Triple
T19239095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centenario (Neuquén) |
E481081
|
entity |
| Predicate | distanceToNeuquénCity_km |
P135307
|
FINISHED |
| Object | about 15 |
—
|
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 | Statement: [Centenario (Neuquén), distanceToNeuquénCity_km, about 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNeuquénCity_km Context triple: [Centenario (Neuquén), distanceToNeuquénCity_km, about 15]
-
A.
distanceFromSaltaByRoad_km
Indicates the distance in kilometers between an entity and Salta when traveling by road.
-
B.
distanceFromCafayateByRoad_km
Indicates the distance in kilometers from Cafayate to another location when traveling by road.
-
C.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
D.
distanceFromBuenosAires
Indicates the measured distance between a given entity’s location and the city of Buenos Aires.
-
E.
distanceFromBariloche
Indicates the measured distance between a given entity or location and Bariloche.
- 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_69d8e8ccb8f48190ad420098e74fb1db |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faef827c81909157bbcd4060dfc9 |
completed | April 20, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e4dcfae6f081909cc173cf71a5005c |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4debc39ac81908b7c5ef797046360 |
completed | April 19, 2026, 1:55 p.m. |
Created at: April 10, 2026, 1:26 p.m.