Triple
T13652572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nandan Nilekani |
E326775
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Nihar Nilekani |
E1020187
|
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: Nihar Nilekani | Statement: [Nandan Nilekani, hasChild, Nihar Nilekani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nihar Nilekani Context triple: [Nandan Nilekani, hasChild, Nihar Nilekani]
-
A.
Nihar Nilekani
chosen
Nihar Nilekani is the son of Indian entrepreneur and Infosys co-founder Nandan Nilekani.
-
B.
Prashant Damle
Prashant Damle is a renowned Indian actor and comedian celebrated for his prolific work in Marathi theatre, television, and films.
-
C.
Vas Narasimhan
Vas Narasimhan is an American physician-executive known for leading major strategic and innovation-driven transformations in the global pharmaceutical industry.
-
D.
Rohini Nilekani
Rohini Nilekani is an Indian philanthropist, author, and social activist known for founding and supporting several major initiatives in education, water, and civic engagement.
-
E.
Abhijit Vinayak Banerjee
Abhijit Vinayak Banerjee is an Indian-American economist and Nobel laureate renowned for his experimental approach to alleviating global poverty.
- 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_69d8076d8270819092afc2f0e9c359a8 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc609676c8190b5b1cabe6b315142 |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7943610488190838719ad31207c52 |
completed | May 3, 2026, 6:30 p.m. |
Created at: April 9, 2026, 9:52 p.m.