Triple

T11203176
Position Surface form Disambiguated ID Type / Status
Subject Bend It Like Beckham E265091 entity
Predicate starring P1507 FINISHED
Object Archie Panjabi E338847 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: Archie Panjabi | Statement: [Bend It Like Beckham, starring, Archie Panjabi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Archie Panjabi
Context triple: [Bend It Like Beckham, starring, Archie Panjabi]
  • A. Archie Panjabi chosen
    Archie Panjabi is a British actress best known for her acclaimed role as Kalinda Sharma on the television series "The Good Wife."
  • B. Baljeet Tjinder
    Baljeet Tjinder is a studious, academically driven boy in the animated series "Phineas and Ferb," known for his intelligence, anxiety about grades, and frequent involvement in the protagonists’ inventions and adventures.
  • C. Jay Chaudhry
    Jay Chaudhry is an Indian-American entrepreneur and billionaire best known as the founder and CEO of the cloud security company Zscaler.
  • D. Naveen Andrews
    Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
  • E. Peter Saraf
    Peter Saraf is an American film producer known for his work on acclaimed independent and mainstream films, including "A Beautiful Day in the Neighborhood."
  • 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_69d6aa9eb9248190b20211772621b4bc completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8d355c481908fc3d555b596314d completed April 9, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4972bfbd481908cd0da59389ae17c completed April 19, 2026, 8:49 a.m.
Created at: April 8, 2026, 9:29 p.m.