Triple
T7039874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR-O06 |
E163481
|
entity |
| Predicate | associatedStationNameInJapanese |
P41163
|
FINISHED |
| Object | 大阪城公園駅 |
—
|
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: 大阪城公園駅 | Statement: [JR-O06, associatedStationNameInJapanese, 大阪城公園駅]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedStationNameInJapanese Context triple: [JR-O06, associatedStationNameInJapanese, 大阪城公園駅]
-
A.
associatedStation
Indicates a relationship where one entity is linked or connected to a particular station as its relevant or related station.
-
B.
associatedWithStationName
chosen
Indicates a relationship where something is linked or connected to a particular station identified by its name.
-
C.
hasOfficialNameInJapanese
Indicates that an entity has an official, formally recognized name expressed in the Japanese language.
-
D.
hasNameInJapanese
Indicates that an entity is associated with a specific name expressed in the Japanese language.
-
E.
adjacentStationOnChuoLine
Indicates that two stations are directly next to each other as consecutive stops on the Chuo railway line.
- 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bb602081908bfa6186a1f5a4b4 |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:36 p.m.