Triple
T25814482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | الجامعة الإسلامية بالمدينة المنورة |
E650210
|
entity |
| Predicate | التخصص |
P140458
|
FINISHED |
| Object | العلوم العربية |
—
|
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: العلوم العربية | Statement: [الجامعة الإسلامية بالمدينة المنورة, التخصص, العلوم العربية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: التخصص Context triple: [الجامعة الإسلامية بالمدينة المنورة, التخصص, العلوم العربية]
-
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.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
D.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
E.
labelSpecialization
Indicates that one label is a more specific or specialized version of another label within a labeling or classification system.
- 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_69e7ab35d264819095367f7e80c983ff |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f600c84ac4819091492e52a5a8b873 |
completed | May 2, 2026, 1:48 p.m. |
| PD | Predicate disambiguation | batch_69f4a0fed15881909b789251fe5d8d45 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 22, 2026, 7:12 a.m.