Triple
T1072155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bariloche |
E23352
|
entity |
| Predicate | distanceFromBuenosAires |
P23952
|
FINISHED |
| Object | approximately 1600 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: approximately 1600 km | Statement: [Bariloche, distanceFromBuenosAires, approximately 1600 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBuenosAires Context triple: [Bariloche, distanceFromBuenosAires, approximately 1600 km]
-
A.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
B.
distanceFromSantiago
Indicates the spatial distance between a given entity and the location of Santiago.
-
C.
distanceToSouthAmerica
Indicates the spatial distance between a given entity’s location and the continent of South America.
-
D.
distanceFromJuanFernandezIslands_km
Indicates the distance, measured in kilometers, between an entity and the Juan Fernández Islands.
-
E.
distanceFromSydney
Indicates the spatial distance between a given location and the city of Sydney.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b9296c5c8190a3060fbfdf24f029 |
completed | March 1, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69a4b73844708190a16c9e9824ca2fb6 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b8d5076481908640a0d873efdf14 |
completed | March 1, 2026, 10:08 p.m. |
Created at: March 1, 2026, 7:42 p.m.