Triple
T7096496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayah 28 mentions hearts finding rest in the remembrance of Allah |
E165341
|
entity |
| Predicate | ayahNumber |
P74892
|
FINISHED |
| Object | 28 |
—
|
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: 28 | Statement: [Ayah 28 mentions hearts finding rest in the remembrance of Allah, ayahNumber, 28]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ayahNumber Context triple: [Ayah 28 mentions hearts finding rest in the remembrance of Allah, ayahNumber, 28]
-
A.
areaCode
Indicates that a location, phone number, or region is associated with a specific telephone area code.
-
B.
ayahCount
Indicates the number of verses (ayahs) associated with a given surah or Quranic unit.
-
C.
hasAreaCode
Indicates that a specified telephone area code is assigned to or associated with a particular geographic region, location, or phone service entity.
-
D.
regionNumber
Indicates that an entity is assigned to or associated with a specific numbered region within a larger spatial or organizational division.
-
E.
MMSINumber
Indicates a relationship where a mobile subscriber is associated with a specific Mobile Station International ISDN Number (MSISDN) used to identify their phone line in a mobile network.
- 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_69c6887e8c10819091cee237560d32da |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e554d9f081909443be54eb501d25 |
completed | March 27, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c313e481908b61a23fc89f9332 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:41 p.m.