Triple
T17466943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miguel Ángel |
E425300
|
entity |
| Predicate | usedInCountry |
P715
|
FINISHED |
| Object | Cuba |
—
|
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: Cuba | Statement: [Miguel Ángel, usedInCountry, Cuba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuba Context triple: [Miguel Ángel, usedInCountry, Cuba]
-
A.
Cuba
chosen
Cuba is a Caribbean island nation known for its communist government, historic Havana architecture, classic cars, and influential music and culture.
-
B.
Cuba
Cuba is a municipality in Portugal’s Beja District, known for its rural Alentejo landscape and traditional wine production.
-
C.
Cubão
Cubão is a locality in the state of São Paulo, Brazil, situated near the industrial city of São Bernardo do Campo.
-
D.
Dominican Republic
The Dominican Republic is a Caribbean nation on the island of Hispaniola known for its beaches, mountainous interior, and vibrant blend of Spanish, African, and Taíno cultural influences.
-
E.
Haiti
Haiti is a Caribbean nation on the island of Hispaniola known for its rich Afro-Caribbean culture, history as the first independent Black republic, and frequent vulnerability to natural disasters.
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451a8f4908190a67a3a82a1c8f011 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.