Triple
T21944896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chandni Bar |
E541909
|
entity |
| Predicate | cinematographer |
P1953
|
FINISHED |
| Object | Vijay Kumar Arora |
—
|
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: Vijay Kumar Arora | Statement: [Chandni Bar, cinematographer, Vijay Kumar Arora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vijay Kumar Arora Context triple: [Chandni Bar, cinematographer, Vijay Kumar Arora]
-
A.
Vijay Kumar Arora
chosen
Vijay Kumar Arora is an Indian cinematographer and film director known for his work in Hindi and Punjabi cinema.
-
B.
Ashok Mishra
Ashok Mishra is an Indian screenwriter known for his work on films such as "Welcome to Sajjanpur."
-
C.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
D.
Virendra Sharma
Virendra Sharma is a British Labour Party politician who has served as the Member of Parliament for the London constituency of Ealing Southall.
-
E.
V. K. Singh
V. K. Singh is an Indian politician and retired four-star General of the Indian Army who has served as a Union minister in the Government of India.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.