Triple
T956185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Arcade |
E20629
|
entity |
| Predicate | gameOwnershipModel |
P21824
|
FINISHED |
| Object | access contingent on active subscription |
—
|
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: access contingent on active subscription | Statement: [Apple Arcade, gameOwnershipModel, access contingent on active subscription]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gameOwnershipModel Context triple: [Apple Arcade, gameOwnershipModel, access contingent on active subscription]
-
A.
hasGamesAt
Indicates that a particular location, venue, or platform hosts or offers one or more games.
-
B.
ownershipType
Indicates the specific nature or category of the ownership relationship that one entity holds over another.
-
C.
usesTrackOwnedBy
Indicates that an entity makes use of a track (e.g., route, path, or rail line) that is owned by another specified entity.
-
D.
teamOwnershipModel
Indicates the type or structure of ownership relationship that exists between a team and its owning entity (such as an individual, group, or organization).
-
E.
hasGameType
Indicates that an entity (such as a game or match) is associated with a specific category or type of game.
- 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_69a493b21f2881908132dcf45dcd2f36 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3f981bc819098125554eeeb6375 |
completed | March 1, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a18ecc8190883f6206fe3b0fb6 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b326d9d88190913c1a892a795707 |
completed | March 1, 2026, 9:44 p.m. |
Created at: March 1, 2026, 7:40 p.m.