Triple
T37757408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Usui Pass section between Yokokawa and Karuizawa |
E941153
|
entity |
| Predicate | hadDoubleTrackSections |
P100755
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Usui Pass section between Yokokawa and Karuizawa, hadDoubleTrackSections, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadDoubleTrackSections Context triple: [Usui Pass section between Yokokawa and Karuizawa, hadDoubleTrackSections, true]
-
A.
hasSingleTrackSections
Indicates that a route or railway line includes sections where only a single track is available for traffic.
-
B.
hasTrackSection
Indicates that an entity includes, is composed of, or is associated with a specific section or segment of a track.
-
C.
hasTwoTracks
chosen
Indicates that the subject possesses or is associated with exactly two distinct tracks or pathways.
-
D.
hasTwoLaneSegmentsIn
Indicates that an entity contains or is composed of exactly two distinct lane segments within a specified context or area.
-
E.
has2DSection
Indicates that one entity represents a two-dimensional cross-sectional view or slice of another entity.
- 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_69f76ee1f3a88190834e6c8af99bccc9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaef69bec8190b601dc1473f4eaf3 |
completed | May 6, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69fbadf632ec8190b14991c971258307 |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:19 p.m.