Triple
T20717736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maibara Station |
E509218
|
entity |
| Predicate | ticketGatesType |
P141241
|
FINISHED |
| Object | automatic fare gates |
—
|
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: automatic fare gates | Statement: [Maibara Station, ticketGatesType, automatic fare gates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketGatesType Context triple: [Maibara Station, ticketGatesType, automatic fare gates]
-
A.
ticketTypeExample
Indicates that an entity serves as an example or illustrative instance of a particular ticket type.
-
B.
ticketTypeAccepted
Indicates that a particular type of ticket is valid for use or accepted in a given context or by a given entity.
-
C.
ticketingZoneType
Indicates the type or category of ticketing zone that applies within a given area or context.
-
D.
ticketTypeStored
Indicates that a particular type of ticket has been recorded and saved in a storage or system.
-
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_69e0b4c40ad88190b81f77695366d328 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d2c57481909840945ffd3b0cc3 |
completed | April 21, 2026, 12:16 a.m. |
| PD | Predicate disambiguation | batch_69e5c04b31248190b9b9d91b5cb854e3 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:17 p.m.