Triple
T4240664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayin |
E95403
|
entity |
| Predicate | transliteratedAsInScholarlyWorks |
P5923
|
FINISHED |
| Object | ʿ |
—
|
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: ʿ | Statement: [Ayin, transliteratedAsInScholarlyWorks, ʿ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transliteratedAsInScholarlyWorks Context triple: [Ayin, transliteratedAsInScholarlyWorks, ʿ]
-
A.
alternativeTransliteration
chosen
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
-
B.
standardTransliteration
Indicates that one representation of text is a transliteration of another according to a recognized standard or convention.
-
C.
translatedWorkOf
Indicates that one work is a translation of another original work.
-
D.
commonTransliterationSystem
Indicates that two or more written forms are derived using the same standardized system for converting text from one script to another.
-
E.
transliterationLanguage
Indicates the language whose writing system is used as the target when converting text from one script to another.
- 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_69b3453d91548190b4d4ef8fe52aa2ac |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e87c9d88190a093b1289df5b974 |
completed | March 12, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69b347f587148190a1830503459939b6 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:05 p.m.