Triple
T10185941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cirrus |
E236907
|
entity |
| Predicate | brandOf |
P1500
|
FINISHED |
| Object | Mastercard ATM network services |
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 ATM network services | Statement: [Cirrus, brandOf, Mastercard ATM network services]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mastercard ATM network services Context triple: [Cirrus, brandOf, Mastercard ATM network services]
-
A.
Mastercard
chosen
Mastercard is a global financial services corporation best known for its widely used credit and debit card payment network.
-
B.
First Data Corporation
First Data Corporation is a global financial services and payment processing company that provides merchant transaction processing, point-of-sale solutions, and related technology services to businesses and financial institutions.
-
C.
Ingenico
Ingenico is a global provider of payment terminals and payment services, widely used by merchants for secure electronic transactions.
-
D.
Worldline
Worldline is a French multinational company specializing in payment and transactional services, recognized as a major European player in digital payments.
-
E.
Metcard
Metcard was Melbourne’s former magnetic stripe ticketing system used for public transport before the introduction of the Myki smartcard.
- 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_69ca84d7260c8190bfbec36762943f37 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded36e9808190b385c5aec4889e00 |
completed | April 2, 2026, 4:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317a4d99c8190941322d3de2998f5 |
completed | April 6, 2026, 2:17 a.m. |
Created at: March 30, 2026, 9:12 p.m.