Triple
T29741475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capilla del Señor station |
E752617
|
entity |
| Predicate | hasLevelCrossingsNearby |
P3381
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Capilla del Señor station, hasLevelCrossingsNearby, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLevelCrossingsNearby Context triple: [Capilla del Señor station, hasLevelCrossingsNearby, yes]
-
A.
hasAtGradeCrossingNearby
chosen
Indicates that one entity (typically a location or segment) has a nearby at-grade crossing where two transportation paths intersect at the same level.
-
B.
hasNearbyCrossingPoint
Indicates that one location has a crossing point (such as a bridge, crosswalk, or intersection) situated close to it.
-
C.
hasGradeCrossings
Indicates that there are one or more level crossings where a road, path, or similar route intersects the railway or track at the same grade.
-
D.
levelCrossingsApproximate
Indicates that one quantity or function has level crossings that approximately coincide with those of another, within some tolerance.
-
E.
crossesAtHeight
Indicates that one entity passes over another at a specified vertical distance or elevation.
- 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_69f0d62b064081908c1ae61cd68fb139 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
Created at: April 28, 2026, 7:48 p.m.