Triple
T5128989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suchitra Sen |
E115648
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Riya Sen |
E116972
|
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: Riya Sen | Statement: [Suchitra Sen, relative, Riya Sen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Riya Sen Context triple: [Suchitra Sen, relative, Riya Sen]
-
A.
Riya Sen
chosen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
-
B.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
C.
Priya Basu
Priya Basu is an economist and development finance expert known for her work on financial inclusion and policy at institutions such as the World Bank.
-
D.
Marianne Mithun
Marianne Mithun is an American linguist renowned for her extensive work on Native American languages, language typology, and the documentation of endangered languages.
-
E.
Kirron Kher
Kirron Kher is an Indian film and television actress and politician known for her powerful character roles in Hindi cinema and her work as a Member of Parliament.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7825facc8190b2a6c17216290b5c |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4c23d5c8190883a297254d9c80d |
completed | March 21, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:42 p.m.