Triple
T33696137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SKRG |
E863316
|
entity |
| Predicate | distanceFromMedellínKilometersApprox |
P85456
|
FINISHED |
| Object | 20 |
—
|
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: 20 | Statement: [SKRG, distanceFromMedellínKilometersApprox, 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMedellínKilometersApprox Context triple: [SKRG, distanceFromMedellínKilometersApprox, 20]
-
A.
distanceFromMedellín
chosen
Indicates the measured spatial distance between an entity and the city of Medellín.
-
B.
distanceFromBogotá
Indicates the spatial distance separating a given entity or location from the city of Bogotá.
-
C.
distanceToSantaMarta
Indicates the measured spatial distance between a given entity’s location and the location of Santa Marta.
-
D.
distanceFromLaPazApproximate
Indicates an approximate measure of how far something is from La Paz, typically expressed as a rough distance rather than an exact value.
-
E.
distanceFromCaracas
Indicates the spatial distance between a given entity or location and the city of Caracas.
- 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_69f3498723a08190ac034339cc78eade |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:43 a.m.