Triple
T12709227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jodey Arrington |
E303669
|
entity |
| Predicate | opposed policy |
P100421
|
FINISHED |
| Object | large federal deficits |
—
|
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: large federal deficits | Statement: [Jodey Arrington, opposed policy, large federal deficits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opposed policy Context triple: [Jodey Arrington, opposed policy, large federal deficits]
-
A.
opposedPolicyContext
chosen
Indicates that an entity opposed a specific policy within a particular situational, temporal, or institutional context.
-
B.
opposedPolicyOfPredecessor
Indicates that one entity actively rejected, resisted, or worked against the policies established by its predecessor.
-
C.
opposedBy
Indicates that one entity actively resists, disagrees with, or works against the actions, views, or position of another entity.
-
D.
opposition
Indicates a relationship in which one entity actively resists, disagrees with, or is positioned against another entity, idea, or action.
-
E.
opposedOutcome
Indicates that one entity’s outcome is in conflict with, counters, or works against the outcome associated with 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96207b2d881908314efc3e350aa78 |
completed | April 10, 2026, 8:48 p.m. |
| PD | Predicate disambiguation | batch_69d960c088dc8190b0e63312c54e4c6c |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:23 p.m.