Triple
T30015757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BeagleBone Black |
E762589
|
entity |
| Predicate | capeDescription |
P168357
|
FINISHED |
| Object | Supports plug-in expansion boards called capes |
—
|
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: Supports plug-in expansion boards called capes | Statement: [BeagleBone Black, capeDescription, Supports plug-in expansion boards called capes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: capeDescription Context triple: [BeagleBone Black, capeDescription, Supports plug-in expansion boards called capes]
-
A.
scenicDescription
Indicates a descriptive portrayal of the visual or aesthetic qualities of a scene or landscape.
-
B.
placeDescribed
Indicates that one entity provides a description or account of a particular place or location.
-
C.
cityDescribedAs
Indicates that a city is characterized or portrayed using a particular description, label, or set of attributes.
-
D.
locationDescription
Indicates a textual description that specifies or characterizes the location of an entity.
-
E.
scenicCategory
Indicates the classification of a place or route based on its visual appeal or scenic qualities.
- F. None of above. chosen
Provenance (4 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_69f2246b0c84819094f1250b6a02d277 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f679844114819097242d623e723e4a |
completed | May 2, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69f66ec9919881908a187bfc7c4df192 |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 29, 2026, 6:45 p.m.