Triple
T9761682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NSData |
E236684
|
entity |
| Predicate | introducedIn |
P513
|
FINISHED |
| Object | Cocoa API |
E59592
|
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: Cocoa API | Statement: [NSData, introducedIn, Cocoa API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cocoa API Context triple: [NSData, introducedIn, Cocoa API]
-
A.
macOS Cocoa
chosen
macOS Cocoa is Apple’s native object-oriented application framework for building graphical user interfaces on macOS.
-
B.
Cocoa Touch
Cocoa Touch is Apple’s application development framework for building iOS and other mobile OS apps, providing the UI components, event handling, and infrastructure layers on top of the underlying operating system.
-
C.
Gluon API
Gluon API is a high-level, imperative deep learning interface designed for building and training neural networks more easily and flexibly on the Apache MXNet framework.
-
D.
Pascal API
Pascal API is a Pascal-based application programming interface used for developing and integrating software components in Pascal.
-
E.
Symbol API
Symbol API is MXNet’s symbolic computation interface for defining, composing, and optimizing deep learning models as static computation graphs.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda04c70108190a8ed09eb6f2a124e |
completed | April 1, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c41022908190a5f55291a2323691 |
completed | April 5, 2026, 2:08 a.m. |
Created at: March 30, 2026, 8:25 p.m.