Triple
T8914774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NATO Rapid Deployable Corps Spain |
E212268
|
entity |
| Predicate | usesLanguageForOperations |
P78017
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [NATO Rapid Deployable Corps Spain, usesLanguageForOperations, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesLanguageForOperations Context triple: [NATO Rapid Deployable Corps Spain, usesLanguageForOperations, English]
-
A.
languageOfOperation
Indicates the language in which an entity (such as a system, service, or process) primarily operates or functions.
-
B.
hasPrimaryLanguageOfOperations
chosen
Indicates that an entity conducts its main activities or operations primarily using a specified language.
-
C.
usesLanguageRuntime
Indicates that an entity operates using, depends on, or is executed within a specific language runtime environment.
-
D.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
E.
tertiaryLanguageOfOperation
Indicates that an entity uses a specified language as its third most prominent or prioritized language of operation.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc660e620c8190b02b9843c8f02bfa |
completed | April 1, 2026, 12:25 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:56 p.m.