Triple
T11352019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple platforms APIs |
E268859
|
entity |
| Predicate | includesFramework |
P1393
|
FINISHED |
| Object | Combine |
E97040
|
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: Combine | Statement: [Apple platforms APIs, includesFramework, Combine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Combine Context triple: [Apple platforms APIs, includesFramework, Combine]
-
A.
Combine
Combine is a type of artwork, pioneered by Robert Rauschenberg, that merges painting and sculpture by incorporating everyday objects and materials into the painted surface.
-
B.
Combine
chosen
Combine is Apple’s declarative reactive programming framework for handling asynchronous events and data streams in Swift applications.
-
C.
Pinagsama
Pinagsama is a barangay (village-level administrative division) located in the city of Taguig in Metro Manila, Philippines.
-
D.
Composite
Composite is a structural design pattern that lets you treat individual objects and compositions of objects uniformly by organizing them into tree-like hierarchies.
-
E.
the Combine
The Combine is the National Football League’s annual pre-draft event where top college football prospects undergo physical tests, drills, and interviews in front of NFL coaches, scouts, and executives.
- 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea24489081908fbf47fd2e6d709c |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e543a6e97481909dc77a553b217b4d |
completed | April 19, 2026, 9:05 p.m. |
Created at: April 8, 2026, 9:33 p.m.