Triple
T25893732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karasuma Station |
E652404
|
entity |
| Predicate | adjacentStationOnKarasumaLine |
P167276
|
FINISHED |
| Object | Shijo Station |
—
|
NE NERFINISHED |
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: Shijo Station | Statement: [Karasuma Station, adjacentStationOnKarasumaLine, Shijo Station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adjacentStationOnKarasumaLine Context triple: [Karasuma Station, adjacentStationOnKarasumaLine, Shijo Station]
-
A.
adjacentStationOnKodomonokuniLine
Indicates that one station is directly next to another station along the Kodomonokuni railway line.
-
B.
adjacentStationOnJRKyotoLine
Indicates that one station is directly next to another station along the JR Kyoto railway line, with no other stations in between.
-
C.
adjacentStationOnSaikyoLine
Indicates that one station is directly next to another station along the Saikyo railway line, with no other stations in between.
-
D.
adjacentStationOnSennichimaeLine
Indicates that one station is directly next to another station along the Sennichimae railway line.
-
E.
adjacentStationOnJRKobeLine
Indicates that one station is directly next to another station along the JR Kobe railway line, with no other stations in between.
- 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_69e7ab3c6cc081908de59bfcc28ec19d |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f66a6468ec8190a43ed6cd8c797f42 |
completed | May 2, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
| PDg | Predicate description generation | batch_69f6691da93081909deaf680614fc900 |
completed | May 2, 2026, 9:14 p.m. |
Created at: April 22, 2026, 8:22 a.m.