Triple
T7188629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | kelvin |
E167630
|
entity |
| Predicate | usedInPlasmaPhysics |
P75634
|
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: [kelvin, usedInPlasmaPhysics, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInPlasmaPhysics Context triple: [kelvin, usedInPlasmaPhysics, yes]
-
A.
alsoUsedIn
Indicates that something is additionally employed, applied, or present in another context, setting, or use case beyond the primary one.
-
B.
areUsedIn
Indicates that certain entities serve as components, tools, or resources within a particular process, context, or application.
-
C.
usedInIndustry
Indicates that something is employed or applied within a particular industry or industrial sector.
-
D.
usedInConcept
Indicates that something (such as a method, idea, or component) is employed as part of, or plays a role within, a particular concept.
-
E.
widelyUsedIn
Indicates that something is commonly or extensively utilized within a particular context, domain, or group.
- 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_69c6888b5248819090499a884ee3ec39 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8e3d9188190ba2792098d76fb86 |
completed | March 27, 2026, 8:30 p.m. |
| PD | Predicate disambiguation | batch_69c6e752385c819096fbab55566ee2a8 |
completed | March 27, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69c6e8b5f6508190af28e06a7959d717 |
completed | March 27, 2026, 8:29 p.m. |
Created at: March 27, 2026, 2:50 p.m.