Triple
T4093846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Certified Analytics Professional |
E87765
|
entity |
| Predicate | isTechnologyAgnostic |
P7054
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Certified Analytics Professional, isTechnologyAgnostic, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isTechnologyAgnostic Context triple: [Certified Analytics Professional, isTechnologyAgnostic, true]
-
A.
isProgrammingLanguageIndependent
Indicates that something (such as a concept, algorithm, or interface) does not depend on any specific programming language and can be applied or used across different languages.
-
B.
technologyType
Indicates the specific kind or category of technology associated with an entity or relationship.
-
C.
isLanguageIndependent
Indicates that the relationship, property, or behavior holds true regardless of the specific natural language used to express or encode it.
-
D.
isGenerallyCompatibleWith
Indicates that two entities can typically function or coexist together without significant conflict, issues, or need for special adaptation.
-
E.
platformNeutral
chosen
Indicates that something is compatible with, applicable to, or designed to work across multiple platforms without dependence on any specific one.
- 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_69aed94425148190be337845d56fac22 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefcda2f408190bcf2b64535193162 |
completed | March 9, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69aef909c9c88190b09d48dad325a83c |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:40 p.m.