Triple
T12933616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Germán |
E309445
|
entity |
| Predicate | usedInCountry |
P715
|
FINISHED |
| Object | Costa Rica |
E16577
|
NE 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: Costa Rica | Statement: [Germán, usedInCountry, Costa Rica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Costa Rica Context triple: [Germán, usedInCountry, Costa Rica]
-
A.
Costa Rica
chosen
Costa Rica is a Central American country renowned for its political stability, rich biodiversity, and strong environmental conservation efforts.
-
B.
Nicaragua
Nicaragua is a Central American country known for its volcanic landscapes, large lakes, and colonial-era architecture.
-
C.
El Salvador
El Salvador is a coastal municipality in the Philippines located along Macajalar Bay in the province of Misamis Oriental.
-
D.
El Salvador
El Salvador is a Central American country known for being the smallest and most densely populated nation in the region, with a history of civil conflict and a recent push toward economic modernization and cryptocurrency adoption.
-
E.
Costa Verde
Costa Verde is a scenic coastal stretch in Lima, Peru, known for its cliffs, beaches, and oceanfront views along the Pacific.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97dc6517481908637781da240b51f |
completed | April 10, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0e0e90881908f6e523754107e75 |
completed | May 3, 2026, 3:28 a.m. |
Created at: April 9, 2026, 5:42 p.m.