Triple
T7778343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Wikipedia |
E221449
|
entity |
| Predicate | hasArticleCountRanking |
P12314
|
FINISHED |
| Object | one of the largest Wikipedia editions by article count |
—
|
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 editions by article count | Statement: [French Wikipedia, hasArticleCountRanking, one of the largest Wikipedia editions by article count]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArticleCountRanking Context triple: [French Wikipedia, hasArticleCountRanking, one of the largest Wikipedia editions by article count]
-
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.
hasRankingCategory
chosen
Indicates that an entity is associated with a particular ranking category or tier within an ordered classification system.
-
E.
hasRankingFactor
Indicates that one entity contributes as a factor to determining the ranking or ordered position of 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_69ca83ebbef881909ac47f789145fef7 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cae7e779ec8190b77296d9c2ac3210 |
completed | March 30, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69caa488532c819093ac40bba0b3c7ef |
completed | March 30, 2026, 4:27 p.m. |
Created at: March 30, 2026, 4:16 p.m.