Triple
T1239696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Eustis, Virginia |
E26629
|
entity |
| Predicate | militaryInstallationType |
P25868
|
FINISHED |
| Object | training base |
—
|
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: training base | Statement: [Fort Eustis, Virginia, militaryInstallationType, training base]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: militaryInstallationType Context triple: [Fort Eustis, Virginia, militaryInstallationType, training base]
-
A.
nearMilitaryInstallation
Indicates that one entity is located in close physical proximity to a military installation or facility.
-
B.
garrisonType
Indicates the specific kind or classification of military garrison associated with an entity.
-
C.
militaryOrganization
Indicates that an entity functions as, or is associated with, a structured armed forces or defense-related organization.
-
D.
hasMilitaryBase
Indicates that one entity possesses, hosts, or contains a military base associated with or located on another entity.
-
E.
militarySystem
Indicates a relationship where an entity functions as, belongs to, or is characterized by a particular military organizational or operational system.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf41c5d08190b07adbdb24d35a76 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bce611ec819092cb13d354d0903e |
completed | March 1, 2026, 10:25 p.m. |
Created at: March 1, 2026, 7:47 p.m.