Triple
T22103313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Actor Prepares |
E546221
|
entity |
| Predicate | notableAlumni |
P51
|
FINISHED |
| Object | Preity Zinta |
—
|
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: Preity Zinta | Statement: [Actor Prepares, notableAlumni, Preity Zinta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Preity Zinta Context triple: [Actor Prepares, notableAlumni, Preity Zinta]
-
A.
Preity Zinta
chosen
Preity Zinta is an Indian film actress and entrepreneur best known for her work in Hindi cinema, including acclaimed performances in films like "Kal Ho Naa Ho," "Dil Chahta Hai," and "Veer-Zaara."
-
B.
Priya Dutt
Priya Dutt is an Indian politician and former Member of Parliament from Mumbai, known for her work with the Indian National Congress and as the daughter of actors-turned-politicians Sunil Dutt and Nargis.
-
C.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
D.
Pooja Bhatt
Pooja Bhatt is an Indian actress, filmmaker, and producer known for her work in Hindi cinema since the early 1990s.
-
E.
Kajol
Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.