Triple
T25732789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuopio region |
E645287
|
entity |
| Predicate | hasSectorSpecialization |
P76324
|
FINISHED |
| Object | health and wellbeing |
—
|
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: health and wellbeing | Statement: [Kuopio region, hasSectorSpecialization, health and wellbeing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectorSpecialization Context triple: [Kuopio region, hasSectorSpecialization, health and wellbeing]
-
A.
isSectorSpecific
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
-
B.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
C.
hasProductionSpecialism
Indicates that an entity has a specific area of specialization or focus within production activities or processes.
-
D.
hasSectoralPriority
chosen
Indicates that something is designated as having priority or special importance within a particular sector or industry.
-
E.
subjectSpecialization
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
- 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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f7117e55908190a67105e92bc4830f |
completed | May 3, 2026, 9:12 a.m. |
| PD | Predicate disambiguation | batch_69f70f380690819090cc34763ba460ed |
completed | May 3, 2026, 9:02 a.m. |
Created at: April 21, 2026, 11:18 p.m.