Triple

T16387712
Position Surface form Disambiguated ID Type / Status
Subject Azad Rao Khan E397965 entity
Predicate mother P120 FINISHED
Object Kiran Rao E392746 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: Kiran Rao | Statement: [Azad Rao Khan, mother, Kiran Rao]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kiran Rao
Context triple: [Azad Rao Khan, mother, Kiran Rao]
  • A. Kiran Rao chosen
    Kiran Rao is an Indian film producer, director, and screenwriter known for her work in Hindi cinema and for co-producing acclaimed films such as "Dhobi Ghat" and "Delhi Belly."
  • B. Surekha Konidela
    Surekha Konidela is an Indian film producer and member of the prominent Allu–Konidela family in the Telugu cinema industry.
  • C. Ameesha Patel
    Ameesha Patel is an Indian actress and model best known for her roles in popular Bollywood films such as "Kaho Naa... Pyaar Hai" and "Gadar: Ek Prem Katha."
  • D. Samantha Akkineni
    Samantha Akkineni is a prominent Indian actress known for her acclaimed performances in Telugu and Tamil cinema, as well as her work in several successful pan-Indian projects.
  • E. Ramya Krishnan
    Ramya Krishnan is an acclaimed Indian actress known for her powerful and versatile performances across Tamil, Telugu, and other South Indian film industries.
  • 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_69d87f2880b48190ae1a9673a3bbef80 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e3263e1534819081a6bf5006c611c5 completed April 18, 2026, 6:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00758847948190b616cc85e208ee61 completed May 10, 2026, 12:09 p.m.
Created at: April 10, 2026, 5:08 a.m.