Triple

T1129557
Position Surface form Disambiguated ID Type / Status
Subject Funny Girl E24796 entity
Predicate starring P1507 FINISHED
Object Omar Sharif E3718 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: Omar Sharif | Statement: [Funny Girl, starring, Omar Sharif]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Omar Sharif
Context triple: [Funny Girl, starring, Omar Sharif]
  • A. Omar Sharif chosen
    Omar Sharif was an acclaimed Egyptian actor known internationally for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
  • B. Peter O'Toole
    Peter O'Toole was an acclaimed Irish-English actor renowned for his charismatic screen presence and multiple Academy Award–nominated performances across film, stage, and television.
  • C. Richard Burton
    Richard Burton was a renowned Welsh actor celebrated for his powerful Shakespearean performances and intense screen presence in classic films of the mid-20th century.
  • D. Yul Brynner
    Yul Brynner was a Russian-born American actor best known for his charismatic, bald-headed performances in classic films and stage productions such as "The King and I."
  • E. David Niven
    David Niven was a distinguished English actor known for his suave, debonair screen presence and acclaimed roles in films such as "Around the World in 80 Days" and "Separate Tables."
  • 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_69a4940712c88190aa244f3fc6070a65 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bbf979108190adad7073c8275dd2 completed March 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf13304881908aa74d92ef7b1c86 completed March 8, 2026, 12:13 a.m.
Created at: March 1, 2026, 7:44 p.m.