Triple
T836700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | She Walks in Beauty |
E18083
|
entity |
| Predicate | usesContrastBetween |
P7994
|
FINISHED |
| Object | light and dark |
—
|
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: light and dark | Statement: [She Walks in Beauty, usesContrastBetween, light and dark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesContrastBetween Context triple: [She Walks in Beauty, usesContrastBetween, light and dark]
-
A.
oftenContrastedWith
Indicates that one entity is frequently compared to another in a way that highlights their differences or opposing characteristics.
-
B.
themeContrast
chosen
Indicates a relationship where two themes are compared or opposed to highlight their differences or tension.
-
C.
usesColorDifferenceSignals
Indicates that one entity employs differences in color as signals to convey information or communicate.
-
D.
usedAgainst
Indicates that one entity is employed, applied, or deployed in opposition to, or for the purpose of affecting, another entity.
-
E.
nameContrastsWith
Indicates that one name is deliberately chosen or used to highlight a difference or opposition in meaning, style, or identity relative to another name.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abcf69888190b342363978273ae2 |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7c7df881909c539c3ab8ff0367 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.