Triple
T28582218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DIN 1451 |
E723406
|
entity |
| Predicate | legibilityOptimizedFor |
P164653
|
FINISHED |
| Object | distance reading |
—
|
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: distance reading | Statement: [DIN 1451, legibilityOptimizedFor, distance reading]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legibilityOptimizedFor Context triple: [DIN 1451, legibilityOptimizedFor, distance reading]
-
A.
legibilityLevel
Indicates the degree to which something can be easily read or understood.
-
B.
lessOptimizedFor
Indicates that one entity is designed, configured, or adapted to perform a task or function with lower efficiency or effectiveness compared to another entity.
-
C.
opticalCharacter
Indicates a relationship where an entity functions as or is identified as an optical character (such as a symbol or glyph recognized visually).
-
D.
readable
Indicates that one entity can be read or interpreted by another entity (e.g., a subject has permission or ability to read an object).
-
E.
isEasierToSeeThan
Indicates that one entity is more visually noticeable or discernible than another under comparable viewing conditions.
- 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_69f01d7e97708190ae9e77ee66a68abd |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f650cc74788190aba40de8949079e8 |
completed | May 2, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
| PDg | Predicate description generation | batch_69f64db8ee1881909362701d72ffe282 |
completed | May 2, 2026, 7:17 p.m. |
Created at: April 28, 2026, 4:15 a.m.