Triple
T37579471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief Executive Officer of Efes Beverage Group |
E934914
|
entity |
| Predicate | sectorOfEmployer |
P2510
|
FINISHED |
| Object | private sector |
—
|
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: private sector | Statement: [Chief Executive Officer of Efes Beverage Group, sectorOfEmployer, private sector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectorOfEmployer Context triple: [Chief Executive Officer of Efes Beverage Group, sectorOfEmployer, private sector]
-
A.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
B.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
C.
sectorOfParentOrganization
Indicates that the specified sector or industry classification belongs to, or characterizes, the parent organization of the given entity.
-
D.
employerType
chosen
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
E.
sectorEmploymentRole
Indicates the specific role or function an entity holds within a particular employment sector or industry.
- 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_69f76ecd99148190be327e391a70f5b6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbacaf54648190811ea33b34907e8e |
completed | May 6, 2026, 9:03 p.m. |
| PD | Predicate disambiguation | batch_69fba883f770819091059c6f6c6af9f7 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:17 p.m.