Triple
T5958788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zürich trolleybus network |
E132581
|
entity |
| Predicate | hasVehicleFeature |
P67070
|
FINISHED |
| Object | low-floor access |
—
|
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: low-floor access | Statement: [Zürich trolleybus network, hasVehicleFeature, low-floor access]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVehicleFeature Context triple: [Zürich trolleybus network, hasVehicleFeature, low-floor access]
-
A.
hasParkFeature
Indicates that a park includes, contains, or is associated with a specific feature or amenity.
-
B.
hasVehicle
Indicates that one entity possesses, owns, or is assigned a vehicle.
-
C.
supportsDrivetrainFeature
Indicates that an entity provides or is compatible with a specified drivetrain-related capability or function.
-
D.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
-
E.
hasMountingFeature
Indicates that one entity includes or provides a structural feature intended for mounting or attaching another entity.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03fb7f8a88190a8bd45208bda4a03 |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0335a635881909c58c1ef0f97f1e8 |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c03fb6bb7c81909c5629fba408dc69 |
completed | March 22, 2026, 7:15 p.m. |
Created at: March 22, 2026, 4:02 p.m.