Triple
T21943925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paan Singh Tomar |
E541888
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Vipin Sharma |
—
|
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: Vipin Sharma | Statement: [Paan Singh Tomar, starring, Vipin Sharma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vipin Sharma Context triple: [Paan Singh Tomar, starring, Vipin Sharma]
-
A.
Vipin Sharma
chosen
Vipin Sharma is an Indian actor known for his character roles in Hindi cinema and streaming projects, including a prominent part in the action thriller film "Monkey Man."
-
B.
Vipin Kumar
Vipin Kumar is a prominent computer scientist known for his influential research in data mining and high-performance computing, particularly in applications to climate and earth sciences.
-
C.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
D.
Vijay Arora
Vijay Arora is a cinematographer known for his work on the Hindi film "Shaadi No. 1."
-
E.
Vinay Mishra
Vinay Mishra is an Indian politician serving as a Member of the Legislative Assembly (MLA) from the Dwarka constituency in Delhi.
- 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_69f1242515ec8190b015bf8c7b13be85 |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.