Triple
T29959697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cold Shoulder |
E761006
|
entity |
| Predicate | aestheticEffect |
P16366
|
FINISHED |
| Object | emphasizes shoulders |
—
|
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: emphasizes shoulders | Statement: [Cold Shoulder, aestheticEffect, emphasizes shoulders]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aestheticEffect Context triple: [Cold Shoulder, aestheticEffect, emphasizes shoulders]
-
A.
visualEffect
chosen
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
B.
resolutionEffect
Indicates the outcome, consequence, or change that results from a particular resolution, decision, or problem-solving action.
-
C.
landscapeEffect
Indicates how a particular landscape or terrain influences or modifies the outcome, behavior, or characteristics of another entity or process.
-
D.
contrastEffect
Indicates that one entity’s characteristics are perceived or evaluated differently because they are compared or juxtaposed with another entity.
-
E.
specialEffectsBy
Indicates that the special effects for something (such as a film, scene, or shot) are created or provided by a particular person 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_69f22466327481908ba6db916837bece |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6783d1944819080f26d4d2fdd1256 |
completed | May 2, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:28 p.m.