Triple
T11946546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wesley Hunt |
E284312
|
entity |
| Predicate | hasMilitaryEducation |
P38925
|
FINISHED |
| Object | officer training at West Point |
—
|
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: officer training at West Point | Statement: [Wesley Hunt, hasMilitaryEducation, officer training at West Point]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryEducation Context triple: [Wesley Hunt, hasMilitaryEducation, officer training at West Point]
-
A.
containsMilitaryAcademy
Indicates that one entity includes or hosts a military academy within its boundaries or domain.
-
B.
isMilitarySchool
Indicates that an educational institution functions as a military school, operating under military principles, structure, or training programs.
-
C.
hasMilitarySpeciality
Indicates that an entity possesses a specific military role, skill set, or area of professional expertise within the armed forces.
-
D.
militaryBackground
chosen
Indicates that an entity has prior or current experience, service, or training in a military organization.
-
E.
hasMilitaryBranch
Indicates that an entity is associated with, served in, or is part of a specific branch of a military organization.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903456ec0819082b8b10755a6b732 |
completed | April 10, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.