Triple
T10212872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dil Se.. |
E242373
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Preity Zinta
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."
|
E850371
|
NE FINISHED |
How this triple was built (4 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 Se.., starring, Preity Zinta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Preity Zinta Context triple: [Dil Se.., starring, Preity Zinta]
-
A.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
B.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
C.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
D.
Neena Gupta
Neena Gupta is an acclaimed Indian film, television, and theatre actress and director known for her versatile performances across parallel and mainstream cinema.
-
E.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Preity Zinta Triple: [Dil Se.., starring, Preity Zinta]
Generated description
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."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Preity Zinta Target entity description: 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."
-
A.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
B.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
C.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
D.
Neena Gupta
Neena Gupta is an acclaimed Indian film, television, and theatre actress and director known for her versatile performances across parallel and mainstream cinema.
-
E.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
- F. None of above. chosen
Provenance (5 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa23bce881909b5deac612ec22cb |
completed | April 6, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a7f6730081908b941eaeb6c00993 |
completed | April 8, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69d6ad94a6a881908d4c3b4408695d5b |
completed | April 8, 2026, 7:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6d02015bc8190a7041a7d725c8a1b |
completed | April 8, 2026, 10:01 p.m. |
Created at: April 6, 2026, 11:03 a.m.