Triple
T13050108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vermillion, South Dakota |
E327427
|
entity |
| Predicate | distanceToSiouxCity |
P108439
|
FINISHED |
| Object | about 60 miles |
—
|
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 60 miles | Statement: [Vermillion, South Dakota, distanceToSiouxCity, about 60 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSiouxCity Context triple: [Vermillion, South Dakota, distanceToSiouxCity, about 60 miles]
-
A.
distanceToSaintPaul
Indicates the measured spatial distance between a given entity and the location of Saint Paul.
-
B.
distanceToWichita
Indicates the measured distance between a given entity’s location and the city of Wichita.
-
C.
distanceToStLouis
Indicates the measured distance between a given entity’s location and the city of St. Louis.
-
D.
distanceFromDesMoines
Indicates the physical distance between a given location and the city of Des Moines.
-
E.
distanceToMilwaukee
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Milwaukee.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
| PDg | Predicate description generation | batch_69d98a9577d081908ddef9ea77e408e2 |
completed | April 10, 2026, 11:41 p.m. |
Created at: April 9, 2026, 8:57 p.m.