Triple
T14177440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vani Khosla |
E351368
|
entity |
| Predicate | notableFamilyMemberOf |
P367
|
FINISHED |
| Object | Nina Khosla |
E1088757
|
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: Nina Khosla | Statement: [Vani Khosla, notableFamilyMemberOf, Nina Khosla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nina Khosla Context triple: [Vani Khosla, notableFamilyMemberOf, Nina Khosla]
-
A.
Nina Khosla
chosen
Nina Khosla is a designer and entrepreneur known for her work at the intersection of technology, product design, and venture-backed startups.
-
B.
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.
-
C.
Mona Kapoor
Mona Kapoor was an Indian television and film producer best known as the first wife of Bollywood film producer Boney Kapoor and mother of actor Arjun Kapoor.
-
D.
Anu Khosla
Anu Khosla is one of the children of Indian-American billionaire venture capitalist and Sun Microsystems co-founder Vinod Khosla.
-
E.
Sarita Khurana
Sarita Khurana is a filmmaker and producer known for her work on culturally focused, immigrant-centered stories in film and television.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61c76e8081909994b95b631100e9 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c27b7088190bd7714391d0536b1 |
completed | May 8, 2026, 2:36 a.m. |
Created at: April 10, 2026, 1:02 a.m.