Triple
T17480821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maldonado Department |
E425652
|
entity |
| Predicate | hasResortArea |
P10436
|
FINISHED |
| Object | La Barra |
—
|
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: La Barra | Statement: [Maldonado Department, hasResortArea, La Barra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Barra Context triple: [Maldonado Department, hasResortArea, La Barra]
-
A.
La Barra
La Barra is the natural volcanic reef that shelters Las Canteras Beach in Las Palmas de Gran Canaria, creating its calm, protected waters.
-
B.
La Barra
chosen
La Barra is a popular seaside resort town in Uruguay known for its beaches, nightlife, and proximity to Punta del Este.
-
C.
Barra Vieja
Barra Vieja is a coastal village and beach area near Acapulco in the Mexican state of Guerrero, known for its long sandy shoreline and seafood restaurants.
-
D.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
-
E.
Barra
Barra is the surname of Mary Barra, the prominent American business executive and CEO of General Motors.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451bfd75481908c20bc2c1cbff593 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:48 a.m.