Triple
T21944129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hindi Medium |
E541892
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Zeenat Lakhani
Zeenat Lakhani is an Indian screenwriter best known for co-writing the acclaimed Hindi film "Hindi Medium."
|
E1511768
|
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: Zeenat Lakhani | Statement: [Hindi Medium, writer, Zeenat Lakhani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zeenat Lakhani Context triple: [Hindi Medium, writer, Zeenat Lakhani]
-
A.
Neelima Azeem
Neelima Azeem is an Indian actress and classical Kathak dancer known for her work in television, film, and theatre, as well as being the mother of Bollywood actor Shahid Kapoor.
-
B.
Ayesha Jhulka
Ayesha Jhulka is an Indian film actress best known for her popular roles in early 1990s Bollywood cinema.
-
C.
Soni Razdan
Soni Razdan is a British-born Indian actress and filmmaker known for her work in Hindi cinema and television, as well as for being the mother of actress Alia Bhatt.
-
D.
Meghna Kapoor
Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
-
E.
Kavita Rao
Kavita Rao is a fictional geneticist in the X-Men universe known for developing a controversial "cure" for mutant powers.
- 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: Zeenat Lakhani Triple: [Hindi Medium, writer, Zeenat Lakhani]
Generated description
Zeenat Lakhani is an Indian screenwriter best known for co-writing the acclaimed Hindi film "Hindi Medium."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zeenat Lakhani Target entity description: Zeenat Lakhani is an Indian screenwriter best known for co-writing the acclaimed Hindi film "Hindi Medium."
-
A.
Neelima Azeem
Neelima Azeem is an Indian actress and classical Kathak dancer known for her work in television, film, and theatre, as well as being the mother of Bollywood actor Shahid Kapoor.
-
B.
Ayesha Jhulka
Ayesha Jhulka is an Indian film actress best known for her popular roles in early 1990s Bollywood cinema.
-
C.
Soni Razdan
Soni Razdan is a British-born Indian actress and filmmaker known for her work in Hindi cinema and television, as well as for being the mother of actress Alia Bhatt.
-
D.
Meghna Kapoor
Meghna Kapoor is known as the wife of Indian actor and filmmaker Rajat Kapoor.
-
E.
Kavita Rao
Kavita Rao is a fictional geneticist in the X-Men universe known for developing a controversial "cure" for mutant powers.
- 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_69f1242515ec8190b015bf8c7b13be85 |
completed | April 28, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a6d6b79808190ba87aedd1ada86e3 |
completed | May 18, 2026, 1:37 a.m. |
| NEDg | Description generation | batch_6a0a6df4b3908190800df70ea35c87d5 |
completed | May 18, 2026, 1:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a6e787abc8190b192770e4d3e8daa |
completed | May 18, 2026, 1:42 a.m. |
Created at: April 16, 2026, 7:56 p.m.