Triple
T1267688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Army Infantry School |
E15637
|
entity |
| Predicate | occasionallyTrains |
P788
|
FINISHED |
| Object | allied and partner nation infantry personnel |
—
|
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: allied and partner nation infantry personnel | Statement: [U.S. Army Infantry School, occasionallyTrains, allied and partner nation infantry personnel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occasionallyTrains Context triple: [U.S. Army Infantry School, occasionallyTrains, allied and partner nation infantry personnel]
-
A.
trains
chosen
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
B.
notableTrain
Indicates that there is a train or rail service associated with the subject that is considered notable or significant in some way.
-
C.
peakDailyTrains
Indicates the maximum number of trains operating per day on a given route, line, or segment during its busiest period.
-
D.
trainsForOccupation
Indicates that an entity undergoes training or preparation aimed at qualifying for or performing a specific occupation.
-
E.
railwayTraffic
Indicates the presence, flow, or management of train movements along railway lines between locations.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c0396e048190b4e2d7aab19268b3 |
completed | March 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69a4bede52a081909665d60acbe41d31 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.