Triple
T5934275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Worldline |
E132005
|
entity |
| Predicate | hasSubsidiary |
P254
|
FINISHED |
| Object | Ingenico |
E556363
|
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: Ingenico | Statement: [Worldline, hasSubsidiary, Ingenico]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ingenico Context triple: [Worldline, hasSubsidiary, Ingenico]
-
A.
Ingenico
chosen
Ingenico is a global provider of payment terminals and payment services, widely used by merchants for secure electronic transactions.
-
B.
Worldline
Worldline is a French multinational company specializing in payment and transactional services, recognized as a major European player in digital payments.
-
C.
Zebra Technologies
Zebra Technologies is an American company that provides enterprise asset intelligence solutions, including barcode printing, RFID, and data capture technologies for businesses worldwide.
-
D.
Chase Paymentech
Chase Paymentech is a payment processing and merchant services provider specializing in credit card and electronic transaction solutions for businesses.
-
E.
Mastercard
Mastercard is a global financial services corporation best known for its widely used credit and debit card payment network.
- 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_69c0085c55dc8190aa90e242c956e2fa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c038a0c4e481908170d615330edb1a |
completed | March 22, 2026, 6:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e3b68a1c81908a219ffee8300e02 |
completed | March 23, 2026, 6:54 a.m. |
Created at: March 22, 2026, 4 p.m.