Triple
T17457410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benrath line |
E425065
|
entity |
| Predicate | hasExampleContrast |
P123206
|
FINISHED |
| Object | Low German “maken” vs High German “machen” |
—
|
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: Low German “maken” vs High German “machen” | Statement: [Benrath line, hasExampleContrast, Low German “maken” vs High German “machen”]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExampleContrast Context triple: [Benrath line, hasExampleContrast, Low German “maken” vs High German “machen”]
-
A.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
providesContrastWith
chosen
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
C.
hasMainContrast
Indicates a primary opposing or differing relationship between two elements, highlighting the main point of contrast between them.
-
D.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
E.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45142b08481908cdd290692d796c3 |
completed | April 19, 2026, 3:51 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f0e3fc819094e466b74622c956 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:47 a.m.