Triple
T2015172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regions of Belgium |
E43777
|
entity |
| Predicate | competenceType |
P18508
|
FINISHED |
| Object | exclusive competences in assigned fields |
—
|
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: exclusive competences in assigned fields | Statement: [Regions of Belgium, competenceType, exclusive competences in assigned fields]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: competenceType Context triple: [Regions of Belgium, competenceType, exclusive competences in assigned fields]
-
A.
competence
Indicates that an entity has the ability, skill, or qualification to perform a task or fulfill a role effectively.
-
B.
competenceArea
chosen
Indicates that one entity has a particular domain, field, or area in which it possesses competence, expertise, or responsibility.
-
C.
hasCompetence
Indicates that an entity possesses the ability, skill, or qualification to perform a specific task or function effectively.
-
D.
knowledgeType
Indicates the specific category or nature of knowledge associated with an entity or statement (e.g., factual, procedural, conceptual).
-
E.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
- 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_69a88716e9f08190946313fdc949e3cf |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8b610a88190bc10fd7dda19da08 |
completed | March 7, 2026, 5:33 a.m. |
| PD | Predicate disambiguation | batch_69abb7a03a1c81909ad50d56667db2d5 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:37 p.m.