Triple

T22658186
Position Surface form Disambiguated ID Type / Status
Subject رشدي أباظة E559286 entity
Predicate workedWith P398 FINISHED
Object نادية لطفي 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: نادية لطفي | Statement: [رشدي أباظة, workedWith, نادية لطفي]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: نادية لطفي
Context triple: [رشدي أباظة, workedWith, نادية لطفي]
  • A. Nadia Lotfy chosen
    Nadia Lotfy was a prominent Egyptian film actress, best known for her roles in classic Egyptian cinema of the 1950s and 1960s.
  • B. Hani Nassar
    Hani Nassar is an individual notable enough to be specifically cited as a bearer of the surname Nassar.
  • C. Shereen Reda
    Shereen Reda is an Egyptian actress and public figure known for her roles in film and television as well as her high-profile presence in Arab media.
  • D. Nayel Nassar
    Nayel Nassar is an Egyptian-American professional show jumping rider and businessman known for competing internationally and for being married to Jennifer Gates, the daughter of Bill and Melinda Gates.
  • E. Nabila Ebeid
    Nabila Ebeid is a prominent Egyptian film and television actress known for her leading roles in Arabic cinema since the 1960s.
  • 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_69e245489dd88190b1f674acf61c8769 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1765d10588190b4574f3e64617cd4 completed April 29, 2026, 3:09 a.m.
Created at: April 17, 2026, 3:07 p.m.