Triple
T12672944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ley 397 de 1997 |
E302731
|
entity |
| Predicate | idiomaOficialDelTexto |
P236
|
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: [Ley 397 de 1997, idiomaOficialDelTexto, español]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: idiomaOficialDelTexto Context triple: [Ley 397 de 1997, idiomaOficialDelTexto, español]
-
A.
idiomasOficiales
Indicates that one or more languages are officially recognized or designated for use by a given entity (such as a country, region, or institution).
-
B.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
-
C.
officialLanguage
chosen
Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
-
D.
shareOfficialLanguage
Indicates that two entities have at least one official language in common.
-
E.
macrolanguageOf
Indicates that one language functions as a macrolanguage encompassing or grouping together one or more related individual 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961af991c8190b6079cb57e593b8f |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:20 p.m.