Triple
T8414154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Artesyn Embedded Technologies |
E198691
|
entity |
| Predicate | targetsApplication |
P57747
|
FINISHED |
| Object | communications infrastructure |
—
|
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: communications infrastructure | Statement: [Artesyn Embedded Technologies, targetsApplication, communications infrastructure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetsApplication Context triple: [Artesyn Embedded Technologies, targetsApplication, communications infrastructure]
-
A.
appliesTo
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
B.
targetsUseCase
chosen
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
C.
usesTarget
Indicates that one entity employs, applies, or operates on another entity as its target or object of action.
-
D.
appliesVia
Indicates that an action, rule, or effect is carried out, implemented, or achieved through a specified method, medium, or mechanism.
-
E.
targetedSoftware
Indicates that a particular piece of software is the specific focus or target of an action, operation, or effect.
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e328cc8190b3b038005d0bb66f |
completed | March 31, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:06 p.m.