Triple
T31941825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Échame la Culpa |
E815539
|
entity |
| Predicate | secondaryVocalLanguage |
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: [Échame la Culpa, secondaryVocalLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryVocalLanguage Context triple: [Échame la Culpa, secondaryVocalLanguage, English]
-
A.
hasSecondaryLanguage
chosen
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
B.
secondaryLanguageSupport
Indicates that an entity provides assistance, services, or functionality in an additional (non-primary) language.
-
C.
hasSecondaryNationalLanguage
Indicates that an entity possesses an officially recognized secondary national language in addition to its primary national language.
-
D.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
E.
secondaryLanguageContext
Indicates that the associated information, interaction, or content occurs within or is tailored to a secondary (non-primary) language setting or usage context.
- 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_69f348f42d188190a33fc8d20ec50517 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe6c811bcc81908b1e1b1f8bcb071b |
completed | May 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69fe6c026d5481908b7a814dcf38c183 |
completed | May 8, 2026, 11:04 p.m. |
Created at: May 1, 2026, 12:06 a.m.