Triple

T19839419
Position Surface form Disambiguated ID Type / Status
Subject Younes E476684 entity
Predicate hasTransliterationVariant P5923 FINISHED
Object Younus 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: Younus | Statement: [Younes, hasTransliterationVariant, Younus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Younus
Context triple: [Younes, hasTransliterationVariant, Younus]
  • A. Younus chosen
    Younus is a male given name of Arabic origin, commonly associated with the Quranic and Biblical figure Jonah.
  • B. Hasnat Khan
    Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
  • C. Mohammed Aamir Hussain Khan
    Mohammed Aamir Hussain Khan, better known as Aamir Khan, is a highly acclaimed Indian film actor, producer, and director renowned for his influential roles and socially conscious, commercially successful movies in Bollywood.
  • D. Waqar Younis
    Waqar Younis is a legendary Pakistani fast bowler renowned for his devastating reverse swing and prolific wicket-taking, particularly in one-day internationals.
  • E. Hafeez
    Hafeez is a male given name of Arabic origin, commonly used in South Asia and the Muslim world, meaning "guardian" or "protector."
  • 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_69d8e51d39d081909bcfafeaaf3d2fcc completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65804be608190b49e110c3bf381bc completed April 20, 2026, 4:44 p.m.
Created at: April 10, 2026, 1:50 p.m.