Triple
T21183912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Izuhakone Railway Sunzu Line |
E522025
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Daiba depot
Daiba depot is a maintenance and storage facility serving trains on the Izuhakone Railway Sunzu Line in Shizuoka Prefecture, Japan.
|
E1470072
|
NE FINISHED |
How this triple was built (4 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: Daiba depot | Statement: [Izuhakone Railway Sunzu Line, depot, Daiba depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daiba depot Context triple: [Izuhakone Railway Sunzu Line, depot, Daiba depot]
-
A.
Shojaku Depot
Shojaku Depot is a railway maintenance and storage facility on the Hankyu Railway network in Japan, serving as a base for trains such as the Hankyu 3300 series.
-
B.
Motosumiyoshi Depot
Motosumiyoshi Depot is a railway maintenance and storage facility serving trains on Tokyu Corporation’s Meguro Line in Japan.
-
C.
Suseo Depot
Suseo Depot is a maintenance and storage facility serving trains operating on Seoul Subway Line 3 in South Korea.
-
D.
Fukagawa Depot
Fukagawa Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Tozai Line.
-
E.
Oshima Depot
Oshima Depot is a maintenance and storage facility for trains operating on Tokyo’s Toei Shinjuku Line.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Daiba depot Triple: [Izuhakone Railway Sunzu Line, depot, Daiba depot]
Generated description
Daiba depot is a maintenance and storage facility serving trains on the Izuhakone Railway Sunzu Line in Shizuoka Prefecture, Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daiba depot Target entity description: Daiba depot is a maintenance and storage facility serving trains on the Izuhakone Railway Sunzu Line in Shizuoka Prefecture, Japan.
-
A.
Shojaku Depot
Shojaku Depot is a railway maintenance and storage facility on the Hankyu Railway network in Japan, serving as a base for trains such as the Hankyu 3300 series.
-
B.
Motosumiyoshi Depot
Motosumiyoshi Depot is a railway maintenance and storage facility serving trains on Tokyu Corporation’s Meguro Line in Japan.
-
C.
Suseo Depot
Suseo Depot is a maintenance and storage facility serving trains operating on Seoul Subway Line 3 in South Korea.
-
D.
Fukagawa Depot
Fukagawa Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Tozai Line.
-
E.
Oshima Depot
Oshima Depot is a maintenance and storage facility for trains operating on Tokyo’s Toei Shinjuku Line.
- F. None of above. chosen
Provenance (5 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_69e0b50ef1d48190b063aa342667df22 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7301f7f1c81908686866fdee57127 |
completed | April 21, 2026, 8:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09757d659881909e41f593417247e5 |
completed | May 17, 2026, 7:59 a.m. |
| NEDg | Description generation | batch_6a09769c4d1c8190bf8f53fafc840fa9 |
completed | May 17, 2026, 8:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a097709a4c88190a38d787260599348 |
completed | May 17, 2026, 8:06 a.m. |
Created at: April 16, 2026, 3:05 p.m.