Triple
T22526780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | عمر الشريف |
E556924
|
entity |
| Predicate | الأبناء |
P980
|
FINISHED |
| Object | طارق عمر الشريف |
—
|
NE NERFINISHED |
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: طارق عمر الشريف | Statement: [عمر الشريف, الأبناء, طارق عمر الشريف]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: طارق عمر الشريف Context triple: [عمر الشريف, الأبناء, طارق عمر الشريف]
-
A.
عمر الشريف
عمر الشريف هو ممثل مصري عالمي اشتهر بأدواره في أفلام مثل "لورنس العرب" و"دكتور زيفاجو" وأصبح من أبرز نجوم السينما العربية والدولية في القرن العشرين.
-
B.
Tamer Hassan
Tamer Hassan is a British actor known for his tough-guy roles in crime and gangster films.
-
C.
Kamal Elgargni
Kamal Elgargni is a Libyan professional bodybuilder best known for winning the 212 division title at the Mr. Olympia competition.
-
D.
Ramses Shaffy
Ramses Shaffy was a celebrated Dutch singer, chansonnier, and actor known for his emotional performances and influential role in Dutch popular music and theater in the 1960s and 1970s.
-
E.
Tarek Sharif
chosen
Tarek Sharif is the son of legendary Egyptian actors Omar Sharif and Faten Hamama.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed352b48190a96ef2896f2978cd |
completed | April 29, 2026, 1:28 a.m. |
Created at: April 16, 2026, 8:51 p.m.