Triple
T10650071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Villamil Playas |
E250941
|
entity |
| Predicate | distanceToGuayaquil_km |
P95157
|
FINISHED |
| Object | approximately 90 |
—
|
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 90 | Statement: [General Villamil Playas, distanceToGuayaquil_km, approximately 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGuayaquil_km Context triple: [General Villamil Playas, distanceToGuayaquil_km, approximately 90]
-
A.
distanceFromQuito
Indicates the spatial distance between a given entity or location and the city of Quito.
-
B.
distanceFromCuenca
Indicates the measured spatial distance between a given entity or location and the city of Cuenca.
-
C.
distanceFromArequipa
Indicates the spatial distance between a given location and the city of Arequipa.
-
D.
distanceToIquique_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Iquique.
-
E.
distanceFromLima
Indicates the measured distance between a given place or object and the city of Lima.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfe4b97081908815b77612222318 |
completed | April 8, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69d6dd83b114819098e84dc658e82d7e |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df47899481909ac0e518d94883cb |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:06 p.m.