Triple
T5149139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Maritime Sciences, Kobe University |
E116147
|
entity |
| Predicate | trainsForCareerIn |
P14268
|
FINISHED |
| Object | merchant shipping |
—
|
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: merchant shipping | Statement: [Faculty of Maritime Sciences, Kobe University, trainsForCareerIn, merchant shipping]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsForCareerIn Context triple: [Faculty of Maritime Sciences, Kobe University, trainsForCareerIn, merchant shipping]
-
A.
trainsForOccupation
chosen
Indicates that an entity undergoes training or preparation aimed at qualifying for or performing a specific occupation.
-
B.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
C.
careerTriples
Indicates a relationship that links an individual to key career-related facts, such as their roles, employers, or professional milestones.
-
D.
studCareer
Indicates that a student is pursuing or associated with a particular academic or professional career path.
-
E.
studCareerBegan
Indicates that a student's professional or academic career started at a specified time or institution.
- 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_69bd4446c0e08190a7c29dc74976bf03 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd78d7f4d081908d59adcd86f52f1d |
completed | March 20, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69bd77ae2f10819098bb8939106e1281 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:43 p.m.