Triple
T5174239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osaka Metro Nagahori Tsurumi-ryokuchi Line |
E116757
|
entity |
| Predicate | trainOperation |
P62161
|
FINISHED |
| Object | fully automated |
—
|
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: fully automated | Statement: [Osaka Metro Nagahori Tsurumi-ryokuchi Line, trainOperation, fully automated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainOperation Context triple: [Osaka Metro Nagahori Tsurumi-ryokuchi Line, trainOperation, fully automated]
-
A.
trainOperator
Indicates that one entity operates, manages, or runs train services for another entity or within a specific rail system.
-
B.
trainConfiguration
Indicates the specific arrangement and composition of train elements (such as locomotives and cars) used together for a particular operation or service.
-
C.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
D.
trainsForOccupation
Indicates that an entity undergoes training or preparation aimed at qualifying for or performing a specific occupation.
-
E.
rollingStockOperator
Indicates that an entity operates or manages rolling stock, such as trains or rail vehicles, in a railway system.
- 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_69bd445ff97c81909a2615cc56235470 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7971284481909e6d07b2368a4f76 |
completed | March 20, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69bd77b529948190b86671ebe43f4734 |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd79251b548190918a1eb930e24c22 |
completed | March 20, 2026, 4:43 p.m. |
Created at: March 20, 2026, 1:45 p.m.