Triple
T16479644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Boquerón |
E400280
|
entity |
| Predicate | distanceFromSanSalvador |
P122938
|
FINISHED |
| Object | approximately 20 kilometers |
—
|
LITERAL 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: approximately 20 kilometers | Statement: [El Boquerón, distanceFromSanSalvador, approximately 20 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSanSalvador Context triple: [El Boquerón, distanceFromSanSalvador, approximately 20 kilometers]
-
A.
distanceFromTegucigalpa
Indicates the spatial distance between a given location and the city of Tegucigalpa.
-
B.
distanceFromManagua
Indicates the spatial distance between a given entity and the location of Managua.
-
C.
distanceToGuatemalaCity
Indicates the spatial distance between a given location and Guatemala City.
-
D.
distanceFromCentralAmerica
Indicates the measured spatial distance between a given entity and the region of Central America.
-
E.
distanceFromMexicoCity
Indicates the measured distance between an entity’s location and Mexico City.
- F. None of above. chosen
Provenance (4 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e01f6c88190b75a0d6c94786426 |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24556c1348190902a4d116c3137d9 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:13 a.m.