Triple
T22430780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Modern No. 20 |
E554492
|
entity |
| Predicate | hasStrokeContrast |
P148157
|
FINISHED |
| Object | high contrast between thick and thin strokes |
—
|
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: high contrast between thick and thin strokes | Statement: [Modern No. 20, hasStrokeContrast, high contrast between thick and thin strokes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrokeContrast Context triple: [Modern No. 20, hasStrokeContrast, high contrast between thick and thin strokes]
-
A.
hasStrokeType
Indicates that an entity is associated with a specific type or classification of stroke.
-
B.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
C.
hasStrokeCountApprox
Indicates an approximate number of strokes associated with writing or drawing the related entity.
-
D.
hasStrokeCount
Indicates the number of strokes required to write a given symbol or character.
-
E.
hasStrokeOrder
Indicates that there is a specific, ordered sequence of strokes used to write or draw the related symbol or character.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a311b148190bdb752f3f067bb3f |
completed | April 29, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69e898a327948190beee5e168006a0a7 |
completed | April 22, 2026, 9:45 a.m. |
| PDg | Predicate description generation | batch_69e8aa39e3388190b659d59948ebf3e6 |
completed | April 22, 2026, 11 a.m. |
Created at: April 16, 2026, 8:47 p.m.