Triple
T367082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Pay |
E7983
|
entity |
| Predicate | supportsLoyaltyCards |
P11672
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Apple Pay, supportsLoyaltyCards, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsLoyaltyCards Context triple: [Apple Pay, supportsLoyaltyCards, yes]
-
A.
loyaltyProgramEarnings
Indicates the amount or details of rewards or benefits a participant accrues within a loyalty or rewards program.
-
B.
supportsAccountType
Indicates that one entity is compatible with, or able to operate for, a specified type or category of account.
-
C.
cardType
Indicates the classification or category assigned to a card within a given system or context.
-
D.
supportsBonus
Indicates that one entity provides or enables an additional benefit, reward, or bonus for another entity.
-
E.
supportsClaim
Indicates that one entity provides evidence, reasoning, or backing that strengthens or validates the truth or credibility of another entity’s claim.
- F. None of above. chosen
Provenance (4 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebe92c7c8190b49af2b2b461eacc |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95ede588190998fdf3a6ea90498 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea0b23ec8190bef9d593162388a4 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.