Triple
T7148017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dean Scream speech |
E166617
|
entity |
| Predicate | mediaEffect |
P17824
|
FINISHED |
| Object | framing Howard Dean as overly emotional |
—
|
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: framing Howard Dean as overly emotional | Statement: [Dean Scream speech, mediaEffect, framing Howard Dean as overly emotional]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaEffect Context triple: [Dean Scream speech, mediaEffect, framing Howard Dean as overly emotional]
-
A.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
B.
mediaUse
Indicates that an entity makes use of, consumes, or engages with a particular medium or media resource.
-
C.
mediaMoment
Indicates a specific point or segment within a media item (such as a video, audio, or broadcast) where a notable event or action occurs.
-
D.
mediaDepictionAs
chosen
Indicates that one entity is portrayed or represented as another entity or in a particular way within some medium (e.g., image, film, text).
-
E.
videoModulation
Indicates a relationship where one entity alters or controls the characteristics of a video signal or stream, such as its amplitude, frequency, or encoding parameters.
- 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_69c68886779c8190a8e3fbabffe68253 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e7d6313c8190bdd34e700fc65502 |
completed | March 27, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69c6e1caf4e48190b47bb398a3c1554d |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:46 p.m.