Triple
T1304880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruta CH-31 (Chile) |
E27851
|
entity |
| Predicate | languageOfDesignation |
P15
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Ruta CH-31 (Chile), languageOfDesignation, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfDesignation Context triple: [Ruta CH-31 (Chile), languageOfDesignation, Spanish]
-
A.
languageDesigned
Indicates that one entity created or developed the language used or associated with another entity.
-
B.
languageOfWorkOrName
chosen
Indicates the language in which a work is created or a name is expressed.
-
C.
officialLanguageOfNomination
Indicates the language officially used in the nomination process or documentation for a given entity.
-
D.
macrolanguageOf
Indicates that one language functions as a macrolanguage encompassing or grouping together one or more related individual languages.
-
E.
historicallyDominantLanguageOfAdministrationIn
Indicates that a language has historically been the primary language used for official governance and administrative functions within a given place or political entity.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c13393388190b784d6bd8c447dda |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.