Triple
T8759189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cayo Conejo (Vieques) |
E208149
|
entity |
| Predicate | hasSecondaryLanguageRegion |
P9103
|
FINISHED |
| Object | 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: English | Statement: [Cayo Conejo (Vieques), hasSecondaryLanguageRegion, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecondaryLanguageRegion Context triple: [Cayo Conejo (Vieques), hasSecondaryLanguageRegion, English]
-
A.
hasSecondaryLanguage
chosen
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
B.
hasSuccessorLanguageInRegion
Indicates that one language is followed or replaced by another language within a specific geographic region.
-
C.
hasSecondaryLanguageTradition
Indicates that an entity possesses an additional, non-primary language tradition associated with it, such as in its use, documentation, or cultural context.
-
D.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
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_69ca835cd6b08190bd7c63db92f53c86 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5df81c58819089af99306e103dbb |
completed | March 31, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1884bc8190a46e8308db31f7ab |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:40 p.m.