Triple
T12961056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred Vincy |
E310142
|
entity |
| Predicate | acceptsCareerPath |
P107711
|
FINISHED |
| Object | work in land management |
—
|
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: work in land management | Statement: [Fred Vincy, acceptsCareerPath, work in land management]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: acceptsCareerPath Context triple: [Fred Vincy, acceptsCareerPath, work in land management]
-
A.
careerPath
Indicates the progression or sequence of roles, positions, or occupations that an individual follows over time in their professional life.
-
B.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
C.
supportedCareerOf
Indicates that one entity provided assistance, resources, or endorsement that helped establish or advance another entity’s career.
-
D.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
E.
hasCareerTrack
Indicates that an entity is associated with or follows a particular career path or professional progression.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 5:44 p.m.