Triple
T25330084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sahnun ibn Saʿid |
E635124
|
entity |
| Predicate | legalSchoolStatus |
P165304
|
FINISHED |
| Object | major authority in Maliki madhhab |
—
|
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: major authority in Maliki madhhab | Statement: [Sahnun ibn Saʿid, legalSchoolStatus, major authority in Maliki madhhab]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalSchoolStatus Context triple: [Sahnun ibn Saʿid, legalSchoolStatus, major authority in Maliki madhhab]
-
A.
legalSchoolFor
Indicates that one entity is an educational institution recognized or designated as a law school for another entity (such as a person, jurisdiction, or program).
-
B.
juridicalSchoolStatus
Indicates the legal or institutional status assigned to a school within a given jurisdiction or educational system.
-
C.
lawSchoolAccreditation
Indicates that a law school has been formally evaluated and recognized as meeting established educational and professional standards by an accrediting authority.
-
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. 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_69e75a9908108190a95427a97020632a |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f658a91ba0819084fbe3dd8a09f7cd |
completed | May 2, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69f6575ba12081909396036f78757a76 |
completed | May 2, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f657f2c8b08190bfeb3173ef78207d |
completed | May 2, 2026, 8 p.m. |
Created at: April 21, 2026, 1:30 p.m.