Triple
T25787286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al-Arba'in al-Nawawiyya |
E649454
|
entity |
| Predicate | traditionalNumberOfHadiths |
P26982
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [Al-Arba'in al-Nawawiyya, traditionalNumberOfHadiths, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalNumberOfHadiths Context triple: [Al-Arba'in al-Nawawiyya, traditionalNumberOfHadiths, 40]
-
A.
hadithNarrationsCountApprox
chosen
Indicates an approximate number of hadith narrations associated with an entity.
-
B.
hadithCollections
Indicates a relationship where one entity is a collection that contains, organizes, or compiles hadiths (reports of the sayings, actions, or approvals of the Prophet).
-
C.
numberOfSurahs
Indicates the total count of surahs associated with a given entity (such as a text, section, or collection).
-
D.
hadithMethod
Indicates the method, chain, or approach by which a hadith is transmitted, recorded, or authenticated in relation to another entity.
-
E.
hadithsNarrated
Indicates that one entity (typically a person) has narrated, transmitted, or reported the hadiths associated with another entity (such as a collection, text, or individual hadith).
- 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_69e7ab33e9308190afe415dc6f9e8876 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6c49627908190b3553474c7c3072b |
completed | May 3, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f23ae081909a52801266063a3c |
completed | May 3, 2026, 3:41 a.m. |
Created at: April 22, 2026, 5:56 a.m.