Triple
T12946341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manvinder Singh Banga |
E309776
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Manvinder Singh Banga |
E309776
|
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: Manvinder Singh Banga | Statement: [Manvinder Singh Banga, name, Manvinder Singh Banga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manvinder Singh Banga Context triple: [Manvinder Singh Banga, name, Manvinder Singh Banga]
-
A.
Manvinder Singh Banga
chosen
Manvinder Singh Banga is an Indian business executive best known for his long career at Unilever, where he rose to senior global leadership roles.
-
B.
Amandeep Singh
Amandeep Singh is an actor who appeared in the 2018 biographical thriller film "Hotel Mumbai."
-
C.
Madanjeet Singh
Madanjeet Singh was an Indian diplomat, artist, and UNESCO Goodwill Ambassador known for his lifelong advocacy of peace, tolerance, and non-violence.
-
D.
Nirvikar Singh
Nirvikar Singh is an economist and academic known for his contributions to economic theory and policy, associated with leading institutions such as the Delhi School of Economics.
-
E.
Satinder Singh
Satinder Singh is a computer scientist and researcher known for his contributions to reinforcement learning and artificial intelligence.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1b3694819098527dcea3cfed93 |
completed | April 10, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af75bc04819098d98c47fca48ac9 |
completed | May 3, 2026, 2:14 a.m. |
Created at: April 9, 2026, 5:43 p.m.