Triple
T12698151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UN staff |
E303388
|
entity |
| Predicate | hasEmploymentType |
P11918
|
FINISHED |
| Object | fixed-term appointment |
—
|
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: fixed-term appointment | Statement: [UN staff, hasEmploymentType, fixed-term appointment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmploymentType Context triple: [UN staff, hasEmploymentType, fixed-term appointment]
-
A.
employmentType
chosen
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
B.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
C.
hasWorkforceType
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
D.
employedTo
Indicates that one entity is hired or engaged to perform work, services, or duties for another entity.
-
E.
stateOfEmployment
Indicates that one entity’s employment status or condition is defined in relation to another entity (such as an employer, position, or employment situation).
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d962a32c6481908ddaddae4ea267bf |
completed | April 10, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69d960be63f081908a5ef5ef17a311bf |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:22 p.m.