Triple
T5453261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leicester to Lincoln |
E122417
|
entity |
| Predicate | typicalPassengerUse |
P8370
|
FINISHED |
| Object | commuting |
—
|
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: commuting | Statement: [Leicester to Lincoln, typicalPassengerUse, commuting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPassengerUse Context triple: [Leicester to Lincoln, typicalPassengerUse, commuting]
-
A.
hasPassengerUsageCategory
chosen
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
-
B.
hasPassengerUsageStatistics
Indicates the relationship by which an entity is associated with data describing how passengers use it, such as counts, frequencies, or patterns of passenger activity.
-
C.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
D.
servesPassengerTrafficType
Indicates that a transportation facility or service accommodates a specified type or category of passenger traffic.
-
E.
usedForPassengerInformation
Indicates that something serves the purpose of providing information to passengers.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd95be329c81908783420cf81b6af5 |
completed | March 20, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69bd919e8d18819098c4af6a015e5cc2 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.