Triple
T109740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A22 motorway |
E2219
|
entity |
| Predicate | tollSystem |
P3913
|
FINISHED |
| Object | electronic toll collection |
—
|
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: electronic toll collection | Statement: [A22 motorway, tollSystem, electronic toll collection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tollSystem Context triple: [A22 motorway, tollSystem, electronic toll collection]
-
A.
tollingType
chosen
Indicates the specific method or basis by which a toll, fee, or charge is applied or calculated in a given context.
-
B.
tollFacilityName
Indicates the name assigned to a toll facility where tolls are collected.
-
C.
hasToll
Indicates that the use, access, or passage associated with something requires payment of a toll or fee.
-
D.
hasTollSegment
Indicates that a route, road, or path includes a segment where a toll must be paid.
-
E.
roadSystem
Indicates a relationship where multiple roads are organized and connected as part of a larger, integrated transportation network or infrastructure.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25711f6788190a22252ea3a3af394 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563fd2fc819090265edbfe3092d6 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.