Triple
T24997103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Diplomacy and International Relations |
E625600
|
entity |
| Predicate | typicalCareerOutcome |
P55498
|
FINISHED |
| Object | diplomat |
—
|
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: diplomat | Statement: [School of Diplomacy and International Relations, typicalCareerOutcome, diplomat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCareerOutcome Context triple: [School of Diplomacy and International Relations, typicalCareerOutcome, diplomat]
-
A.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
-
B.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
C.
typicalStudentOutcome
Indicates the usual or expected result, achievement, or status that students generally attain under normal conditions.
-
D.
laterCareer
Indicates that the associated information or events pertain to a later stage or phase in an entity’s professional life or career trajectory.
-
E.
careerType
chosen
Indicates the kind or category of professional occupation or career path associated with an entity.
- 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_69e2ff2611c081908710457fbe6d376b |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: April 18, 2026, 6:04 a.m.