Triple

T20229938
Position Surface form Disambiguated ID Type / Status
Subject Dimple Yadav E495487 entity
Predicate name P16 FINISHED
Object Dimple Yadav 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: Dimple Yadav | Statement: [Dimple Yadav, name, Dimple Yadav]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dimple Yadav
Context triple: [Dimple Yadav, name, Dimple Yadav]
  • A. Dimple Yadav chosen
    Dimple Yadav is an Indian politician and Member of Parliament associated with the Samajwadi Party, known for being the wife of former Uttar Pradesh Chief Minister Akhilesh Yadav and for her electoral prominence from Uttar Pradesh.
  • B. Anita Yadav
    Anita Yadav is known as the wife of Ram Baran Yadav, the first President of Nepal.
  • C. Anjana Patidar
    Anjana Patidar is a community traditionally associated with the Charotar region of Gujarat, India, known for its agrarian roots and Patidar (Patel) social identity.
  • D. Neera Shastri
    Neera Shastri is an Indian politician and social worker associated with the Bharatiya Janata Party and known for her work in public service and women’s empowerment.
  • E. Renu Saluja
    Renu Saluja was a renowned Indian film editor celebrated for her influential work in parallel and mainstream Hindi cinema during the 1980s and 1990s.
  • 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_69da626cff80819097b530718a7c98b6 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66fdc2590819089a946d16c6c0e59 completed April 20, 2026, 6:26 p.m.
Created at: April 11, 2026, 11:39 p.m.