Triple
T20081084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Modern School, New Delhi |
E500000
|
entity |
| Predicate | alumni |
P51
|
FINISHED |
| Object | Nandita Das |
—
|
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: Nandita Das | Statement: [Modern School, New Delhi, alumni, Nandita Das]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nandita Das Context triple: [Modern School, New Delhi, alumni, Nandita Das]
-
A.
Nandita Das
chosen
Nandita Das is an acclaimed Indian actress and filmmaker known for her powerful performances in parallel cinema and her socially conscious directorial work, including the films "Firaaq" and "Manto."
-
B.
Nandana Sen
Nandana Sen is an Indian actress, writer, and child-rights activist known for her work in international and Bollywood films as well as her advocacy for children's welfare.
-
C.
Raima Sen
Raima Sen is an Indian film and television actress known for her work in Bengali and Hindi cinema and for being part of the prominent Sen acting family.
-
D.
Nandita Puri
Nandita Puri is an Indian journalist and author best known for her biography of her late husband, acclaimed actor Om Puri.
-
E.
Priya Basu
Priya Basu is an economist and development finance expert known for her work on financial inclusion and policy at institutions such as the World Bank.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66557c19c8190b511857490bbd423 |
completed | April 20, 2026, 5:41 p.m. |
Created at: April 11, 2026, 3:41 p.m.