Triple
T16272746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dil Chahta Hai |
E395041
|
entity |
| Predicate | leadActress |
P6108
|
FINISHED |
| Object | Preity Zinta |
E850371
|
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: Preity Zinta | Statement: [Dil Chahta Hai, leadActress, Preity Zinta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Preity Zinta Context triple: [Dil Chahta Hai, leadActress, 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.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
C.
Kajol
Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 1990s.
-
D.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
E.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2460b22d88190bdc7cf509cf74198 |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00580b94c88190a3330791e505fd6a |
completed | May 10, 2026, 10:03 a.m. |
Created at: April 10, 2026, 5:05 a.m.