Triple
T13399175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uyghur Cyrillic alphabet |
E319781
|
entity |
| Predicate | hasCharacterSetType |
P8572
|
FINISHED |
| Object | alphabetic |
—
|
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: alphabetic | Statement: [Uyghur Cyrillic alphabet, hasCharacterSetType, alphabetic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacterSetType Context triple: [Uyghur Cyrillic alphabet, hasCharacterSetType, alphabetic]
-
A.
characterSetType
chosen
Indicates the type or category of character set associated with or used by an entity.
-
B.
usesCharacterSet
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
C.
hasTypicalCharacterType
Indicates that an entity is commonly associated with or exemplified by a particular type of character or persona.
-
D.
hasDistinctCharacterSet
Indicates that two compared items use different sets of characters, with no character set being a subset or duplicate of the other.
-
E.
hasCharacterClass
Indicates that an entity (such as a character) belongs to or is assigned a particular character class or role type.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae47e99081909d8b5dba97a11988 |
completed | April 12, 2026, 2:38 p.m. |
| PD | Predicate disambiguation | batch_69d9a0355de48190bb3fb96912e20df3 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:34 p.m.