Triple
T19336938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumar Vishwas |
E483646
|
entity |
| Predicate | twitterHandle |
P2866
|
FINISHED |
| Object | @DrKumarVishwas |
—
|
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: @DrKumarVishwas | Statement: [Kumar Vishwas, twitterHandle, @DrKumarVishwas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: @DrKumarVishwas Context triple: [Kumar Vishwas, twitterHandle, @DrKumarVishwas]
-
A.
Kumar
Kumar is a common Indian surname and given name used across various regions and communities in South Asia.
-
B.
Kumar Saurabh
Kumar Saurabh is a technology entrepreneur best known as a co-founder of the cloud-based machine data analytics company Sumo Logic.
-
C.
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.
-
D.
Amar Kaushik
Amar Kaushik is an Indian film director best known for helming popular Hindi films like the horror-comedy "Stree."
-
E.
Saurabh Jha
Saurabh Jha is an astrophysicist known for his work on high-redshift supernovae and their use in studying cosmic expansion and dark energy.
- 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.