Triple
T2690698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chūichi Hara |
E58393
|
entity |
| Predicate | typeOfCareer |
P24248
|
FINISHED |
| Object | military career |
—
|
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: military career | Statement: [Chūichi Hara, typeOfCareer, military career]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCareer Context triple: [Chūichi Hara, typeOfCareer, military career]
-
A.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
-
B.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
C.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
-
D.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
E.
partOfCareer
Indicates that one entity represents a role, position, or period that forms a component or phase within another entity’s overall career.
- 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_69ab4ac269e481909cb317d79e68b75b |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda0ba2208190ad87763ecbef8c3c |
completed | March 7, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69abd81c9b4c81908e5e0da6ac5f828b |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.