Triple
T28627495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Wikipedia |
E724561
|
entity |
| Predicate | hasArticleCountRank |
P180616
|
FINISHED |
| Object | one of the largest Wikipedia language editions |
—
|
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: one of the largest Wikipedia language editions | Statement: [German Wikipedia, hasArticleCountRank, one of the largest Wikipedia language editions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArticleCountRank Context triple: [German Wikipedia, hasArticleCountRank, one of the largest Wikipedia language editions]
-
A.
articleCount
Indicates the number of articles associated with a given entity or context.
-
B.
articleCountApprox
Indicates that the relationship specifies an approximate number of articles associated with an entity.
-
C.
hasCanonicalNumberOfArticles
Indicates that an entity is associated with a standard, officially recognized count of articles that define or describe it.
-
D.
containsArticle
Indicates that one entity includes or holds an article (such as a written piece, item, or document) as part of its contents.
-
E.
rankComparedToCount
Indicates how an entity’s rank or ordering position compares relative to a specified total count or number of items.
- 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_69f01d822ac08190932de59ec2268ed2 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
| PDg | Predicate description generation | batch_69f74654c09c819084879162eba9d641 |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 28, 2026, 4:36 a.m.