Triple
T33424684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salinas beaches |
E855938
|
entity |
| Predicate | approxDistanceFromGuayaquil |
P95157
|
FINISHED |
| Object | about 140 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 140 km | Statement: [Salinas beaches, approxDistanceFromGuayaquil, about 140 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxDistanceFromGuayaquil Context triple: [Salinas beaches, approxDistanceFromGuayaquil, about 140 km]
-
A.
distanceToGuayaquil_km
chosen
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Guayaquil.
-
B.
distanceFromQuito
Indicates the spatial distance between a given entity or location and the city of Quito.
-
C.
distanceFromCuenca
Indicates the measured spatial distance between a given entity or location and the city of Cuenca.
-
D.
distanceFromArequipa
Indicates the spatial distance between a given location and the city of Arequipa.
-
E.
distanceToAsunción
Indicates the spatial distance between an entity’s location and the city of Asunción.
- 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_69f3496fdf0081908c1aa30870ce518b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fee335cb08819097e3a0e09d5ebf49 |
completed | May 9, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69fee2c74fd88190acfc045ab07b7f6b |
completed | May 9, 2026, 7:31 a.m. |
Created at: May 1, 2026, 1:36 a.m.