Triple
T25288573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seljuk court in Konya |
E634014
|
entity |
| Predicate | followedSchoolOfLaw |
P62874
|
FINISHED |
| Object | Hanafi school |
—
|
NE NERFINISHED |
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: Hanafi school | Statement: [Seljuk court in Konya, followedSchoolOfLaw, Hanafi school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followedSchoolOfLaw Context triple: [Seljuk court in Konya, followedSchoolOfLaw, Hanafi school]
-
A.
associatedSchoolOfLaw
chosen
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.
-
B.
studiedLawIn
Indicates that a person received legal education or training at a particular institution or location.
-
C.
studiedLawBy
Indicates that one entity pursued or received legal education under the instruction, supervision, or at the institution represented by the other entity.
-
D.
majorSchoolOfLaw
Indicates that a particular school of law is a primary or dominant legal tradition or framework associated with an entity.
-
E.
lawSchoolName
Indicates the name of the law school with which an entity (such as a person or institution) is associated.
- 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_69e75a9402fc81909362ca85277c06d9 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6456608190b94e7c2e2c2a4824 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 21, 2026, 1:19 p.m.