Triple
T18309282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyne Dock Metro station |
E438575
|
entity |
| Predicate | hasVisualInformationSystems |
P17090
|
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: [Tyne Dock Metro station, hasVisualInformationSystems, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisualInformationSystems Context triple: [Tyne Dock Metro station, hasVisualInformationSystems, yes]
-
A.
hasVisuals
Indicates that one entity includes, displays, or is associated with visual elements or imagery related to another entity.
-
B.
hasPassengerInformationSystem
chosen
Indicates that an entity is equipped with a system that provides information to passengers, such as schedules, announcements, or travel updates.
-
C.
hasCISDisplays
Indicates that an entity is equipped with or includes CIS (Customer Information System) display units.
-
D.
hasCCTV
Indicates that one entity is equipped with or monitored by a CCTV (closed-circuit television) system installed or provided by another entity.
-
E.
visualizedIn
Indicates that something is represented or depicted within a particular visual medium, view, or visualization.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021709f88190a8047dd57edc2029 |
completed | April 19, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e44fdf43d08190bbcfb6b1fe3cc0ee |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.