Triple
T30428639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mac Pro (2019) |
E774098
|
entity |
| Predicate | supportsRackMountVariant |
P83207
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Mac Pro (2019), supportsRackMountVariant, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsRackMountVariant Context triple: [Mac Pro (2019), supportsRackMountVariant, yes]
-
A.
canBeRackMounted
chosen
Indicates that an entity is suitable or designed to be installed in a standard equipment rack.
-
B.
supportedCabinet
Indicates that one entity has provided political, logistical, or formal backing to a particular cabinet or governing body.
-
C.
supportsModelVariant
Indicates that one entity is capable of operating with, being compatible with, or otherwise accommodating a specific variant of a model.
-
D.
supportsChassisType
Indicates that one entity is compatible with and can be used to support or accommodate a specified chassis type.
-
E.
typeOfInstallationSupported
Indicates that one entity supports or is compatible with a specified type or method of installation for another entity.
- 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_69f22491ba248190b9a4776ca8e42d02 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 29, 2026, 8:06 p.m.