Triple
T9260379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surah Fussilat |
E222560
|
entity |
| Predicate | hasAyahCount |
P87842
|
FINISHED |
| Object | 54 |
—
|
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: 54 | Statement: [Surah Fussilat, hasAyahCount, 54]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAyahCount Context triple: [Surah Fussilat, hasAyahCount, 54]
-
A.
hasHizbCount
Indicates the number of hizbs (sections/parts) associated with or assigned to a given entity.
-
B.
hasJuzCount
Indicates that an entity (such as a text or document) is associated with a specific number of Juz (sections or parts).
-
C.
hasVersesBy
Indicates a relationship where a work, such as a song or poem, contains verses authored or written by a specific creator.
-
D.
hasImam
Indicates that an entity is associated with, led by, or served by a particular imam in a religious or community context.
-
E.
hasVersesIn
Indicates that one entity (typically a text, chapter, or section) contains or is composed of verses found within another entity (such as a book, collection, or scripture).
- 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_69ca841e4cd481908e738c74e958eaea |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07160e408190be4bd7b757260a0e |
completed | April 1, 2026, 11:52 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4e79e48190b3200247f4624867 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:32 p.m.