Triple
T17111831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latn |
E415243
|
entity |
| Predicate | isMostWidelyUsedScriptByNumberOfLanguages |
P125991
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Latn, isMostWidelyUsedScriptByNumberOfLanguages, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMostWidelyUsedScriptByNumberOfLanguages Context triple: [Latn, isMostWidelyUsedScriptByNumberOfLanguages, true]
-
A.
isMostWidelyUsedWritingSystem
Indicates that the subject writing system is used by more people or in more contexts than any other writing system.
-
B.
associatedLanguageScript
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
C.
languageOfScriptPromoted
Indicates that a particular language is associated with and promoted through the use of a given writing script.
-
D.
hasWritingSystemForMajorLanguage
Indicates that there exists a writing system used to represent a major language associated with the given entity.
-
E.
numberOfMajorLanguages
Indicates the total count of major languages associated with a given entity.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2bab0881908339ec7fb3ebe7e9 |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e37542d060819082aa73948eb8ebd4 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:35 a.m.