Triple
T14948477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Chitose Airport Station |
E372728
|
entity |
| Predicate | hasAutomatedTicketMachines |
P49831
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [New Chitose Airport Station, hasAutomatedTicketMachines, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutomatedTicketMachines Context triple: [New Chitose Airport Station, hasAutomatedTicketMachines, yes]
-
A.
hasSelfServiceTicketMachines
chosen
Indicates that an entity is equipped with self-service ticket machines available for use.
-
B.
ticketMachines
Indicates that there is a relationship involving ticket machines, typically denoting where they are located, available, or associated with a particular entity or place.
-
C.
hasAutomaticFareCollection
Indicates that an entity is equipped with a system that automatically collects fares or payments from users without manual processing.
-
D.
hasTicketBooths
Indicates that one entity possesses or contains ticket booths used for selling or distributing tickets.
-
E.
hasRetailKiosks
Indicates that one entity operates or maintains retail kiosks associated with or located within another entity.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded68fae3c81909873b113bfcaca05 |
completed | April 15, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69de9a588c2c8190b1245a1c406f447c |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:39 a.m.