Triple
T25243321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Værnes Air Station |
E632527
|
entity |
| Predicate | hasCivilMilitaryCooperation |
P159422
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Værnes Air Station, hasCivilMilitaryCooperation, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCivilMilitaryCooperation Context triple: [Værnes Air Station, hasCivilMilitaryCooperation, yes]
-
A.
hasMilitaryCooperationAt
Indicates that two or more entities engage in military cooperation at or in relation to a specific location or venue.
-
B.
militaryCooperation
Indicates a collaborative relationship in which entities coordinate, support, or jointly conduct military activities, operations, or planning.
-
C.
isCivilMilitary
Indicates that an entity or relationship involves both civilian and military components or functions.
-
D.
hasMilitaryAssociation
Indicates a relationship in which an entity is connected or affiliated with a military organization, activity, or function.
-
E.
hasMilitaryPresence
Indicates that a military force is present in, stationed at, or operating within a particular location or entity.
- 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_69e75a8fdd3881909ba0b05aa5da92a7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f584f07b648190aee894c1d5320bc3 |
completed | May 2, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_69f4a0edd10c81908a052ab864d57c54 |
completed | May 1, 2026, 12:47 p.m. |
| PDg | Predicate description generation | batch_69f55e497fa081909bc59a7b92c5df59 |
completed | May 2, 2026, 2:15 a.m. |
Created at: April 21, 2026, 1:10 p.m.