Triple
T7225130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qur’an 96:1 |
E150359
|
entity |
| Predicate | openingWordsTransliteration |
P5923
|
FINISHED |
| Object | Iqra’ bismi rabbika alladhī khalaq |
—
|
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: Iqra’ bismi rabbika alladhī khalaq | Statement: [Qur’an 96:1, openingWordsTransliteration, Iqra’ bismi rabbika alladhī khalaq]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: openingWordsTransliteration Context triple: [Qur’an 96:1, openingWordsTransliteration, Iqra’ bismi rabbika alladhī khalaq]
-
A.
formerTransliteration
Indicates that one transliteration was previously used for an entity but has since been replaced by a different transliteration.
-
B.
transliterationTarget
Indicates that one entity is the target script or form into which another entity is transliterated.
-
C.
translationOfOpeningWords
Indicates that one text is a translation of the initial words or opening phrase of another text.
-
D.
alternativeTransliteration
chosen
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
-
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_69c687effb44819092b95d07d0368c9f |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e9dc835881909ea646c392a980b6 |
completed | March 27, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69c6e761b7fc8190857794d78af1b468 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:54 p.m.