Triple
T166445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Certified Software Development Professional |
E3024
|
entity |
| Predicate | typeOfAssessment |
P750
|
FINISHED |
| Object | professional competency assessment |
—
|
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: professional competency assessment | Statement: [Certified Software Development Professional, typeOfAssessment, professional competency assessment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfAssessment Context triple: [Certified Software Development Professional, typeOfAssessment, professional competency assessment]
-
A.
assessmentMethod
chosen
Indicates the method or procedure used to evaluate, measure, or judge something.
-
B.
assessedBy
Indicates that an entity has been evaluated, examined, or judged by another entity (typically an agent or authority).
-
C.
historicalAssessment
Indicates an evaluation or judgment of something based on its historical context, significance, or development over time.
-
D.
typeOfSupport
Indicates the kind or category of assistance, help, or backing provided in a given context.
-
E.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25883ac8481909616b2179561bd98 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a25664ba8081908ac298511a9fc5ba |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.