Triple
T24334975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cape Prince of Wales |
E613352
|
entity |
| Predicate | distanceToRussiaAtClosestPoint |
P21939
|
FINISHED |
| Object | approximately 89 km |
—
|
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 89 km | Statement: [Cape Prince of Wales, distanceToRussiaAtClosestPoint, approximately 89 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToRussiaAtClosestPoint Context triple: [Cape Prince of Wales, distanceToRussiaAtClosestPoint, approximately 89 km]
-
A.
distanceToRussianBorder_km
chosen
Indicates the physical distance, measured in kilometers, between a given location and the nearest point on the Russian border.
-
B.
distanceToArkhangelskApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Arkhangelsk.
-
C.
distanceToVladivostok_km
Indicates the physical distance, measured in kilometers, between a given location and Vladivostok.
-
D.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
-
E.
distanceFromMurmansk
Indicates the spatial distance between a given location and the city of Murmansk.
- 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_69e2d7dcc5a08190b53691130d56cbc4 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f292f444b08190b9c8e033ff6ee5fc |
completed | April 29, 2026, 11:23 p.m. |
| PD | Predicate disambiguation | batch_69f287ad30048190b3ad3613486f277f |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 1:56 a.m.