Triple
T4462276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naval Station Great Lakes |
E98282
|
entity |
| Predicate | trainingPopulation |
P55564
|
FINISHED |
| Object | tens of thousands of recruits annually |
—
|
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: tens of thousands of recruits annually | Statement: [Naval Station Great Lakes, trainingPopulation, tens of thousands of recruits annually]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingPopulation Context triple: [Naval Station Great Lakes, trainingPopulation, tens of thousands of recruits annually]
-
A.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
B.
trainingDatasetSize
Indicates the number of data samples or instances used to train a model or system.
-
C.
trainingSetSize
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
D.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
E.
trainingDataSource
Indicates the origin or provider from which the training data for a model or system is obtained.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567716a4819092a5bc9732e74592 |
completed | March 13, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69b34f65f6448190abfadb2ae5658798 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff7018c81908ad8597e525c042b |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:34 p.m.