Triple
T22433244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lootera |
E554548
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Dipika Kalra
Dipika Kalra is an Indian film editor known for her work on acclaimed Hindi films, including the period romance drama "Lootera."
|
E1536428
|
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: Dipika Kalra | Statement: [Lootera, editor, Dipika Kalra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dipika Kalra Context triple: [Lootera, editor, Dipika Kalra]
-
A.
Sandhini Agarwal
Sandhini Agarwal is an AI researcher known for her work at OpenAI on safety, policy, and the development and deployment of large-scale models such as CLIP.
-
B.
Bhavna Limbachia
Bhavna Limbachia is a British actress best known for her comedic and dramatic roles on UK television, including prominent parts in series such as Citizen Khan and Coronation Street.
-
C.
Vaishali Kasaravalli
Vaishali Kasaravalli was an Indian actress and costume designer known for her work in Kannada cinema and television.
-
D.
Aditi Yadav
Aditi Yadav is the daughter of Indian politician and former Uttar Pradesh Chief Minister Akhilesh Yadav.
-
E.
Surekha Sikri
Surekha Sikri was a renowned Indian theatre, film, and television actress celebrated for her powerful character roles and multiple National Film Awards.
- 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: Dipika Kalra Triple: [Lootera, editor, Dipika Kalra]
Generated description
Dipika Kalra is an Indian film editor known for her work on acclaimed Hindi films, including the period romance drama "Lootera."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dipika Kalra Target entity description: Dipika Kalra is an Indian film editor known for her work on acclaimed Hindi films, including the period romance drama "Lootera."
-
A.
Sandhini Agarwal
Sandhini Agarwal is an AI researcher known for her work at OpenAI on safety, policy, and the development and deployment of large-scale models such as CLIP.
-
B.
Bhavna Limbachia
Bhavna Limbachia is a British actress best known for her comedic and dramatic roles on UK television, including prominent parts in series such as Citizen Khan and Coronation Street.
-
C.
Vaishali Kasaravalli
Vaishali Kasaravalli was an Indian actress and costume designer known for her work in Kannada cinema and television.
-
D.
Aditi Yadav
Aditi Yadav is the daughter of Indian politician and former Uttar Pradesh Chief Minister Akhilesh Yadav.
-
E.
Surekha Sikri
Surekha Sikri was a renowned Indian theatre, film, and television actress celebrated for her powerful character roles and multiple National Film Awards.
- 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a3320448190ae3931062599116e |
completed | April 29, 2026, 1:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0af0f90f5881908f0011690cb0f1e4 |
completed | May 18, 2026, 10:59 a.m. |
| NEDg | Description generation | batch_6a0af268db2881908706641ad1e41f79 |
completed | May 18, 2026, 11:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b08e411048190a368214c57c35d5e |
completed | May 18, 2026, 12:41 p.m. |
Created at: April 16, 2026, 8:47 p.m.