Triple
T16842433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nish Kumar |
E409445
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nishant Kumar |
E409445
|
NE FINISHED |
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: Nishant Kumar | Statement: [Nish Kumar, name, Nishant Kumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nishant Kumar Context triple: [Nish Kumar, name, Nishant Kumar]
-
A.
Nish Kumar
chosen
Nish Kumar is a British stand-up comedian, actor, and radio presenter known for his sharp political satire and appearances on various UK comedy shows.
-
B.
Abhishek Verma
Abhishek Verma is a computer scientist best known as a co-creator of Google Borg, the large-scale cluster management and scheduling system that inspired Kubernetes.
-
C.
Gautam Kumar
Gautam Kumar is known as the son of legendary Indian Bengali actor Uttam Kumar.
-
D.
Kumar Saurabh
Kumar Saurabh is a technology entrepreneur best known as a co-founder of the cloud-based machine data analytics company Sumo Logic.
-
E.
Siddharth Sinha
Siddharth Sinha is one of the three close friends at the heart of the Hindi film "Dil Chahta Hai," known for his sensitive, introspective nature and emotionally mature outlook on love and relationships.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b35167a48190b45a459023e3ab1b |
completed | April 18, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb199168819080a9ddc7534f19a7 |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 5:24 a.m.