Triple
T5699084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kazakh Arabic alphabet |
E125612
|
entity |
| Predicate | characterSetOrigin |
P65654
|
FINISHED |
| Object | Arabic letters |
—
|
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: Arabic letters | Statement: [Kazakh Arabic alphabet, characterSetOrigin, Arabic letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterSetOrigin Context triple: [Kazakh Arabic alphabet, characterSetOrigin, Arabic letters]
-
A.
characterSetType
Indicates the type or category of character set associated with or used by an entity.
-
B.
characterSetStorage
Indicates that a particular character set is used for storing data in a given context or system.
-
C.
characterSetSize
Indicates the total number of distinct characters contained in or allowed by a given character set.
-
D.
characterSetName
Indicates the name assigned to a particular character set used for encoding or representing characters.
-
E.
usesCharacterSet
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
- F. None of above. chosen
Provenance (4 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_69c0082c96988190b3a6a201edce472a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0240ecef48190bdef10b38ecb2bd0 |
completed | March 22, 2026, 5:17 p.m. |
| PD | Predicate disambiguation | batch_69c021c2d8bc8190b947c7d1f423d2f3 |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c023dfec6881909ee6189b874b4348 |
completed | March 22, 2026, 5:16 p.m. |
Created at: March 22, 2026, 3:45 p.m.