Triple
T14513276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mars Gradivus |
E340450
|
entity |
| Predicate | hasAspectContrast |
P100967
|
FINISHED |
| Object | peaceful civic aspect of Mars |
—
|
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: peaceful civic aspect of Mars | Statement: [Mars Gradivus, hasAspectContrast, peaceful civic aspect of Mars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAspectContrast Context triple: [Mars Gradivus, hasAspectContrast, peaceful civic aspect of Mars]
-
A.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
B.
textureContrast
Indicates a relationship where two surfaces or regions differ noticeably in their tactile or visual texture qualities.
-
C.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
D.
contrastEffect
chosen
Indicates that one entity’s characteristics are perceived or evaluated differently because they are compared or juxtaposed with another entity.
-
E.
albedoContrast
Indicates the degree to which two surfaces or regions differ in their reflectivity (albedo), typically highlighting contrast in brightness.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de9a6c6054819086b4c0ce1d83fdc5 |
completed | April 14, 2026, 7:50 p.m. |
| PD | Predicate disambiguation | batch_69de5c4ccba08190a988bfda0bc9f5cb |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:21 a.m.