Triple
T10249449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diners Club International |
E240300
|
entity |
| Predicate | hasCompetitor |
P1375
|
FINISHED |
| Object | UnionPay |
E298146
|
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: UnionPay | Statement: [Diners Club International, hasCompetitor, UnionPay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UnionPay Context triple: [Diners Club International, hasCompetitor, UnionPay]
-
A.
UnionPay
chosen
UnionPay is a Chinese state-backed financial services corporation best known for operating one of the world’s largest bank card and payment networks.
-
B.
JCB
JCB is a major British multinational manufacturer of construction, agricultural, and industrial equipment, best known for its yellow diggers and backhoe loaders.
-
C.
Mastercard
Mastercard is a global financial services corporation best known for its widely used credit and debit card payment network.
-
D.
Chase Paymentech
Chase Paymentech is a payment processing and merchant services provider specializing in credit card and electronic transaction solutions for businesses.
-
E.
Worldline
Worldline is a French multinational company specializing in payment and transactional services, recognized as a major European player in digital payments.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23c4cd88190b99e65a074b68d6b |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7b66c6881908b432fbdd5ecf11e |
completed | April 9, 2026, 12:49 a.m. |
Created at: April 6, 2026, 11:28 a.m.