Triple
T7722193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nahuatl |
E175038
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | El Salvador |
E1657
|
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: El Salvador | Statement: [Nahuatl, region, El Salvador]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: El Salvador Context triple: [Nahuatl, region, El Salvador]
-
A.
El Salvador
chosen
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.
-
B.
Honduras
Honduras is a Central American country known for its mountainous terrain, Caribbean and Pacific coastlines, and rich Mayan and colonial heritage.
-
C.
Nicaragua
Nicaragua is a Central American country known for its volcanic landscapes, large lakes, and colonial-era architecture.
-
D.
Guatemala
Guatemala is a Central American country known for its Mayan heritage, volcanic landscapes, and vibrant indigenous cultures.
-
E.
Costa Rica
Costa Rica is a Central American country renowned for its political stability, rich biodiversity, and strong environmental conservation efforts.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702f1786881908b025d8986e5f1fa |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d6b1f7648190a0e2fd82ecb9c8b9 |
completed | March 29, 2026, 7:37 a.m. |
Created at: March 27, 2026, 4:05 p.m.