Triple
T1001823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International Affairs Fellowship Program |
E21619
|
entity |
| Predicate | typeOfExperience |
P22183
|
FINISHED |
| Object | policy-focused fellowship |
—
|
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: policy-focused fellowship | Statement: [International Affairs Fellowship Program, typeOfExperience, policy-focused fellowship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfExperience Context triple: [International Affairs Fellowship Program, typeOfExperience, policy-focused fellowship]
-
A.
softwareExperience
Indicates the level or extent of a person's prior experience working with or using specific software.
-
B.
allowsNoPriorExperience
Indicates that the action, role, or opportunity does not require any previous experience from the participant or applicant.
-
C.
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).
-
D.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
-
E.
experiences
Indicates that an entity undergoes, feels, or is affected by a particular event, state, or condition.
- F. None of above. chosen
Provenance (4 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_69a493c53e648190ae8cb76c433fd9a7 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4fcbc04819098d2125518f62ae7 |
completed | March 1, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69a4b2b1f4f88190822598cfd2a0fd2b |
completed | March 1, 2026, 9:42 p.m. |
| PDg | Predicate description generation | batch_69a4b36064a48190b85c402f32cbadd1 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:41 p.m.