Triple
T16196617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lysebotn |
E393076
|
entity |
| Predicate | roadFeatures |
P1777
|
FINISHED |
| Object | series of hairpin bends |
—
|
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: series of hairpin bends | Statement: [Lysebotn, roadFeatures, series of hairpin bends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadFeatures Context triple: [Lysebotn, roadFeatures, series of hairpin bends]
-
A.
roadFeature
chosen
Indicates that an entity is a specific physical or functional characteristic associated with a road, such as its structure, markings, or related infrastructure.
-
B.
roadType
Indicates the classification or category of a road based on its functional or physical characteristics.
-
C.
roadSystem
Indicates a relationship where multiple roads are organized and connected as part of a larger, integrated transportation network or infrastructure.
-
D.
roadJunctionIncludes
Indicates that a road junction spatially contains or encompasses a specific road segment or related roadway element as part of its structure.
-
E.
roadEnvironment
Indicates that the relationship or action occurs within, is influenced by, or is specifically associated with a road or roadway environment.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222dace848190b1a98e47333b922b |
completed | April 17, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69e219e11f6081909106b1240a17fd37 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.