Triple
T15529187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alver Municipality |
E370164
|
entity |
| Predicate | distanceToBergenByRoad_km |
P116810
|
FINISHED |
| Object | approximately 25 |
—
|
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 25 | Statement: [Alver Municipality, distanceToBergenByRoad_km, approximately 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBergenByRoad_km Context triple: [Alver Municipality, distanceToBergenByRoad_km, approximately 25]
-
A.
distanceToBergen
chosen
Indicates the spatial distance between a given entity and the location Bergen.
-
B.
distanceFromKristiansand
Indicates the spatial distance between a given location or object and the city of Kristiansand.
-
C.
distanceFromOslo
Indicates the spatial distance between a given entity’s location and the city of Oslo.
-
D.
distanceFromTrondheimApproximate
Indicates an approximate measure of how far something is from Trondheim, typically expressed as a rough or estimated distance rather than an exact value.
-
E.
distanceToDrammen
Indicates the measured distance between a given entity and the location Drammen.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0414620588190958ffde651ccab5f |
completed | April 16, 2026, 1:54 a.m. |
| PD | Predicate disambiguation | batch_69ded28ab0588190a47a9090d1238707 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 4:05 a.m.