Triple
T15489297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hankyu 3300 series |
E377135
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
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.
|
E1161676
|
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: Shojaku Depot | Statement: [Hankyu 3300 series, depot, Shojaku Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shojaku Depot Context triple: [Hankyu 3300 series, depot, Shojaku Depot]
-
A.
Suminoe Depot
Suminoe Depot is a railway maintenance and storage facility serving Osaka Metro’s Yotsubashi Line in Osaka, Japan.
-
B.
Shimura Depot
Shimura Depot is a train maintenance and storage facility serving Tokyo's Toei Mita Line.
-
C.
Nakano Depot
Nakano Depot is a major Tokyo Metro facility used for the storage, inspection, and maintenance of Marunouchi Line trains.
-
D.
Motosumiyoshi Depot
Motosumiyoshi Depot is a railway maintenance and storage facility serving trains on Tokyu Corporation’s Meguro Line in Japan.
-
E.
Shiki Depot
Shiki Depot is a Tobu Railway maintenance and storage facility that services and houses Tobu 9000 series commuter trains.
- 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: Shojaku Depot Triple: [Hankyu 3300 series, depot, Shojaku Depot]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shojaku Depot Target entity description: 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.
-
A.
Suminoe Depot
Suminoe Depot is a railway maintenance and storage facility serving Osaka Metro’s Yotsubashi Line in Osaka, Japan.
-
B.
Shimura Depot
Shimura Depot is a train maintenance and storage facility serving Tokyo's Toei Mita Line.
-
C.
Nakano Depot
Nakano Depot is a major Tokyo Metro facility used for the storage, inspection, and maintenance of Marunouchi Line trains.
-
D.
Motosumiyoshi Depot
Motosumiyoshi Depot is a railway maintenance and storage facility serving trains on Tokyu Corporation’s Meguro Line in Japan.
-
E.
Shiki Depot
Shiki Depot is a Tobu Railway maintenance and storage facility that services and houses Tobu 9000 series commuter trains.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03faaca588190b0397bc2e27a522a |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d4661088190bb53161247effcc4 |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3e5643088190a9b001ef815ddd3a |
completed | May 9, 2026, 2:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff3f4456f88190b7fc9b853b0155e4 |
completed | May 9, 2026, 2:05 p.m. |
Created at: April 10, 2026, 3:48 a.m.