Triple
T190932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omar Sharif |
E3718
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Tarek Sharif |
E24444
|
NE 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: Tarek Sharif | Statement: [Omar Sharif, child, Tarek Sharif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarek Sharif Context triple: [Omar Sharif, child, Tarek Sharif]
-
A.
Mostafa Madbouly
Mostafa Madbouly is an Egyptian politician who has served as the Prime Minister of Egypt, overseeing the country's executive government.
-
B.
Nabil Elderkin
Nabil Elderkin is an acclaimed photographer and music video director known for his visually striking work with artists such as Kanye West, Frank Ocean, and John Legend.
-
C.
Nazlet El-Semman
Nazlet El-Semman is a village on the outskirts of Giza in Egypt, best known as the primary gateway settlement to the Giza Pyramids and Sphinx plateau.
-
D.
Tariq Anwar
Tariq Anwar is a British film editor known for his acclaimed work on numerous major films, including the Academy Award–winning drama "The King’s Speech."
-
E.
Faten Hamama
chosen
Faten Hamama was a renowned Egyptian film actress often hailed as the "Lady of the Arabic Screen" and a central figure in the golden age of Egyptian cinema.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a25964fc5c8190bd3e37daaf695ecf |
completed | Feb. 28, 2026, 2:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3115a91148190b554ca5fe372569c |
completed | Feb. 28, 2026, 4:01 p.m. |
Created at: Feb. 28, 2026, 2:41 a.m.