Triple
T23439429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anushka Sharma |
E565353
|
entity |
| Predicate | producerOf |
P490
|
FINISHED |
| Object | Pari |
—
|
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: Pari | Statement: [Anushka Sharma, producerOf, Pari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pari Context triple: [Anushka Sharma, producerOf, Pari]
-
A.
Pari
Pari is the official mascot character created for the 2015 World Aquatics Championships held in Kazan, Russia.
-
B.
Pari
Pari is an acclaimed Iranian film directed by Dariush Mehrjui, adapted from J.D. Salinger’s “Franny and Zooey” and known for its introspective, philosophical narrative.
-
C.
Pari
chosen
Pari is a 2018 Indian supernatural horror film starring Anushka Sharma, known for its dark, atmospheric storytelling and departure from conventional Bollywood horror tropes.
-
D.
Paraggi
Paraggi is a small, picturesque bay and beach on the Italian Riviera, known for its clear emerald waters and upscale seaside atmosphere between Santa Margherita Ligure and Portofino.
-
E.
Prati
Prati is an elegant central district of Rome known for its wide boulevards, upscale shops, and proximity to the Vatican and major judicial buildings.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a5de713c8190b35bfa66dddbd5af |
completed | April 29, 2026, 6:31 a.m. |
Created at: April 17, 2026, 5:50 p.m.