Triple
T21944907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chandni Bar |
E541909
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Potya Sawant
Potya Sawant is a fictional character from the acclaimed Indian crime drama film "Chandni Bar," which explores the gritty underworld of Mumbai's dance bars and organized crime.
|
E1525567
|
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: Potya Sawant | Statement: [Chandni Bar, character, Potya Sawant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Potya Sawant Context triple: [Chandni Bar, character, Potya Sawant]
-
A.
Meghna Kapoor
Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
-
B.
Neha Dhupia
Neha Dhupia is an Indian actress and former Miss India who has appeared in numerous Bollywood films and television shows.
-
C.
Aditi Pandit
Aditi Pandit is a fictional character from the Indian television series "Astitva...Ek Prem Kahani."
-
D.
Sanya Malhotra
Sanya Malhotra is an Indian actress known for her acclaimed debut in the film "Dangal" and subsequent roles in Hindi cinema.
-
E.
Divya Katdare
Divya Katdare is a central character on the television series "Royal Pains," known as a skilled and poised physician assistant who works closely with concierge doctor Hank Lawson in the Hamptons.
- 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: Potya Sawant Triple: [Chandni Bar, character, Potya Sawant]
Generated description
Potya Sawant is a fictional character from the acclaimed Indian crime drama film "Chandni Bar," which explores the gritty underworld of Mumbai's dance bars and organized crime.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Potya Sawant Target entity description: Potya Sawant is a fictional character from the acclaimed Indian crime drama film "Chandni Bar," which explores the gritty underworld of Mumbai's dance bars and organized crime.
-
A.
Meghna Kapoor
Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
-
B.
Neha Dhupia
Neha Dhupia is an Indian actress and former Miss India who has appeared in numerous Bollywood films and television shows.
-
C.
Aditi Pandit
Aditi Pandit is a fictional character from the Indian television series "Astitva...Ek Prem Kahani."
-
D.
Sanya Malhotra
Sanya Malhotra is an Indian actress known for her acclaimed debut in the film "Dangal" and subsequent roles in Hindi cinema.
-
E.
Divya Katdare
Divya Katdare is a central character on the television series "Royal Pains," known as a skilled and poised physician assistant who works closely with concierge doctor Hank Lawson in the Hamptons.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0aae57ea5c81909003d0c6da529fbf |
completed | May 18, 2026, 6:14 a.m. |
| NEDg | Description generation | batch_6a0aaf0f47948190b602d1016badc21c |
completed | May 18, 2026, 6:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ab0befa5481909d00cb845dd67b12 |
completed | May 18, 2026, 6:25 a.m. |
Created at: April 16, 2026, 7:56 p.m.