Triple
T32301132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marburg city bus network |
E825240
|
entity |
| Predicate | typicalTicketTypes |
P206244
|
FINISHED |
| Object | single ticket |
—
|
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: single ticket | Statement: [Marburg city bus network, typicalTicketTypes, single ticket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTicketTypes Context triple: [Marburg city bus network, typicalTicketTypes, single ticket]
-
A.
ticketTypeExample
Indicates that an entity serves as an example or illustrative instance of a particular ticket type.
-
B.
ticketTypeCommon
Indicates that two or more tickets share the same general type or classification.
-
C.
typicalTicketText
Indicates that the associated text represents the standard or commonly used wording for a ticket.
-
D.
ticketTypeAccepted
Indicates that a particular type of ticket is valid for use or accepted in a given context or by a given entity.
-
E.
liftTicketType
Indicates the type or category of lift ticket associated with or assigned to an 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_69f349115304819084ee91d345b6c8aa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 12:45 a.m.