Triple
T3110662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banga |
E64940
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Harpreet Banga
Harpreet Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
|
E328799
|
NE FINISHED |
How this triple was built (4 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: Harpreet Banga | Statement: [Banga, hasNotableBearer, Harpreet Banga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harpreet Banga Context triple: [Banga, hasNotableBearer, Harpreet Banga]
-
A.
Manvinder Singh Banga
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.
Amarjeet Sohi
Amarjeet Sohi is a Canadian politician who serves as the mayor of Edmonton and is a former federal cabinet minister.
-
C.
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.
-
D.
Madanjeet Singh
Madanjeet Singh was an Indian diplomat, artist, and UNESCO Goodwill Ambassador known for his lifelong advocacy of peace, tolerance, and non-violence.
-
E.
Sanjiv Singh
Sanjiv Singh is a robotics researcher and professor known for his work in autonomous systems and field robotics at Carnegie Mellon University.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Harpreet Banga Triple: [Banga, hasNotableBearer, Harpreet Banga]
Generated description
Harpreet Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Harpreet Banga Target entity description: Harpreet Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
-
A.
Manvinder Singh Banga
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.
Amarjeet Sohi
Amarjeet Sohi is a Canadian politician who serves as the mayor of Edmonton and is a former federal cabinet minister.
-
C.
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.
-
D.
Madanjeet Singh
Madanjeet Singh was an Indian diplomat, artist, and UNESCO Goodwill Ambassador known for his lifelong advocacy of peace, tolerance, and non-violence.
-
E.
Sanjiv Singh
Sanjiv Singh is a robotics researcher and professor known for his work in autonomous systems and field robotics at Carnegie Mellon University.
- F. None of above. chosen
Provenance (5 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada43954f0819096a96331bf3c53a8 |
completed | March 8, 2026, 4:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f5cfc7c8190b867794c0e9a271e |
completed | March 12, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69b2101824c0819097dc967d83d18751 |
completed | March 12, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b210886d308190b120beb8f6bcbf3a |
completed | March 12, 2026, 1:02 a.m. |
Created at: March 8, 2026, 3:04 p.m.