Triple
T2129300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Wallet |
E46499
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Passbook |
E46499
|
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: Passbook | Statement: [Apple Wallet, formerName, Passbook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Passbook Context triple: [Apple Wallet, formerName, Passbook]
-
A.
Apple Wallet
chosen
Apple Wallet is a digital wallet app by Apple that securely stores and manages payment cards, passes, tickets, and IDs for use across compatible Apple devices.
-
B.
Myki
Myki is Melbourne’s contactless smartcard public transport ticketing system used across trains, trams, and buses in Victoria, Australia.
-
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.
Apple Books
Apple Books is Apple’s e-book and audiobook platform and reading app, available across its devices for purchasing, organizing, and enjoying digital books and audio content.
-
E.
Applefest
Applefest is an annual autumn festival in Warwick, New York, celebrating the local apple harvest with food, crafts, entertainment, and community activities.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb7659f48190871cb27faf47e18a |
completed | March 7, 2026, 5:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae51a36398819081df18cc18bc3456 |
completed | March 9, 2026, 4:50 a.m. |
Created at: March 4, 2026, 7:44 p.m.