Triple
T33405126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luther, Obama’s Anger Translator |
E855416
|
entity |
| Predicate | toneContrastWithObama |
P176866
|
FINISHED |
| Object | angry vs calm |
—
|
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: angry vs calm | Statement: [Luther, Obama’s Anger Translator, toneContrastWithObama, angry vs calm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toneContrastWithObama Context triple: [Luther, Obama’s Anger Translator, toneContrastWithObama, angry vs calm]
-
A.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
B.
tempoContrast
Indicates a relationship where two musical passages or sections differ in tempo, highlighting a contrast in their speed or pacing.
-
C.
textureContrast
Indicates a relationship where two surfaces or regions differ noticeably in their tactile or visual texture qualities.
-
D.
contrastEffect
Indicates that one entity’s characteristics are perceived or evaluated differently because they are compared or juxtaposed with another entity.
-
E.
hasToneContrast
Indicates a relationship where two tones differ in pitch, contour, or phonological features such that they form a perceptible tonal contrast.
- F. None of above. chosen
Provenance (4 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_69f3496f04a08190804e56ac5098b8e4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69f6f37f36ac8190b1bff8711d6771cb |
completed | May 3, 2026, 7:04 a.m. |
Created at: May 1, 2026, 1:36 a.m.