Triple
T208314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Global War on Terrorism |
E4655
|
entity |
| Predicate | hasLongTermEffect |
P812
|
FINISHED |
| Object | restructuring of U.S. national security policy |
—
|
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: restructuring of U.S. national security policy | Statement: [Global War on Terrorism, hasLongTermEffect, restructuring of U.S. national security policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLongTermEffect Context triple: [Global War on Terrorism, hasLongTermEffect, restructuring of U.S. national security policy]
-
A.
hasConsequence
chosen
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
B.
hasLegalEffect
Indicates that an action, document, or condition produces recognized legal consequences or enforceable rights and obligations.
-
C.
primaryEffect
Indicates the main direct outcome or consequence that results from a given cause, action, or condition.
-
D.
hasEnvironmentalImpactOn
Indicates that one entity affects or alters the environmental conditions, quality, or ecological state of another entity.
-
E.
hasLongTermDatasetSince
Indicates that an entity has maintained or used a particular dataset continuously starting from a specified point in time.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25e8b9b908190b69a3f0594b95f7e |
completed | Feb. 28, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69a25b4e3c2881908d83e8218aa9f2d9 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.