Triple
T19386533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ál-va-ro |
E484949
|
entity |
| Predicate | showsContrastWith |
P123206
|
FINISHED |
| Object | unstressed syllables 'va' and 'ro' |
—
|
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: unstressed syllables 'va' and 'ro' | Statement: [Ál-va-ro, showsContrastWith, unstressed syllables 'va' and 'ro']
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: showsContrastWith Context triple: [Ál-va-ro, showsContrastWith, unstressed syllables 'va' and 'ro']
-
A.
providesContrastWith
chosen
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
B.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
C.
createsContrastIn
Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
-
D.
dramaticContrastWith
Indicates that one entity is presented in a way that sharply emphasizes differences in tone, style, or impact when compared with another entity.
-
E.
contrastUse
Indicates that one entity is used in opposition or distinction to another to highlight differences between them.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61b40d1148190b4fcd9ad56aa6910 |
completed | April 20, 2026, 12:25 p.m. |
| PD | Predicate disambiguation | batch_69e4fd602f008190aa9bc76ae17e4ce1 |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.