Triple
T16183539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanaa |
E392742
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Kajol
Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 1990s.
|
E1203564
|
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: Kajol | Statement: [Fanaa, starring, Kajol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kajol Context triple: [Fanaa, starring, Kajol]
-
A.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
B.
Karisma Kapoor
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
-
C.
Vidya Balan
Vidya Balan is an acclaimed Indian actress known for her powerful performances in Hindi cinema and for pioneering strong, female-led films in Bollywood.
-
D.
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."
-
E.
Rani Mukerji
Rani Mukerji is an acclaimed Indian film actress known for her versatile performances in numerous successful Hindi movies since the late 1990s.
- 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: Kajol Triple: [Fanaa, starring, Kajol]
Generated description
Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 1990s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kajol Target entity description: Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 1990s.
-
A.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
B.
Karisma Kapoor
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
-
C.
Vidya Balan
Vidya Balan is an acclaimed Indian actress known for her powerful performances in Hindi cinema and for pioneering strong, female-led films in Bollywood.
-
D.
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."
-
E.
Rani Mukerji
Rani Mukerji is an acclaimed Indian film actress known for her versatile performances in numerous successful Hindi movies since the late 1990s.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2205ef39081908da383abdebc2ccc |
completed | April 17, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017a7223c81909f04144bdffb22ff |
completed | May 10, 2026, 5:29 a.m. |
| NEDg | Description generation | batch_6a00195984c881909483fbf2afb518d1 |
completed | May 10, 2026, 5:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0019cbdc64819092184420bd4fd8ed |
completed | May 10, 2026, 5:38 a.m. |
Created at: April 10, 2026, 5:02 a.m.