Triple
T37788012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | მერაბ ჟორდანია |
E942007
|
entity |
| Predicate | საქმიანობის სფერო |
P3
|
FINISHED |
| Object | სპორტის მენეჯმენტი |
—
|
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: სპორტის მენეჯმენტი | Statement: [მერაბ ჟორდანია, საქმიანობის სფერო, სპორტის მენეჯმენტი]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: საქმიანობის სფერო Context triple: [მერაბ ჟორდანია, საქმიანობის სფერო, სპორტის მენეჯმენტი]
-
A.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
B.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
C.
workRelatedTo
Indicates a relationship where one entity’s work, tasks, or professional activities are connected, associated, or relevant to those of another entity.
-
D.
employmentBasedCategory
Indicates that one entity’s classification or status is determined by its relationship to employment, such as being based on a specific job, role, or work-related category.
-
E.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
- 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_69f76ee5cb0c81909a363d1c929156c0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbb9e8108c8190ae1c7940b1677e95 |
completed | May 6, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fbb141605c8190b9c27d70352522db |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:19 p.m.