Triple
T20775073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mesa de la Cámara de Diputadas y Diputados |
E511334
|
entity |
| Predicate | géneroLenguaje |
P3087
|
FINISHED |
| Object | inclusivo |
—
|
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: inclusivo | Statement: [Mesa de la Cámara de Diputadas y Diputados, géneroLenguaje, inclusivo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: géneroLenguaje Context triple: [Mesa de la Cámara de Diputadas y Diputados, géneroLenguaje, inclusivo]
-
A.
grammaticalGenderInSpanish
Indicates that the entity has the specified grammatical gender (masculine, feminine, or neuter) in the Spanish language.
-
B.
hasGrammaticalGender
chosen
Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
-
C.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
D.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
E.
languageCharacterizedBy
Indicates that a language is defined or distinguished by a particular feature, property, or characteristic.
- 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_69e0b4cac7a48190a715cb3d545df2b4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c26a39bc81909ca5d102056d8586 |
completed | April 21, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69e5c0550ec481908a0877fb2409d983 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:37 p.m.