Triple
T1442043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Arab Emirates Armed Forces |
E31094
|
entity |
| Predicate | manpowerType |
P11922
|
FINISHED |
| Object | professional volunteer force |
—
|
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 volunteer force | Statement: [United Arab Emirates Armed Forces, manpowerType, professional volunteer force]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: manpowerType Context triple: [United Arab Emirates Armed Forces, manpowerType, professional volunteer force]
-
A.
hasWorkforceType
chosen
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
B.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
C.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
-
D.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
E.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
- 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_69a4991633388190a4d61b5a98aa407a |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c55714588190a95b4f677c21cbaa |
completed | March 1, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69a4c47a840c819083307a65c027a19e |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.