Triple
T5582000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puerto Iguazú |
E146660
|
entity |
| Predicate | distanceToIguazuFallsApprox |
P64593
|
FINISHED |
| Object | about 18 km |
—
|
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: about 18 km | Statement: [Puerto Iguazú, distanceToIguazuFallsApprox, about 18 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToIguazuFallsApprox Context triple: [Puerto Iguazú, distanceToIguazuFallsApprox, about 18 km]
-
A.
distanceFromVictoriaFalls
Indicates the measured distance between a given location and Victoria Falls.
-
B.
distanceToSouthAmerica
Indicates the spatial distance between a given entity’s location and the continent of South America.
-
C.
distanceFromPotosiApproximate
Indicates an approximate measure of how far something is from Potosi.
-
D.
distanceToLaPlata
Indicates the measured or calculated distance between a given entity and the location La Plata.
-
E.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
- 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_69c0090287a08190b4098411effe970c |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0208333f08190bf0049b6bdd280f5 |
completed | March 22, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69c01b147cc081909237f3f2967d4cb8 |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f0684908190ae2d14f0bd2ab892 |
completed | March 22, 2026, 4:55 p.m. |
Created at: March 22, 2026, 3:37 p.m.