Triple
T6439284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LTE-Advanced |
E129975
|
entity |
| Predicate | targetUseCases |
P57747
|
FINISHED |
| Object | mobile broadband |
—
|
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: mobile broadband | Statement: [LTE-Advanced, targetUseCases, mobile broadband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetUseCases Context triple: [LTE-Advanced, targetUseCases, mobile broadband]
-
A.
targetsUseCase
chosen
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
B.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
C.
toolUseExamples
Indicates that one entity provides example instances or demonstrations of how a particular tool is or can be used by another entity.
-
D.
usesTarget
Indicates that one entity employs, applies, or operates on another entity as its target or object of action.
-
E.
hasUseCase
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06967241c8190965bac395adf2d03 |
completed | March 22, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69c060f96980819091bab9335922a457 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:45 p.m.