Triple
T7902626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juz 16 |
E183491
|
entity |
| Predicate | hasTotalNumberOfJuz |
P71345
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [Juz 16, hasTotalNumberOfJuz, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalNumberOfJuz Context triple: [Juz 16, hasTotalNumberOfJuz, 30]
-
A.
hasJuzCount
chosen
Indicates that an entity (such as a text or document) is associated with a specific number of Juz (sections or parts).
-
B.
hasJuzRange
Indicates that an entity (such as a text or passage) spans, is associated with, or falls within a specified range of Juz segments.
-
C.
hasTotalNumber
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
-
D.
quranicJuz
Indicates that a specific portion or passage belongs to, or is contained within, a particular Juz (section) of the Quran.
-
E.
hasHizbCount
Indicates the number of hizbs (sections/parts) associated with or assigned to a given entity.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a40a0508190864479c2c41b12cb |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92d94448190b4425bbfb64c658c |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:02 p.m.