Triple
T11648514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yanito |
E276839
|
entity |
| Predicate | languageContactType |
P22730
|
FINISHED |
| Object | Spanish–English contact variety |
—
|
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–English contact variety | Statement: [Yanito, languageContactType, Spanish–English contact variety]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageContactType Context triple: [Yanito, languageContactType, Spanish–English contact variety]
-
A.
languageContactWith
chosen
Indicates a relationship where two or more languages come into contact through their speakers, leading to interaction and potential mutual influence.
-
B.
primaryLanguageContact
Indicates that one language serves as the main or dominant medium of communication in a particular contact situation between language communities.
-
C.
languageGroupSpoken
Indicates that a particular language group is spoken or used for communication by an entity.
-
D.
ethnicLanguageStatus
Indicates the status or role of a language in relation to a particular ethnic group (e.g., primary, secondary, heritage, or minority language).
-
E.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a2cd9bb0819093d107204bed2fe0 |
completed | April 10, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69d85dd94bdc819091fa2ed33eb31624 |
completed | April 10, 2026, 2:18 a.m. |
Created at: April 8, 2026, 9:39 p.m.