Triple
T35874674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quran 19:7 |
E1037325
|
entity |
| Predicate | containsArabicText |
P6450
|
FINISHED |
| Object | يَا زَكَرِيَّا إِنَّا نُبَشِّرُكَ بِغُلَامٍ اسْمُهُ يَحْيَى لَمْ نَجْعَل لَّهُ مِن قَبْلُ سَمِيًّا |
—
|
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: يَا زَكَرِيَّا إِنَّا نُبَشِّرُكَ بِغُلَامٍ اسْمُهُ يَحْيَى لَمْ نَجْعَل لَّهُ مِن قَبْلُ سَمِيًّا | Statement: [Quran 19:7, containsArabicText, يَا زَكَرِيَّا إِنَّا نُبَشِّرُكَ بِغُلَامٍ اسْمُهُ يَحْيَى لَمْ نَجْعَل لَّهُ مِن قَبْلُ سَمِيًّا]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsArabicText Context triple: [Quran 19:7, containsArabicText, يَا زَكَرِيَّا إِنَّا نُبَشِّرُكَ بِغُلَامٍ اسْمُهُ يَحْيَى لَمْ نَجْعَل لَّهُ مِن قَبْلُ سَمِيًّا]
-
A.
hasArabComponent
Indicates that something includes or contains an Arab-related part, element, or component as one of its constituents.
-
B.
hasNameInArabic
chosen
Indicates that an entity is associated with a specific name expressed in the Arabic language.
-
C.
endsWithArabic
Indicates that one entity’s content or representation terminates with Arabic script or characters.
-
D.
hasLatinText
Indicates that an entity possesses or is associated with text written in Latin.
-
E.
hasKeyTermArabic
Indicates that an entity is associated with a specific key term expressed in the Arabic 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_69f76e1e701c8190a4990d4978ce4fe6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a00d76d0e0881908d83a8dcce511167 |
completed | May 10, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_6a00d711805881909a94cfd1f25fb331 |
completed | May 10, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.