Triple
T22658040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | فتحي سرور |
E559282
|
entity |
| Predicate | مجال التخصص الأكاديمي |
P778
|
FINISHED |
| Object | القانون الجنائي |
—
|
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: القانون الجنائي | Statement: [فتحي سرور, مجال التخصص الأكاديمي, القانون الجنائي]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: مجال التخصص الأكاديمي Context triple: [فتحي سرور, مجال التخصص الأكاديمي, القانون الجنائي]
-
A.
academicFocus
chosen
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
B.
housesAcademicDiscipline
Indicates that an entity serves as the location or institutional home where a particular academic discipline is based, organized, or conducted.
-
C.
regionOfAcademicFocus
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
-
D.
academicType
Indicates the specific academic category or classification associated with an entity (such as a work, program, or role).
-
E.
regionOfAcademicInterest
Indicates that an entity has a particular academic field or subject area as its focus of interest or study.
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765d10588190b4574f3e64617cd4 |
completed | April 29, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69ee6294c4c08190b7e4829f4b9af24b |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:07 p.m.