Triple
T24408455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premio FIL de Literatura en Lenguas Romances |
E615376
|
entity |
| Predicate | regiónCultural |
P1968
|
FINISHED |
| Object | América Latina |
—
|
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: América Latina | Statement: [Premio FIL de Literatura en Lenguas Romances, regiónCultural, América Latina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regiónCultural Context triple: [Premio FIL de Literatura en Lenguas Romances, regiónCultural, América Latina]
-
A.
culturalRegion
chosen
Indicates that an entity is located in, associated with, or belongs to a specific cultural region or cultural area.
-
B.
namedForCulturalRegion
Indicates that something is given a name derived from or honoring a specific cultural region.
-
C.
isCulturalArea
Indicates that a given area is recognized or designated as a cultural region, characterized by shared cultural traits, practices, or heritage.
-
D.
regionHasCulturalHeritage
Indicates that a specific region possesses, contains, or is associated with particular cultural heritage.
-
E.
regionOfCulturalImpact
Indicates the geographic area where an entity’s cultural influence, activities, or effects are most significantly felt or observed.
- 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_69e2d7e780bc81908049c779e697a7f6 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2957fab0481909121c6da6b5e34c0 |
completed | April 29, 2026, 11:34 p.m. |
| PD | Predicate disambiguation | batch_69f287cc4fd4819081e93cc638d9512d |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 2:05 a.m.