Triple
T29030443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Launchpad |
E737711
|
entity |
| Predicate | targetUserExperience |
P860
|
FINISHED |
| Object | make macOS feel familiar to iOS users |
—
|
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: make macOS feel familiar to iOS users | Statement: [Launchpad, targetUserExperience, make macOS feel familiar to iOS users]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetUserExperience Context triple: [Launchpad, targetUserExperience, make macOS feel familiar to iOS users]
-
A.
targetUserActivity
Indicates that a specific user is the intended recipient or focus of a particular activity or action.
-
B.
targetUserAction
Indicates that a specific user is the intended recipient or focus of a particular action performed within the system.
-
C.
targetsUseCase
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
D.
target
chosen
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
E.
targetAudienceRatingContext
Indicates the contextual conditions or setting (such as platform, region, or usage scenario) under which a particular audience rating is intended to apply.
- F. None of above.
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_69f077ef00fc81909325f084ad37c035 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: April 28, 2026, 9:55 a.m.