Triple
T2520495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anant Agarwal |
E55510
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Anant Agarwal |
E55510
|
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: Anant Agarwal | Statement: [Anant Agarwal, name, Anant Agarwal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anant Agarwal Context triple: [Anant Agarwal, name, Anant Agarwal]
-
A.
Anant Agarwal
chosen
Anant Agarwal is a computer scientist and MIT professor best known as the founding CEO of edX, a major online learning platform.
-
B.
Parag Agrawal
Parag Agrawal is an Indian-American technology executive and computer scientist best known for serving as the chief executive officer of Twitter.
-
C.
Vinod Khosla
Vinod Khosla is an Indian-American engineer, billionaire venture capitalist, and co-founder of Sun Microsystems known for his influential role in Silicon Valley and early-stage technology investing.
-
D.
Laxman Narasimhan
Laxman Narasimhan is an Indian-American business executive best known as the chief executive officer of Starbucks and former CEO of Reckitt Benckiser.
-
E.
Abhijit Vinayak Banerjee
Abhijit Vinayak Banerjee is an Indian-American economist and Nobel laureate renowned for his experimental approach to alleviating global poverty.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2367b6c819094239dfd12399643 |
completed | March 7, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2ba64390819083ea639eb4c1a1bd |
completed | March 9, 2026, 8:20 p.m. |
Created at: March 6, 2026, 9:46 p.m.