Triple
T11218505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Ensenada |
E265499
|
entity |
| Predicate | distanceToUSBorderByRoad_km |
P97898
|
FINISHED |
| Object | approximately 100 |
—
|
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 100 | Statement: [Port of Ensenada, distanceToUSBorderByRoad_km, approximately 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToUSBorderByRoad_km Context triple: [Port of Ensenada, distanceToUSBorderByRoad_km, approximately 100]
-
A.
distanceToCanadianBorder
Indicates the measured or estimated spatial distance between a given location and the nearest point on the Canadian national border.
-
B.
distanceToMexicoBorder
Indicates the measured or estimated distance between a given location or entity and the border of Mexico.
-
C.
distanceToEnglishBorder
Indicates the spatial distance between a given location and the border of England.
-
D.
distanceToSpanishBorder
Indicates the measured distance between a given location and the border of Spain.
-
E.
distanceToRussianBorder_km
Indicates the physical distance, measured in kilometers, between a given location and the nearest point on the Russian border.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8ea19e8819095d5d02c1f145534 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d75cfbbb188190861efd5d94fe27da |
completed | April 9, 2026, 8:02 a.m. |
| PDg | Predicate description generation | batch_69d77062271c8190b63da714ab5beff9 |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.