Triple

T11289271
Position Surface form Disambiguated ID Type / Status
Subject Shashi Kapoor E267281 entity
Predicate spouse P13 FINISHED
Object Jennifer Kendal E841614 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: Jennifer Kendal | Statement: [Shashi Kapoor, spouse, Jennifer Kendal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jennifer Kendal
Context triple: [Shashi Kapoor, spouse, Jennifer Kendal]
  • A. Jennifer Kendal chosen
    Jennifer Kendal was a British-Indian actress known for her work in Indian theatre and art-house cinema, particularly through her collaborations with director James Ivory and the Shakespeareana theatre company she ran with her husband Shashi Kapoor.
  • B. Liz Kendall
    Liz Kendall is a British Labour Party politician and Member of Parliament known for her centrist, Blairite positions within the party.
  • C. Annette Kaye
    Annette Kaye is known as one of Larry King's former wives, with whom he reportedly had a brief marriage and a son.
  • D. Renée Asherson
    Renée Asherson was a British stage and film actress known for her delicate, expressive performances in mid-20th-century British cinema and theatre.
  • E. Annette Badland
    Annette Badland is a British character actress known for her distinctive presence in film, television, and radio, with roles ranging from gritty dramas to popular series like "Doctor Who" and "Outlander."
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e98875a08190b8509fe55e49d52d completed April 9, 2026, 6:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69e603a1acc08190816db1ff13708e79 completed April 20, 2026, 10:44 a.m.
Created at: April 8, 2026, 9:32 p.m.