Triple
T23439424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anushka Sharma |
E565353
|
entity |
| Predicate | coStarredWith |
P14987
|
FINISHED |
| Object | Ranbir Kapoor |
—
|
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: Ranbir Kapoor | Statement: [Anushka Sharma, coStarredWith, Ranbir Kapoor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ranbir Kapoor Context triple: [Anushka Sharma, coStarredWith, Ranbir Kapoor]
-
A.
Ranbir Kapoor
chosen
Ranbir Kapoor is a prominent Indian film actor and producer known for his leading roles in contemporary Hindi cinema.
-
B.
Ranbir Singh
Ranbir Singh was a 19th-century Maharaja of Jammu and Kashmir known for consolidating Dogra rule and promoting administrative, legal, and educational reforms in the princely state.
-
C.
Ranveer Singh
Ranveer Singh is a popular Indian film actor known for his energetic performances and leading roles in numerous successful Bollywood movies.
-
D.
Shahid Kapoor
Shahid Kapoor is a popular Indian film actor known for his versatile performances in Hindi cinema, including acclaimed roles in films like "Jab We Met," "Haider," and "Kabir Singh."
-
E.
Varun Dhawan
Varun Dhawan is a popular Indian film actor known for his work in contemporary Bollywood cinema, particularly in commercial comedies and dramas.
- 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.