Triple

T7544130
Position Surface form Disambiguated ID Type / Status
Subject Nassar E178352 entity
Predicate hasNotableBearer P458 FINISHED
Object Maher Nassar E178352 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: Maher Nassar | Statement: [Nassar, hasNotableBearer, Maher Nassar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maher Nassar
Context triple: [Nassar, hasNotableBearer, Maher Nassar]
  • A. Nassar chosen
    Nassar is a surname most prominently associated with Egyptian-American show jumping rider Nayel Nassar.
  • B. 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.
  • C. Michael Ansara
    Michael Ansara was a Syrian-American character actor best known for his deep voice and frequent roles in Westerns and science fiction, including memorable appearances in series like Star Trek and Babylon 5.
  • D. Tarek Sharif
    Tarek Sharif is the son of legendary Egyptian actors Omar Sharif and Faten Hamama.
  • E. Faten Hamama
    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_69c69f2be3888190a6667a27f8f195e9 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f896a27481908b2e120208f268e7 completed March 27, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84f21aa5c819085e8d1ecbd9b01e3 completed March 28, 2026, 9:58 p.m.
Created at: March 27, 2026, 3:48 p.m.