Triple
T13712166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanjiv Banga |
E328798
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sanjiv Banga |
E328798
|
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: Sanjiv Banga | Statement: [Sanjiv Banga, name, Sanjiv Banga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanjiv Banga Context triple: [Sanjiv Banga, name, Sanjiv Banga]
-
A.
Sanjiv Banga
chosen
Sanjiv Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
-
B.
Sanjiv Singh
Sanjiv Singh is a robotics researcher and professor known for his work in autonomous systems and field robotics at Carnegie Mellon University.
-
C.
Sanjay Banerji
Sanjay Banerji is an economist and academic recognized for his scholarly contributions associated with the Delhi School of Economics.
-
D.
Charanjit Jutla
Charanjit Jutla is a cryptographer known for his research contributions in theoretical computer science and cryptographic protocols.
-
E.
Asheem Chandna
Asheem Chandna is a prominent venture capitalist known for investing in and advising leading enterprise technology and cybersecurity startups.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d54a68081908df25edf6d5df362 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:54 p.m.