Triple

T11535397
Position Surface form Disambiguated ID Type / Status
Subject Randhir Kapoor E273533 entity
Predicate sibling P363 FINISHED
Object Ritu Nanda E905441 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: Ritu Nanda | Statement: [Randhir Kapoor, sibling, Ritu Nanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ritu Nanda
Context triple: [Randhir Kapoor, sibling, Ritu Nanda]
  • A. Ritu Nanda chosen
    Ritu Nanda was an Indian businesswoman and insurance advisor, best known as a member of the prominent Kapoor film family and for setting industry records in the insurance sector.
  • B. Gita Sen
    Gita Sen is an Indian actress known for her frequent collaborations with her husband, acclaimed filmmaker Mrinal Sen, in Bengali parallel cinema.
  • C. Nandita Puri
    Nandita Puri is an Indian journalist and author best known for her biography of her late husband, acclaimed actor Om Puri.
  • D. Babita Kapoor
    Babita Kapoor is an Indian former actress and member of the prominent Kapoor film family, known for her work in Hindi cinema of the late 1960s and 1970s.
  • E. Suchitra Sen
    Suchitra Sen was a legendary Indian film actress renowned for her powerful performances in Bengali cinema and as the first Indian actress to receive an international film award.
  • 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_69d6aae3fbec8190a14632a5df2538b6 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8839b4bb48190b748ec4119f36c11 completed April 10, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6e7fe305c8190bbf981b2c0e63983 completed April 21, 2026, 2:59 a.m.
Created at: April 8, 2026, 9:37 p.m.