Triple
T16666372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cahitan |
E404991
|
entity |
| Predicate | closelyRelatedLanguages |
P76213
|
FINISHED |
| Object | Yaqui and Mayo |
—
|
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: Yaqui and Mayo | Statement: [Cahitan, closelyRelatedLanguages, Yaqui and Mayo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: closelyRelatedLanguages Context triple: [Cahitan, closelyRelatedLanguages, Yaqui and Mayo]
-
A.
closelyAssociatedLanguage
Indicates that one language is closely connected to another, such as through frequent co-use, mutual influence, or strong cultural or regional association.
-
B.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
C.
sharesLinguisticFamilyWith
chosen
Indicates that two languages belong to the same linguistic family or branch within a language family.
-
D.
neighboringLanguageFamilies
Indicates that two language families are geographically adjacent or border each other in their primary regions of use.
-
E.
hasNeighboringLanguages
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
- 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_69d8838b5fbc81908c6575c132b82e80 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37c9cd7ec819084aa9b2830874bf5 |
completed | April 18, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69e319b1d7f08190b5ecb4a68c636c15 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:18 a.m.