Triple
T24507801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joint Task Force–National Capital Region |
E606121
|
entity |
| Predicate | usesForcesFrom |
P156551
|
FINISHED |
| Object | United States Army |
—
|
NE NERFINISHED |
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: United States Army | Statement: [Joint Task Force–National Capital Region, usesForcesFrom, United States Army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesForcesFrom Context triple: [Joint Task Force–National Capital Region, usesForcesFrom, United States Army]
-
A.
usesForces
Indicates that one entity applies physical, magical, or other types of forces to influence, move, or affect another entity.
-
B.
ledForcesFrom
Indicates that one entity commanded or directed military or armed forces originating from another entity or location.
-
C.
componentForces
Indicates that a force is decomposed into its constituent directional components that together produce the original overall force.
-
D.
sendsForcesTo
Indicates that one entity dispatches or deploys military or security forces to another entity or location.
-
E.
hasTypicalForces
Indicates that an entity is associated with the characteristic or commonly occurring forces that typically act on it in a given context.
- 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_69e2c4c725148190a4e41577c5cb409c |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
| PDg | Predicate description generation | batch_69f2a9d795288190916368e3cec1f666 |
completed | April 30, 2026, 1:01 a.m. |
Created at: April 18, 2026, 2:23 a.m.