Triple

T6057458
Position Surface form Disambiguated ID Type / Status
Subject Omar Sharif E134949 entity
Predicate child P120 FINISHED
Object Tarek Sharif E134949 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. Tarek Sharif chosen
    Tarek Sharif is the son of legendary Egyptian actors Omar Sharif and Faten Hamama.
  • B. Alexander Siddig
    Alexander Siddig is a Sudanese-born British actor known for his roles in film and television, including prominent performances in "Star Trek: Deep Space Nine," "Syriana," and "Game of Thrones."
  • C. Nabil Shaath
    Nabil Shaath is a prominent Palestinian politician and diplomat who has held senior roles in the Palestinian Authority, including as foreign minister and negotiator in peace talks with Israel.
  • D. Nadim Sawalha
    Nadim Sawalha is a Jordanian-British actor known for his character roles in film and television, including appearances in James Bond movies and various British dramas.
  • E. Adel Emam
    Adel Emam is a legendary Egyptian actor and comedian widely regarded as one of the most influential and beloved stars in the Arab world's film and television industry.
  • 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_69c00877b6d4819096b0e163728b73a3 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0570d00e88190b2d8d596e40378d9 completed March 22, 2026, 8:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11d0e06288190b4389b43825d5929 completed March 23, 2026, 10:59 a.m.
Created at: March 22, 2026, 4:09 p.m.