Triple
T2051190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mac OS X 10.0 Cheetah |
E45571
|
entity |
| Predicate | userInterface |
P1594
|
FINISHED |
| Object | Aqua |
E6178
|
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: Aqua | Statement: [Mac OS X 10.0 Cheetah, userInterface, Aqua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aqua Context triple: [Mac OS X 10.0 Cheetah, userInterface, Aqua]
-
A.
Aqua
chosen
Aqua is the distinctive, glossy, and translucent graphical user interface introduced by Apple for macOS, known for its vibrant colors, smooth animations, and skeuomorphic design elements.
-
B.
Mika
Mika is a British-Lebanese pop singer-songwriter known for his flamboyant style and hit singles like "Grace Kelly."
-
C.
Crystal
Crystal is a feminine given name often associated with clarity and beauty, derived from the English word for clear, transparent mineral or glass.
-
D.
Crystal
Crystal is the surname of American actor, comedian, and filmmaker Billy Crystal, known for his work in film, television, and stand-up comedy.
-
E.
Bluewater
Bluewater is a large out-of-town shopping and leisure centre located in Kent, England.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb98f5f4881908d9aa0f10be44041 |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2009951881909d562821fef39c88 |
completed | March 9, 2026, 1:19 a.m. |
Created at: March 4, 2026, 7:39 p.m.