Triple
T4831341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorasan area (limited crossings) |
E107951
|
entity |
| Predicate | crossingFrequency |
P27025
|
FINISHED |
| Object | rare and tightly scheduled |
—
|
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: rare and tightly scheduled | Statement: [Dorasan area (limited crossings), crossingFrequency, rare and tightly scheduled]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossingFrequency Context triple: [Dorasan area (limited crossings), crossingFrequency, rare and tightly scheduled]
-
A.
frequentlyVisitedBy
Indicates that an entity is regularly or often visited by another entity.
-
B.
crossingOf
Indicates that one entity serves as the intersection or crossing point of two or more linear features, such as roads, paths, or tracks.
-
C.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
D.
visitorFrequency
chosen
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
E.
approximateCrossingTime
Indicates an estimated point in time when one entity is expected to cross or intersect another (such as a path, boundary, or trajectory).
- 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ff981fc819080d4466c6fe06cf3 |
completed | March 20, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69bd6c21c7f08190846049d31fdfa144 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:24 p.m.