Triple
T25746536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | وزارة الطيران المدني المصرية |
E648359
|
entity |
| Predicate | مجال الاختصاص |
P57114
|
FINISHED |
| Object | المجال الجوي المصري |
—
|
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: المجال الجوي المصري | Statement: [وزارة الطيران المدني المصرية, مجال الاختصاص, المجال الجوي المصري]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: مجال الاختصاص Context triple: [وزارة الطيران المدني المصرية, مجال الاختصاص, المجال الجوي المصري]
-
A.
regionOfPractice
chosen
Indicates the geographic area or jurisdiction in which an entity regularly conducts its professional activities or services.
-
B.
disciplinaryFocus
Indicates the primary academic or professional field, subject area, or discipline that something is centered on or concerned with.
-
C.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
D.
governingDiscipline
Indicates that one discipline or field provides the primary rules, principles, or framework that regulate or guide another activity, domain, or practice.
-
E.
subjectSpecialization
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
- 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_69e7ab306eec8190b05c312c6ab186b8 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f5fd1fb8d88190a03c705fecf22634 |
completed | May 2, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69f4938262ac8190b41f922d0407d272 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 3:51 a.m.