Triple
T5587808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Watertown, New York, United States |
E146797
|
entity |
| Predicate | distanceToCanadianBorder |
P64923
|
FINISHED |
| Object | approximately 30 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 30 miles | Statement: [Watertown, New York, United States, distanceToCanadianBorder, approximately 30 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCanadianBorder Context triple: [Watertown, New York, United States, distanceToCanadianBorder, approximately 30 miles]
-
A.
distanceToMexicoBorder
Indicates the measured or estimated distance between a given location or entity and the border of Mexico.
-
B.
hasBorderLengthWithCanada_km
Indicates the length, in kilometers, of the land or maritime border that an entity shares with Canada.
-
C.
distanceToPennsylvaniaBorder
Indicates the measured distance between a given location and the border of Pennsylvania.
-
D.
distanceToRussianBorder_km
Indicates the physical distance, measured in kilometers, between a given location and the nearest point on the Russian border.
-
E.
distanceFromMainland
Indicates the measured spatial separation between a location and the nearest point on the mainland.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0209e892c8190b936a05ef2a14d36 |
completed | March 22, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69c01b16b9bc8190ab0b945507d90e05 |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f4032408190a4f0d2eb21ebd870 |
completed | March 22, 2026, 4:56 p.m. |
Created at: March 22, 2026, 3:38 p.m.