Triple
T3022722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Desktop Bus Mouse |
E82499
|
entity |
| Predicate | trackingTechnology |
P44685
|
FINISHED |
| Object | mechanical ball |
—
|
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: mechanical ball | Statement: [Apple Desktop Bus Mouse, trackingTechnology, mechanical ball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trackingTechnology Context triple: [Apple Desktop Bus Mouse, trackingTechnology, mechanical ball]
-
A.
trackingType
Indicates the method or category by which an entity’s movement, status, or progress is monitored or recorded.
-
B.
technologyTrackExample
Indicates a relationship where one entity serves as an example or instance of a particular technology track associated with another entity.
-
C.
usesTrackage
Indicates that one transportation operator or service runs its vehicles over railway or transit tracks that are owned or controlled by another entity.
-
D.
tracksLocation
Indicates that one entity monitors and records the geographic position or movement of another entity over time.
-
E.
technologyTrend
Indicates a relationship where a technology is characterized as part of a broader pattern of change or direction in technological development over time.
- 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_69ad8b1fb34081908c1b873e2b7273e1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a963034819093d96566e9b0cea9 |
completed | March 8, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69ad961c430c8190ac48f2e3c7e7c649 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f6af3881909f4547967384114c |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 3 p.m.