Triple
T36523650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Army Tactical Training Center |
E900241
|
entity |
| Predicate | includesTrainingAreaType |
P36978
|
FINISHED |
| Object | field training areas |
—
|
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: field training areas | Statement: [Army Tactical Training Center, includesTrainingAreaType, field training areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesTrainingAreaType Context triple: [Army Tactical Training Center, includesTrainingAreaType, field training areas]
-
A.
includesAreaType
chosen
Indicates that one entity encompasses or contains another entity of a specified area type within its scope or boundaries.
-
B.
hasTrainingType
Indicates that an entity is associated with or characterized by a specific type or category of training.
-
C.
trainingLocationType
Indicates the type or category of place where a training activity occurs.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasStudyAreaType
Indicates that an entity’s study area is classified as a specific type or category (e.g., lab, field site, classroom).
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.