Triple
T19336898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumar Vishwas |
E483646
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Kumar Vishwas |
—
|
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: Kumar Vishwas | Statement: [Kumar Vishwas, name, Kumar Vishwas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kumar Vishwas Context triple: [Kumar Vishwas, name, Kumar Vishwas]
-
A.
Kumar Vishwas
chosen
Kumar Vishwas is an Indian Hindi poet, politician, and public speaker known for his involvement in anti-corruption politics and his early association with the Aam Aadmi Party.
-
B.
Ranvir Singh
Ranvir Singh is a British television presenter and journalist best known for her work on ITV’s Good Morning Britain and other news and current affairs programmes.
-
C.
Raghuvir Yadav
Raghuvir Yadav is an acclaimed Indian actor and singer known for his versatile performances in parallel cinema, mainstream films, and television.
-
D.
Shekhar Suman
Shekhar Suman is an Indian actor, television host, and comedian known for his work in Hindi films and popular TV shows.
-
E.
Shekhar
Shekhar is an Indian filmmaker and actor best known for directing acclaimed films such as "Bandit Queen" and the historical drama "Elizabeth."
- 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61646e89081908c9f1d2cf557672c |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 10, 2026, 1:33 p.m.