Triple
T6161240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Greely, Alaska |
E137446
|
entity |
| Predicate | hasTrainingEnvironment |
P69125
|
FINISHED |
| Object | arctic and subarctic conditions |
—
|
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: arctic and subarctic conditions | Statement: [Fort Greely, Alaska, hasTrainingEnvironment, arctic and subarctic conditions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainingEnvironment Context triple: [Fort Greely, Alaska, hasTrainingEnvironment, arctic and subarctic conditions]
-
A.
hasTrainingFor
Indicates that an entity has received or possesses training that prepares it for performing a specific task, role, or function.
-
B.
hasTrained
Indicates that one entity has provided training or instruction to another entity.
-
C.
hasTrainingComplex
Indicates that an entity possesses or is associated with a dedicated facility or complex used for training activities.
-
D.
hasTrainingFunction
Indicates that one entity serves as a training function or mechanism for another entity.
-
E.
hasTrainingRole
Indicates that an entity holds or is assigned a specific role within a training or instructional 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_69c008a54fc88190b6ce4416490ca79d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d35b2f88190abb9b90b5971e6d7 |
completed | March 22, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69c055f5b81481908819515cdc334ae6 |
completed | March 22, 2026, 8:49 p.m. |
| PDg | Predicate description generation | batch_69c056df95ac8190bc5efe050d3af864 |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:17 p.m.