Triple
T5343333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reglamento de la Cámara de Diputadas y Diputados |
E123992
|
entity |
| Predicate | idiomaOriginal |
P5459
|
FINISHED |
| Object | español |
—
|
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: español | Statement: [Reglamento de la Cámara de Diputadas y Diputados, idiomaOriginal, español]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: idiomaOriginal Context triple: [Reglamento de la Cámara de Diputadas y Diputados, idiomaOriginal, español]
-
A.
originalTextLanguage
chosen
Indicates the language in which a text was originally written or created before any translation or adaptation.
-
B.
originalLanguagePhrase
Indicates that one phrase is the original-language version from which another phrase (typically a translation or adaptation) is derived.
-
C.
originalLanguageContext
Indicates the language in which something was first created or expressed, providing the original linguistic context for its content or meaning.
-
D.
originalLanguageCountry
Indicates the country where a work’s original language is primarily spoken or officially used.
-
E.
originalLanguageOfFilmOrTVShow
Indicates the language in which a film or TV show was originally produced and released.
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85e86edc81908d87933db6489f91 |
completed | March 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69bd845a62b081909782863865b257a9 |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:01 p.m.