Triple
T4449958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sansei |
E96382
|
entity |
| Predicate | hasLanguageShift |
P4292
|
FINISHED |
| Object | from Japanese to English |
—
|
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: from Japanese to English | Statement: [Sansei, hasLanguageShift, from Japanese to English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageShift Context triple: [Sansei, hasLanguageShift, from Japanese to English]
-
A.
languageShift
chosen
Indicates a change in the primary language used by an entity, such as switching from one language to another over time or in a given context.
-
B.
hasCulturalShift
Indicates a change in the prevailing beliefs, values, norms, or practices within a group, organization, or society over time.
-
C.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
D.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
E.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
- 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_69b345415ba481908df738e7174448ba |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355d5975c8190bfe8a2d5d2dbf075 |
completed | March 13, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69b34f62c180819097ced38da2052207 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:32 p.m.