Triple
T15682257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Winger |
E377605
|
entity |
| Predicate | settingOfTraining |
P76860
|
FINISHED |
| Object | fictional Army training camp |
—
|
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: fictional Army training camp | Statement: [John Winger, settingOfTraining, fictional Army training camp]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfTraining Context triple: [John Winger, settingOfTraining, fictional Army training camp]
-
A.
providesTrainingSetting
chosen
Indicates that one entity serves as the environment or context in which training or educational activities are conducted for another entity.
-
B.
trainingModality
Indicates the method or format through which training or instruction is delivered or conducted.
-
C.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
D.
trainingSystem
Indicates a system or framework used to train, instruct, or develop skills or knowledge in a target entity.
-
E.
trainingUnder
Indicates that one entity is receiving instruction, guidance, or mentorship from another, typically in a subordinate or apprentice-like capacity.
- 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_69d85cd2e28481909d4e975bee20872f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04f306a1c8190a819541a3cc51f5a |
completed | April 16, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69deda8c856c8190882330114f9a1a5f |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:16 a.m.