Triple
T10242687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tabata Station |
E243634
|
entity |
| Predicate | stationNumberOnYamanoteLine |
P93161
|
FINISHED |
| Object | JY09 |
—
|
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: JY09 | Statement: [Tabata Station, stationNumberOnYamanoteLine, JY09]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stationNumberOnYamanoteLine Context triple: [Tabata Station, stationNumberOnYamanoteLine, JY09]
-
A.
adjacentStationOnYamanoteLine
Indicates that one station is directly next to another station along the Yamanote Line, with no other stations in between.
-
B.
adjacentStationOnSubwaySeishinYamateLine
Indicates that two stations are directly next to each other on the Seishin-Yamate subway line, with no other stations in between.
-
C.
adjacentStationOnYokosukaLine
Indicates that one station is directly next to another station along the Yokosuka railway line, with no other stations in between.
-
D.
adjacentStationOnChuoLine
Indicates that two stations are directly next to each other as consecutive stops on the Chuo railway line.
-
E.
hasShinkansenStop
Indicates that a location is served by and includes a stop for a Shinkansen (high-speed rail) line.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d328272c8190a3548d7f7f38cfc4 |
completed | April 7, 2026, 9:49 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ebd6c88190a1f3f4a72a99d6fe |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d32741888190928b045e2241cfac |
completed | April 7, 2026, 9:49 a.m. |
Created at: April 6, 2026, 11:25 a.m.