Triple
T9245557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trisha |
E222184
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Trisha Krishnan |
E786405
|
NE FINISHED |
How this triple was built (2 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, birthName, Trisha Krishnan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trisha Krishnan Context triple: [Trisha, birthName, Trisha Krishnan]
-
A.
Trisha Krishnan
chosen
Trisha Krishnan is a prominent Indian actress and model best known for her leading roles in Tamil and Telugu cinema.
-
B.
Maya Bhaskar
Maya Bhaskar is the daughter of British comedian, writer, and actress Meera Syal.
-
C.
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.
-
D.
Tessy Antony
Tessy Antony is a Luxembourgish former princess, social entrepreneur, and advocate for education and women's rights.
-
E.
Gautami Tadimalla
Gautami Tadimalla is an Indian actress and television personality known for her work in South Indian cinema, particularly Tamil and Telugu films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d09bca80a481908038fee287989a54 |
completed | April 4, 2026, 5:04 a.m. |
Created at: March 30, 2026, 7:30 p.m.