Triple
T186378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | kishida230 |
E3989
|
entity |
| Predicate | languageOfMostTweets |
P6711
|
FINISHED |
| Object | Japanese |
—
|
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: Japanese | Statement: [kishida230, languageOfMostTweets, Japanese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfMostTweets Context triple: [kishida230, languageOfMostTweets, Japanese]
-
A.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
B.
secondMostSpokenLanguage
Indicates that the related language is the second most widely spoken language associated with the given entity (such as a country, region, or population).
-
C.
deFactoLanguage
Indicates that a language is used in practice as the primary or common language in a context, even if it has no official legal status there.
-
D.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
E.
languageOfSources
Indicates that the specified language is the language in which the referenced sources or source materials are expressed.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a2594809288190b3d3b1283e7e0d00 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25670feb081908e26a2543ebe7b3a |
completed | Feb. 28, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69a257e763d081908c54ad57d8d3060d |
completed | Feb. 28, 2026, 2:50 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.