Triple
T31250993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Department of Cauca |
E796815
|
entity |
| Predicate | recognizedRegionalLanguages |
P2982
|
FINISHED |
| Object | Nasa Yuwe |
—
|
NE NERFINISHED |
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: Nasa Yuwe | Statement: [Department of Cauca, recognizedRegionalLanguages, Nasa Yuwe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recognizedRegionalLanguages Context triple: [Department of Cauca, recognizedRegionalLanguages, Nasa Yuwe]
-
A.
recognizedRegionalLanguage
chosen
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
B.
alsoInLanguageRegion
Indicates that two or more entities are located within or associated with the same language-defined geographic region.
-
C.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
D.
subjectLanguageRegion
Indicates that the subject is associated with or uses a language specific to a particular geographic region.
-
E.
languageRegionsRepresented
Indicates that certain geographic or cultural regions are represented or covered through specific 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_69f224dc84d0819081f1cb6f9127e6b1 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7b5ccbda481908fe1945c35e36ce8 |
completed | May 3, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
Created at: April 29, 2026, 9:11 p.m.