no character set being a subset or duplicate of the other.
-
D.
hasCharacterSetSizeCategory
Indicates the relationship between something and the category that classifies the size of its character set.
-
E.
hasLanguageCharacter
Indicates that an entity uses, contains, or is associated with a specific written or symbolic character from a language.
- 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_69e288c9e488819093dd1acd91b08b8a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e0e108308190ba8740590a1c5130 |
completed | April 29, 2026, 10:43 a.m. |
| PD | Predicate disambiguation | batch_69f176585f3481909beb907de252cd98 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 11:30 p.m.