Triple
T16240207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Metro Tozai Line |
E394221
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object |
JR Keiyo Line
The JR Keiyo Line is a railway line in the Tokyo–Chiba area of Japan that runs along Tokyo Bay, linking central Tokyo with destinations such as Maihama (Tokyo Disney Resort) and Chiba.
|
E1780189
|
NE 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: JR Keiyo Line | Statement: [Tokyo Metro Tozai Line, connectsWith, JR Keiyo Line]
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: JR Keiyo Line Triple: [Tokyo Metro Tozai Line, connectsWith, JR Keiyo Line]
Generated description
The JR Keiyo Line is a railway line in the Tokyo–Chiba area of Japan that runs along Tokyo Bay, linking central Tokyo with destinations such as Maihama (Tokyo Disney Resort) and Chiba.
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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2455d5270819090171d4207223a28 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12d0a179d081909d8e3369afb277f0 |
completed | May 24, 2026, 10:19 a.m. |
| NEDg | Description generation | batch_6a12d169e8888190bf3c8e7f0718a3a5 |
completed | May 24, 2026, 10:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12d2747f6881909aa2a5b0c389a494 |
completed | May 24, 2026, 10:27 a.m. |
Created at: April 10, 2026, 5:04 a.m.