Triple
T10390043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eyak |
E244865
|
entity |
| Predicate | languageRevitalizationEffort |
P4252
|
FINISHED |
| Object | Eyak language documentation projects |
—
|
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: Eyak language documentation projects | Statement: [Eyak, languageRevitalizationEffort, Eyak language documentation projects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageRevitalizationEffort Context triple: [Eyak, languageRevitalizationEffort, Eyak language documentation projects]
-
A.
hasLanguageRevitalizationEfforts
chosen
Indicates that there are organized actions or initiatives aimed at preserving, strengthening, or reviving the use of a particular language.
-
B.
languageRevivalMethod
Indicates the method or strategy used to revive or revitalize a language that is endangered, dormant, or no longer actively spoken.
-
C.
languageRevived
Indicates that a previously endangered or no-longer-spoken language has been brought back into active use within a community.
-
D.
languageAdvocated
Indicates that an entity actively supports, promotes, or argues in favor of the use or adoption of a particular language.
-
E.
languageReform
Indicates efforts or actions aimed at changing, standardizing, or improving aspects of a language, such as its spelling, grammar, or usage rules.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b4f7d08190bcb16d3b4c8f22ad |
completed | April 7, 2026, 11:25 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb0e7a88190bec0b7a52c70dfe2 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 12:05 p.m.