Triple
T23629258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aniva Bay |
E583558
|
entity |
| Predicate | hasFormerLanguageRegion |
P145638
|
FINISHED |
| Object | Japanese language |
—
|
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 language | Statement: [Aniva Bay, hasFormerLanguageRegion, Japanese language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormerLanguageRegion Context triple: [Aniva Bay, hasFormerLanguageRegion, Japanese language]
-
A.
hasTraditionalLanguageRegion
Indicates the geographic region traditionally associated with the use or origin of a particular language.
-
B.
formerLanguageRegion
chosen
Indicates that a region previously used a particular language as significant or dominant, but no longer does so.
-
C.
hasSuccessorLanguageInRegion
Indicates that one language is followed or replaced by another language within a specific geographic region.
-
D.
hasLanguageRegionContext
Indicates that something is associated with or situated within a specific linguistic or language-region context.
-
E.
alsoInLanguageRegion
Indicates that two or more entities are located within or associated with the same language-defined geographic region.
- 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_69e248fc8d74819091bd5baef2f36f6f |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b1e5ae80819085c417e8d81b74ad |
completed | April 29, 2026, 7:23 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:46 p.m.