Triple
T24981342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hohenfels, Germany |
E625173
|
entity |
| Predicate | militaryTrainingType |
P157889
|
FINISHED |
| Object | force-on-force maneuver training |
—
|
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: force-on-force maneuver training | Statement: [Hohenfels, Germany, militaryTrainingType, force-on-force maneuver training]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: militaryTrainingType Context triple: [Hohenfels, Germany, militaryTrainingType, force-on-force maneuver training]
-
A.
militaryTrainingFocus
Indicates that the primary emphasis or specialization of a subject’s military training is on a particular skill, domain, or operational area.
-
B.
navalTrainingType
Indicates the specific kind or category of naval training associated with an entity.
-
C.
hasMilitaryType
Indicates that an entity is associated with or classified under a specific military category, role, or type.
-
D.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
E.
hasMilitarySpeciality
Indicates that an entity possesses a specific military role, skill set, or area of professional expertise within the armed forces.
- 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_69e2ff254570819093d197b1900305ac |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d77f6e88190a4643ab2cbef567b |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 18, 2026, 6:02 a.m.