Triple
T22103058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special 26 |
E546216
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Divya Dutta |
—
|
NE NERFINISHED |
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: Divya Dutta | Statement: [Special 26, starring, Divya Dutta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Divya Dutta Context triple: [Special 26, starring, Divya Dutta]
-
A.
Divya Dutta
chosen
Divya Dutta is an Indian film actress known for her versatile supporting and character roles across Hindi and Punjabi cinema.
-
B.
Sanya Malhotra
Sanya Malhotra is an Indian actress known for her acclaimed debut in the film "Dangal" and subsequent roles in Hindi cinema.
-
C.
Priya Dutt
Priya Dutt is an Indian politician and former Member of Parliament from Mumbai, known for her work with the Indian National Congress and as the daughter of actors-turned-politicians Sunil Dutt and Nargis.
-
D.
Divya Katdare
Divya Katdare is a central character on the television series "Royal Pains," known as a skilled and poised physician assistant who works closely with concierge doctor Hank Lawson in the Hamptons.
-
E.
Kajal Aggarwal
Kajal Aggarwal is a popular Indian actress best known for her leading roles in Telugu and Tamil cinema, as well as appearances in Hindi films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 16, 2026, 8:30 p.m.