Triple
T9245556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trisha |
E222184
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object |
Trisha Krishnan
Trisha Krishnan is a prominent Indian actress and model best known for her leading roles in Tamil and Telugu cinema.
|
E786405
|
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: Trisha Krishnan | Statement: [Trisha, name, Trisha Krishnan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trisha Krishnan Context triple: [Trisha, name, Trisha Krishnan]
-
A.
Maya Bhaskar
Maya Bhaskar is the daughter of British comedian, writer, and actress Meera Syal.
-
B.
Ramya Krishnan
Ramya Krishnan is an acclaimed Indian actress known for her powerful and versatile performances across Tamil, Telugu, and other South Indian film industries.
-
C.
Tessy Antony
Tessy Antony is a Luxembourgish former princess, social entrepreneur, and advocate for education and women's rights.
-
D.
Gautami Tadimalla
Gautami Tadimalla is an Indian actress and television personality known for her work in South Indian cinema, particularly Tamil and Telugu films.
-
E.
Kiran Rao
Kiran Rao is an Indian film producer, director, and screenwriter known for her work in Hindi cinema and for co-producing acclaimed films such as "Dhobi Ghat" and "Delhi Belly."
- 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: Trisha Krishnan Triple: [Trisha, name, Trisha Krishnan]
Generated description
Trisha Krishnan is a prominent Indian actress and model best known for her leading roles in Tamil and Telugu cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trisha Krishnan Target entity description: Trisha Krishnan is a prominent Indian actress and model best known for her leading roles in Tamil and Telugu cinema.
-
A.
Maya Bhaskar
Maya Bhaskar is the daughter of British comedian, writer, and actress Meera Syal.
-
B.
Ramya Krishnan
Ramya Krishnan is an acclaimed Indian actress known for her powerful and versatile performances across Tamil, Telugu, and other South Indian film industries.
-
C.
Tessy Antony
Tessy Antony is a Luxembourgish former princess, social entrepreneur, and advocate for education and women's rights.
-
D.
Gautami Tadimalla
Gautami Tadimalla is an Indian actress and television personality known for her work in South Indian cinema, particularly Tamil and Telugu films.
-
E.
Kiran Rao
Kiran Rao is an Indian film producer, director, and screenwriter known for her work in Hindi cinema and for co-producing acclaimed films such as "Dhobi Ghat" and "Delhi Belly."
- 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_69ca841d2b18819089f9faf5b2c2aec0 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd03efaa748190973916bd790f6e3a |
completed | April 1, 2026, 11:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d077f14804819098f443a2517ad461 |
completed | April 4, 2026, 2:31 a.m. |
| NEDg | Description generation | batch_69d07933d26c81909257a4e6a5fe1c6e |
completed | April 4, 2026, 2:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d079ed3eb48190b410934b809ebc3d |
completed | April 4, 2026, 2:39 a.m. |
Created at: March 30, 2026, 7:30 p.m.