Triple
T33279133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Lakes, Illinois |
E851988
|
entity |
| Predicate | hasBootCamp |
P193671
|
FINISHED |
| Object | U.S. Navy Recruit Training Command |
—
|
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: U.S. Navy Recruit Training Command | Statement: [Great Lakes, Illinois, hasBootCamp, U.S. Navy Recruit Training Command]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBootCamp Context triple: [Great Lakes, Illinois, hasBootCamp, U.S. Navy Recruit Training Command]
-
A.
hasTrainingTrack
Indicates that an entity is associated with or assigned to a specific training track or program.
-
B.
hasTrainingFor
Indicates that an entity has received or possesses training that prepares it for performing a specific task, role, or function.
-
C.
hasTutorialIn
Indicates that one entity provides or includes a tutorial within the context or medium of another entity.
-
D.
hasTrainingMedium
Indicates that an entity uses or is associated with a particular medium, format, or environment for training.
-
E.
hasHandsOnTraining
Indicates that an entity has received practical, experiential instruction or practice in performing a specific task or activity.
- 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_69f349653da08190819876015a298fdb |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69fd4f38728c8190b3271abc80882cfb |
completed | May 8, 2026, 2:49 a.m. |
Created at: May 1, 2026, 1:32 a.m.