Triple
T22526263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | زكريا عزمي |
E556911
|
entity |
| Predicate | ظهر اسمه في |
P55029
|
FINISHED |
| Object | وسائل الإعلام المصرية بعد ثورة 25 يناير بسبب قضايا الفساد |
—
|
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: وسائل الإعلام المصرية بعد ثورة 25 يناير بسبب قضايا الفساد | Statement: [زكريا عزمي, ظهر اسمه في, وسائل الإعلام المصرية بعد ثورة 25 يناير بسبب قضايا الفساد]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ظهر اسمه في Context triple: [زكريا عزمي, ظهر اسمه في, وسائل الإعلام المصرية بعد ثورة 25 يناير بسبب قضايا الفساد]
-
A.
appearedWith
Indicates that two entities were present or participated together in the same context, event, or medium.
-
B.
notableAppearanceIn
chosen
Indicates that an entity is prominently featured or plays a significant role in a particular work, event, or context.
-
C.
hasNotableMultipleAppearances
Indicates that an entity appears multiple times in a context or medium in a way considered significant or noteworthy.
-
D.
appearedAt
Indicates that an entity was present or made an appearance at a specific event, location, or occasion.
-
E.
firstNamedAppearance
Indicates the point or context in which an entity is first explicitly named or mentioned.
- 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_69e11e5657e881909f16ca58352c50da |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed352b48190a96ef2896f2978cd |
completed | April 29, 2026, 1:28 a.m. |
| PD | Predicate disambiguation | batch_69ee625e3b408190a60c759fb0b28fe2 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 16, 2026, 8:51 p.m.