Triple
T22330696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CherryPy |
E552013
|
entity |
| Predicate | runsOn |
P23
|
FINISHED |
| Object | macOS |
—
|
NE NERFINISHED |
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: macOS | Statement: [CherryPy, runsOn, macOS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: macOS Context triple: [CherryPy, runsOn, macOS]
-
A.
macOS
chosen
macOS is Apple’s proprietary Unix-based operating system known for its graphical user interface, tight integration with Apple hardware and services, and strong emphasis on usability and security.
-
B.
OSX
OSX is the Orquesta Sinfónica de Xalapa, one of Mexico’s oldest and most prestigious symphony orchestras, based in Xalapa, Veracruz.
-
C.
Mac
Mac is Apple’s line of personal computers known for their sleek hardware design and tight integration with the macOS operating system.
-
D.
Mac
Mac is the affectionate nickname used by fans for the resourceful TV character Angus MacGyver from the action-adventure series "MacGyver."
-
E.
Mac
Mac is a character portrayed by Michael Badalucco, best known as the endearing and quirky private investigator on the television series "The Practice."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577a9c348190b8662142afa832be |
completed | April 29, 2026, 12:57 a.m. |
Created at: April 16, 2026, 8:43 p.m.