Triple
T30064083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald L. Kirkpatrick |
E763977
|
entity |
| Predicate | modelUsedIn |
P158185
|
FINISHED |
| Object | corporate training evaluation |
—
|
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: corporate training evaluation | Statement: [Donald L. Kirkpatrick, modelUsedIn, corporate training evaluation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelUsedIn Context triple: [Donald L. Kirkpatrick, modelUsedIn, corporate training evaluation]
-
A.
modelIn
chosen
Indicates that one entity serves as a representation or simulation of another entity.
-
B.
engineUsedIn
Indicates that a particular engine is utilized as a component or power source within a specified system, device, or application.
-
C.
usedInDeviceModel
Indicates that something (such as a component, material, or technology) is utilized within or incorporated into a particular device model.
-
D.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
E.
hardwareUsedBy
Indicates that a piece of hardware is utilized or operated by a particular entity (such as a person, system, or organization).
- 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_69f2247221388190a13a22c47094a0ef |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67ca5a1008190880f6da9f69c263b |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f673c664f08190b4d66cdc305e10db |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 6:58 p.m.