Triple
T3413457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westerscheldetunnel |
E71952
|
entity |
| Predicate | hasTrafficControlCenter |
P49308
|
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: [Westerscheldetunnel, hasTrafficControlCenter, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrafficControlCenter Context triple: [Westerscheldetunnel, hasTrafficControlCenter, yes]
-
A.
hasTrafficControl
Indicates that some form of traffic management or regulation mechanism is present or applied to a given route, intersection, or transportation element.
-
B.
hasTrafficSignals
Indicates that traffic control signals are present at or associated with a given location or roadway feature.
-
C.
hasTrafficIsland
Indicates the presence of a traffic island separating or organizing lanes or directions of vehicular movement within a roadway.
-
D.
hasTrafficManagement
Indicates that an entity implements, uses, or is associated with systems or measures for controlling and optimizing traffic flow.
-
E.
fareControlLocation
Indicates a location where access to or use of a transportation service is regulated, checked, or controlled for fare payment.
- 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_69ad85ac312481909e7027ced1456a9f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb90ba83c81909abdcddb334e64f6 |
completed | March 8, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69adadfcbc38819080852c18240451c5 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb23d23088190aeafe1379eae2eaa |
completed | March 8, 2026, 5:30 p.m. |
Created at: March 8, 2026, 3:15 p.m.