Triple
T36184588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SPA platform |
E1046809
|
entity |
| Predicate | trackWidthVariability |
P185005
|
FINISHED |
| Object | supports multiple track widths |
—
|
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: supports multiple track widths | Statement: [SPA platform, trackWidthVariability, supports multiple track widths]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trackWidthVariability Context triple: [SPA platform, trackWidthVariability, supports multiple track widths]
-
A.
trackWidth
Indicates the lateral distance between two parallel tracks or wheels, typically measured from center to center.
-
B.
roadWidth
Indicates the measured breadth or distance across a road, typically from one edge or curb to the opposite edge or curb.
-
C.
hasRoadWidth
Indicates the width measurement of a road segment or roadway in the relationship.
-
D.
carWidth
Indicates the measurement of how wide a car is across its lateral (side-to-side) dimension.
-
E.
hasRailWidth
Indicates that one entity has a specified width measurement for its rail or rails.
- F. None of above. chosen
Provenance (4 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_69f76e3d4fbc81908c159c7beeb4ce00 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b69b333081909cadbed3fcb8ecf5 |
completed | May 3, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c2a5f8819094ad4621d7b97e0c |
completed | May 3, 2026, 8:49 p.m. |
| PDg | Predicate description generation | batch_69f7b69a74a08190b31b1201278a2c57 |
completed | May 3, 2026, 8:56 p.m. |
Created at: May 3, 2026, 4:08 p.m.