Triple
T1317247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shafi'i school |
E28131
|
entity |
| Predicate | approachToHadith |
P10782
|
FINISHED |
| Object | prefers authentic hadith over local practice |
—
|
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: prefers authentic hadith over local practice | Statement: [Shafi'i school, approachToHadith, prefers authentic hadith over local practice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approachToHadith Context triple: [Shafi'i school, approachToHadith, prefers authentic hadith over local practice]
-
A.
approaches
Indicates that one entity moves closer in position or state to another entity or reference point.
-
B.
hasApproachType
chosen
Indicates the specific method, strategy, or manner in which an action, process, or interaction is carried out or approached.
-
C.
quranicStatus
Indicates the status or classification of something in relation to its recognition, treatment, or role within the Quran.
-
D.
includesApproach
Indicates that one entity incorporates, utilizes, or is characterized by a particular method, strategy, or approach in relation to another entity or context.
-
E.
styleOfExegesis
Indicates the particular interpretive method or approach applied when analyzing or explaining a text.
- 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c175079481909077cf11ed72d6fa |
completed | March 1, 2026, 10:45 p.m. |
| PD | Predicate disambiguation | batch_69a4beebcb348190964bd7215811942c |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.