Triple
T20734932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maihama Station |
E509673
|
entity |
| Predicate | hasStationNumberingSystem |
P44484
|
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: [Maihama Station, hasStationNumberingSystem, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationNumberingSystem Context triple: [Maihama Station, hasStationNumberingSystem, yes]
-
A.
hasRailwayStationNumberingSystem
chosen
Indicates that a railway station is associated with a specific system for assigning it an identifying number or code.
-
B.
hasStreetNumberingSystem
Indicates that a location or area uses an organized system for assigning numbers to buildings or addresses along its streets.
-
C.
hasNumberSystem
Indicates that an entity possesses or uses a particular system for representing and organizing numbers.
-
D.
numberingSystemBasedOn
Indicates that one numbering system is derived from, structured according to, or conceptually dependent on another numbering system.
-
E.
laterNumberingSystem
Indicates that one numbering system was adopted or used after another, reflecting a subsequent or more recent scheme of numbering.
- 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_69e0b4c589c08190834fb5d86d0efa2b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c209348c819084a2f35f36378680 |
completed | April 21, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69e5c04b31248190b9b9d91b5cb854e3 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:31 p.m.