Triple
T235186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Look defense policy |
E4493
|
entity |
| Predicate | typeOfDeterrence |
P1135
|
FINISHED |
| Object | nuclear deterrence by punishment |
—
|
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: nuclear deterrence by punishment | Statement: [New Look defense policy, typeOfDeterrence, nuclear deterrence by punishment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfDeterrence Context triple: [New Look defense policy, typeOfDeterrence, nuclear deterrence by punishment]
-
A.
typeOfDefense
chosen
Indicates the specific kind or category of defense employed or possessed in a given context.
-
B.
punishedBy
Indicates that an entity receives punishment administered by another entity.
-
C.
decisionType
Indicates the specific category or nature of a decision associated with an entity or event.
-
D.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
-
E.
crimeType
Indicates the specific category or nature of the crime associated with an event or 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25cc9ab2c81909af278a07f86aa1e |
completed | Feb. 28, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69a25b5dc640819092669575731c393f |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.