Triple
T20170818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashok Kumar |
E491951
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Bharti Jaffrey |
—
|
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: Bharti Jaffrey | Statement: [Ashok Kumar, child, Bharti Jaffrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bharti Jaffrey Context triple: [Ashok Kumar, child, Bharti Jaffrey]
-
A.
Madhur Jaffrey
Madhur Jaffrey is an Indian-born actress and influential food and travel writer widely credited with popularizing Indian cuisine in the Western world.
-
B.
Sakina Jaffrey
chosen
Sakina Jaffrey is an American actress known for her roles in television series such as House of Cards, Timeless, and Billions, as well as numerous film and stage appearances.
-
C.
Manjula Ghattamaneni
Manjula Ghattamaneni is an Indian film producer and actress primarily associated with Telugu cinema and a member of the prominent Ghattamaneni film family.
-
D.
Nina Khosla
Nina Khosla is a designer and entrepreneur known for her work at the intersection of technology, product design, and venture-backed startups.
-
E.
Devi Parikh
Devi Parikh is a computer vision and AI researcher known for her work on visual question answering, human-AI collaboration, and interpretable machine learning.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66847ed9481908e6b23b399fa7005 |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:35 p.m.