Triple
T21682901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dina Huapi |
E535155
|
entity |
| Predicate | distanceToSanCarlosDeBariloche |
P68554
|
FINISHED |
| Object | about 15 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 15 km | Statement: [Dina Huapi, distanceToSanCarlosDeBariloche, about 15 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSanCarlosDeBariloche Context triple: [Dina Huapi, distanceToSanCarlosDeBariloche, about 15 km]
-
A.
distanceFromBariloche
chosen
Indicates the measured distance between a given entity or location and Bariloche.
-
B.
distanceToNeuquénCity
Indicates the spatial distance between a given location and the city of Neuquén.
-
C.
distanceToBahíaBlanca
Indicates the measured distance between a given entity and the location of Bahía Blanca.
-
D.
distanceToNeuquénCity_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Neuquén.
-
E.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
- F. None of above.
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_69e0c469b6ec8190aee4cadd1527db91 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef96c7c6a08190bbd9a89b8a3b921b |
completed | April 27, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69e6968abfdc81909cf9e0bd72db9eca |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:43 p.m.