Triple
T503624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ajay Banga |
E10452
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Mastercard |
E47018
|
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: Mastercard | Statement: [Ajay Banga, employer, Mastercard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mastercard Context triple: [Ajay Banga, employer, Mastercard]
-
A.
Mastercard
chosen
Mastercard is a global financial services corporation best known for its widely used credit and debit card payment network.
-
B.
Visa
Visa is a global financial services corporation best known for operating one of the world’s largest electronic payment networks for credit, debit, and prepaid cards.
-
C.
American Express
American Express is a global financial services company best known for its charge and credit cards, payment network, and travel-related services.
-
D.
Chase Paymentech
Chase Paymentech is a payment processing and merchant services provider specializing in credit card and electronic transaction solutions for businesses.
-
E.
Capital One
Capital One is a major American bank holding company best known for its credit card, auto loan, banking, and savings products.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1357738819085eae6c10fa2fca9 |
completed | Feb. 28, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4984d3a4881909b4bec4c9b7dcd03 |
completed | March 1, 2026, 7:49 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.