Triple
T22103044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special 26 |
E546216
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Shital Bhatia
Shital Bhatia is an Indian film producer known for backing acclaimed Hindi movies, including the heist thriller "Special 26."
|
E1561323
|
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: Shital Bhatia | Statement: [Special 26, producer, Shital Bhatia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shital Bhatia Context triple: [Special 26, producer, Shital Bhatia]
-
A.
Deepa Bhatia
Deepa Bhatia is an Indian film editor known for her acclaimed work on several Hindi films, including the critically celebrated drama "Taare Zameen Par."
-
B.
Persis Khambatta
Persis Khambatta was an Indian model and actress best known internationally for her role as Lieutenant Ilia in "Star Trek: The Motion Picture."
-
C.
Tarika Bansal
Tarika Bansal is the ambitious daughter of the protagonist in the Hindi film "Angrezi Medium," whose dream of studying abroad drives the emotional core of the story.
-
D.
Bhavani Choudhry
Bhavani Choudhry is a fictional character from the 1994 Hindi action drama film "Vijaypath."
-
E.
Sharan Narang
Sharan Narang is a machine learning researcher known for his work on large-scale natural language processing models, including contributions to the development of the T5 transformer architecture.
- 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: Shital Bhatia Triple: [Special 26, producer, Shital Bhatia]
Generated description
Shital Bhatia is an Indian film producer known for backing acclaimed Hindi movies, including the heist thriller "Special 26."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shital Bhatia Target entity description: Shital Bhatia is an Indian film producer known for backing acclaimed Hindi movies, including the heist thriller "Special 26."
-
A.
Deepa Bhatia
Deepa Bhatia is an Indian film editor known for her acclaimed work on several Hindi films, including the critically celebrated drama "Taare Zameen Par."
-
B.
Persis Khambatta
Persis Khambatta was an Indian model and actress best known internationally for her role as Lieutenant Ilia in "Star Trek: The Motion Picture."
-
C.
Tarika Bansal
Tarika Bansal is the ambitious daughter of the protagonist in the Hindi film "Angrezi Medium," whose dream of studying abroad drives the emotional core of the story.
-
D.
Bhavani Choudhry
Bhavani Choudhry is a fictional character from the 1994 Hindi action drama film "Vijaypath."
-
E.
Sharan Narang
Sharan Narang is a machine learning researcher known for his work on large-scale natural language processing models, including contributions to the development of the T5 transformer architecture.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bc22afa788190a93dc2beeeeedcc3 |
completed | May 19, 2026, 1:51 a.m. |
| NEDg | Description generation | batch_6a0bc31b94f481909c437296742cfed8 |
completed | May 19, 2026, 1:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bc38cf34c8190aaca05bbf32d28de |
completed | May 19, 2026, 1:57 a.m. |
Created at: April 16, 2026, 8:30 p.m.