Triple
T122941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wear OS |
E2486
|
entity |
| Predicate | supportsService |
P203
|
FINISHED |
| Object | Google Pay |
E9286
|
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: Google Pay | Statement: [Wear OS, supportsService, Google Pay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google Pay Context triple: [Wear OS, supportsService, Google Pay]
-
A.
Google Pay
chosen
Google Pay is a digital wallet and online payment platform developed by Google that enables users to make secure contactless payments, in-app purchases, and online transactions using their devices.
-
B.
Samsung Pay
Samsung Pay is a mobile payment and digital wallet service by Samsung that lets users make secure contactless payments using compatible Samsung devices.
-
C.
Apple Pay
Apple Pay is a mobile payment and digital wallet service by Apple that lets users make secure, contactless payments using their Apple devices.
-
D.
PayPal
PayPal is a leading global online payment platform that enables individuals and businesses to send, receive, and manage digital payments securely over the internet.
-
E.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a2573b4e7481909ee09d2899f8a74b |
completed | Feb. 28, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2982485348190b2b361aef7b42fc9 |
completed | Feb. 28, 2026, 7:24 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.