Triple
T15363625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knivskjellodden |
E367350
|
entity |
| Predicate | distanceFromMainlandEurope |
P5691
|
FINISHED |
| Object | connected via tunnels and roads to mainland Norway |
—
|
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: connected via tunnels and roads to mainland Norway | Statement: [Knivskjellodden, distanceFromMainlandEurope, connected via tunnels and roads to mainland Norway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMainlandEurope Context triple: [Knivskjellodden, distanceFromMainlandEurope, connected via tunnels and roads to mainland Norway]
-
A.
distanceFromMainland
chosen
Indicates the measured spatial separation between a location and the nearest point on the mainland.
-
B.
distanceToContinentApproximate
Indicates an approximate measure of how far something is from a specified continent.
-
C.
distanceFromMediterranean
Indicates the measured spatial distance between a given location and the Mediterranean Sea.
-
D.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
E.
countryClosestTo
Indicates the relationship where one country is geographically nearer to a given reference point or entity than any other country.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e479f188190bbbc3dcd73853e02 |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69deca9ab7e88190a9261ef27be665b1 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:18 a.m.