Triple
T10306225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | al-Sunan al-Kubra |
E241767
|
entity |
| Predicate | schoolOfLawContext |
P78579
|
FINISHED |
| Object | Shafiʿi jurisprudence |
—
|
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: Shafiʿi jurisprudence | Statement: [al-Sunan al-Kubra, schoolOfLawContext, Shafiʿi jurisprudence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolOfLawContext Context triple: [al-Sunan al-Kubra, schoolOfLawContext, Shafiʿi jurisprudence]
-
A.
schoolOfJurisprudence
chosen
Indicates that one entity is a legal philosophy, doctrine, or interpretive framework to which the other entity (such as a jurist, decision, or institution) adheres or belongs.
-
B.
associatedSchoolOfLaw
Indicates a relationship where an entity is connected or linked to a particular school of law, typically as its legal education institution or legal academic affiliation.
-
C.
majorSchoolOfLaw
Indicates that a particular school of law is a primary or dominant legal tradition or framework associated with an entity.
-
D.
lawSchoolName
Indicates the name of the law school with which an entity (such as a person or institution) is associated.
-
E.
legalSchoolFoundedIn
Indicates that a law school was established or came into existence in a specific year or time period.
- 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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f4f354819080b4ed4bc61bdff6 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:46 a.m.