Triple
T10078079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cayo Yayi (Vieques) |
E213823
|
entity |
| Predicate | hasPrimaryLanguageNearby |
P91957
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Cayo Yayi (Vieques), hasPrimaryLanguageNearby, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryLanguageNearby Context triple: [Cayo Yayi (Vieques), hasPrimaryLanguageNearby, Spanish]
-
A.
hasPrimaryLanguage1
Indicates that an entity’s main or most commonly used language is the specified language.
-
B.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
-
C.
hasNeighboringLanguages
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
-
D.
isLinguaFrancaOf
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
-
E.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd030a0fc819084b523e8e63636fa |
completed | April 2, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 9 p.m.