Triple
T25746527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | وزارة الطيران المدني المصرية |
E648359
|
entity |
| Predicate | قطاع |
P71
|
FINISHED |
| Object | الطيران المدني |
—
|
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: الطيران المدني | Statement: [وزارة الطيران المدني المصرية, قطاع, الطيران المدني]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: قطاع Context triple: [وزارة الطيران المدني المصرية, قطاع, الطيران المدني]
-
A.
missionRegion
Indicates the geographic or administrative region in which a mission is carried out or assigned.
-
B.
province
Indicates that one entity is an administrative subdivision or region (a province) governed by or belonging to another, typically larger, political or territorial entity.
-
C.
sector
chosen
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
D.
region1
Indicates that one entity is the first or primary region associated with, containing, or encompassing another entity.
-
E.
regionSub
Indicates that one region is a subregion or contained part of another, larger region.
- 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.