Triple
T8976859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Metro Chiyoda Line |
E214411
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Ayase Depot
Ayase Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Chiyoda Line in Tokyo, Japan.
|
E769045
|
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: Ayase Depot | Statement: [Tokyo Metro Chiyoda Line, depot, Ayase Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ayase Depot Context triple: [Tokyo Metro Chiyoda Line, depot, Ayase Depot]
-
A.
Shibuya Depot
Shibuya Depot is a Tokyo Metro facility in Shibuya used for the storage, inspection, and maintenance of Ginza Line trains.
-
B.
Shibuya 109
Shibuya 109 is a famous multi-story fashion shopping mall in Tokyo known as a trendsetting hub for youth and street fashion.
-
C.
Shiki Depot
Shiki Depot is a Tobu Railway maintenance and storage facility that services and houses Tobu 9000 series commuter trains.
-
D.
Shibuya Hikarie
Shibuya Hikarie is a major high-rise commercial complex in Tokyo known for its shopping, dining, cultural facilities, and direct connection to Shibuya Station.
-
E.
Suminoe Depot
Suminoe Depot is a railway maintenance and storage facility serving Osaka Metro’s Yotsubashi Line in Osaka, Japan.
- 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: Ayase Depot Triple: [Tokyo Metro Chiyoda Line, depot, Ayase Depot]
Generated description
Ayase Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Chiyoda Line in Tokyo, Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ayase Depot Target entity description: Ayase Depot is a major Tokyo Metro maintenance and storage facility serving trains on the Chiyoda Line in Tokyo, Japan.
-
A.
Shibuya Depot
Shibuya Depot is a Tokyo Metro facility in Shibuya used for the storage, inspection, and maintenance of Ginza Line trains.
-
B.
Shibuya 109
Shibuya 109 is a famous multi-story fashion shopping mall in Tokyo known as a trendsetting hub for youth and street fashion.
-
C.
Shiki Depot
Shiki Depot is a Tobu Railway maintenance and storage facility that services and houses Tobu 9000 series commuter trains.
-
D.
Shibuya Hikarie
Shibuya Hikarie is a major high-rise commercial complex in Tokyo known for its shopping, dining, cultural facilities, and direct connection to Shibuya Station.
-
E.
Suminoe Depot
Suminoe Depot is a railway maintenance and storage facility serving Osaka Metro’s Yotsubashi Line in Osaka, Japan.
- 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_69ca839ea8b88190922c6a326ffcc0d3 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6786c880819088393bb107a7364c |
completed | April 1, 2026, 12:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc96aa46c81908b95a23fd2b4da57 |
completed | April 3, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69cfca00ffa08190ad66fb99572a6275 |
completed | April 3, 2026, 2:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfca84937881909707497d733218e5 |
completed | April 3, 2026, 2:11 p.m. |
Created at: March 30, 2026, 7:02 p.m.