Triple
T30905037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | بدر الدين العيني |
E787275
|
entity |
| Predicate | تخصص_في |
P140458
|
FINISHED |
| Object | شرح كتب الحديث المعتمدة عند أهل السنة |
—
|
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: شرح كتب الحديث المعتمدة عند أهل السنة | Statement: [بدر الدين العيني, تخصص_في, شرح كتب الحديث المعتمدة عند أهل السنة]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: تخصص_في Context triple: [بدر الدين العيني, تخصص_في, شرح كتب الحديث المعتمدة عند أهل السنة]
-
A.
subjectSpecialization
chosen
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
-
B.
laterSpecializedIn
Indicates that an entity initially engaged in a broader or different field and subsequently focused its work or expertise in a more specific or specialized area.
-
C.
الاختصاص الرئيسي
Indicates that one entity is the primary field of specialization or main area of focus for another entity.
-
D.
technicalSpecialty
Indicates that an entity has expertise or specialized knowledge in a particular technical field or domain.
-
E.
isConsideredSpecialtyOf
Indicates that one field, practice, or area of expertise is regarded as a specialized branch or subset of another broader field.
- 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_69f224bcbcb48190836df847424e4057 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6927dd38081909f32b60565283795 |
completed | May 3, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69f68b7ec098819080480998038de940 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:50 p.m.