Triple
T25964770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dean Scream |
E645635
|
entity |
| Predicate | perceivedImpact |
P22974
|
FINISHED |
| Object | contributed to decline of Howard Dean's campaign |
—
|
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: contributed to decline of Howard Dean's campaign | Statement: [Dean Scream, perceivedImpact, contributed to decline of Howard Dean's campaign]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: perceivedImpact Context triple: [Dean Scream, perceivedImpact, contributed to decline of Howard Dean's campaign]
-
A.
influencedPerceptionOf
chosen
Indicates that one entity has affected, shaped, or altered how another entity is perceived or understood.
-
B.
exportImpact
Indicates the effect or consequences that an entity’s exports have on another entity, system, or context.
-
C.
encodingImpact
Indicates how one encoding or encoding choice affects, modifies, or constrains another process, representation, or outcome.
-
D.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
E.
impactOnSubject
Indicates the effect, influence, or consequence that one entity, event, or action has on a specified subject.
- 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_69e77e85efc08190997da7fcf98bd300 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f627aedf548190bc9f53c8a2d67b50 |
completed | May 2, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69f623a4e1048190bbb8dd1253fdcee9 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 22, 2026, 8:48 a.m.