Triple
T1265439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ripon, Wisconsin |
E12589
|
entity |
| Predicate | distanceToMilwaukee |
P24901
|
FINISHED |
| Object | approximately 80 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: approximately 80 miles | Statement: [Ripon, Wisconsin, distanceToMilwaukee, approximately 80 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMilwaukee Context triple: [Ripon, Wisconsin, distanceToMilwaukee, approximately 80 miles]
-
A.
distanceToPhiladelphia
Indicates the spatial distance between a given entity’s location and the city of Philadelphia.
-
B.
distanceToSeattle
Indicates the measured or calculated distance between a given entity’s location and the city of Seattle.
-
C.
distanceToToronto
Indicates the spatial distance between a given entity’s location and the city of Toronto.
-
D.
distanceToWashingtonDC
Indicates the physical distance between a given location and Washington, D.C.
-
E.
distanceFromToronto
Indicates the spatial distance between a given entity and the location of Toronto.
- 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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4c036deb881909b234894347c75c6 |
completed | March 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69a4bb7058e88190825b0cba5ee60b51 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc218cb8819090bff71ac7107a03 |
completed | March 1, 2026, 10:22 p.m. |
Created at: March 1, 2026, 7:50 p.m.