Triple
T1239977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GWOT |
E26635
|
entity |
| Predicate | policyDimension |
P7917
|
FINISHED |
| Object | military intervention |
—
|
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: military intervention | Statement: [GWOT, policyDimension, military intervention]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyDimension Context triple: [GWOT, policyDimension, military intervention]
-
A.
policyLevel
Indicates the degree or tier of strictness, scope, or priority associated with a given policy.
-
B.
policyDomain
chosen
Indicates the thematic or subject-matter area to which a given policy, rule, or regulatory action belongs.
-
C.
policyContext
Indicates the situational or regulatory framework within which a policy is defined, interpreted, or applied.
-
D.
policyStance
Indicates the position or viewpoint an entity holds regarding a specific policy or set of policies.
-
E.
dimension
Indicates that one entity specifies a measurable extent or size attribute (such as length, width, height, or similar quantitative property) of another entity.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf41c5d08190b07adbdb24d35a76 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.