Triple
T2767456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LC2 Petit Modèle |
E61369
|
entity |
| Predicate | visualContrast |
P40635
|
FINISHED |
| Object | soft cushions vs. rigid frame |
—
|
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: soft cushions vs. rigid frame | Statement: [LC2 Petit Modèle, visualContrast, soft cushions vs. rigid frame]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualContrast Context triple: [LC2 Petit Modèle, visualContrast, soft cushions vs. rigid frame]
-
A.
themeContrast
Indicates a relationship where two themes are compared or opposed to highlight their differences or tension.
-
B.
createsContrastIn
chosen
Indicates a relationship where one element is used to highlight or emphasize differences with another element within a given context.
-
C.
hasMainContrast
Indicates a primary opposing or differing relationship between two elements, highlighting the main point of contrast between them.
-
D.
contrastCapability
Indicates a relationship where one entity’s capabilities are compared or set in opposition to another’s, highlighting differences in what they can do or achieve.
-
E.
usesColorDifferenceSignals
Indicates that one entity employs differences in color as signals to convey information or communicate.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdcfc5e1c8190a5ac2c48d3eaeb0a |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.