Triple
T36082590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin S-Bahn fare zone A |
E1043689
|
entity |
| Predicate | typicalTicketType |
P69646
|
FINISHED |
| Object | AB 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: AB ticket | Statement: [Berlin S-Bahn fare zone A, typicalTicketType, AB ticket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTicketType Context triple: [Berlin S-Bahn fare zone A, typicalTicketType, AB ticket]
-
A.
ticketTypeCommon
Indicates that two or more tickets share the same general type or classification.
-
B.
typicalTicketText
Indicates that the associated text represents the standard or commonly used wording for a ticket.
-
C.
ticketTypeExample
chosen
Indicates that an entity serves as an example or illustrative instance of a particular ticket type.
-
D.
typicalBillet
Indicates that something serves as a standard or characteristic billet or assignment for a given role, context, or entity.
-
E.
ticketClass
Indicates the category or level of service assigned to a ticket within a ticketing or reservation system.
- 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_69f76e3154908190a6f702671c2bea08 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:08 p.m.