Triple
T2800445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monica Lewinsky scandal |
E53139
|
entity |
| Predicate | impactOnMedia |
P1782
|
FINISHED |
| Object | rise of 24-hour political scandal coverage |
—
|
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: rise of 24-hour political scandal coverage | Statement: [Monica Lewinsky scandal, impactOnMedia, rise of 24-hour political scandal coverage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnMedia Context triple: [Monica Lewinsky scandal, impactOnMedia, rise of 24-hour political scandal coverage]
-
A.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
-
B.
mediaInfluence
Indicates that one entity affects, shapes, or alters another entity’s attitudes, behaviors, or perceptions through media content or channels.
-
C.
mediaAttentionLevel
Indicates the degree or intensity of attention or coverage that media outlets give to a particular subject or entity.
-
D.
mediaCoverage
chosen
Indicates that one entity reports on, documents, or broadcasts information about another entity through news or media channels.
-
E.
mediaCoverageControversy
Indicates that media coverage is associated with, contributes to, or centers around a controversy involving the related entities.
- 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_69ab495a90788190941b6917e1eca3a6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abde2ec2ac8190bd702ad3eafb6aed |
completed | March 7, 2026, 8:13 a.m. |
| PD | Predicate disambiguation | batch_69abdd059f308190853191f6ffe2bc6f |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:58 p.m.