Triple
T38701638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sendai City Transportation Bureau |
E950154
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Subway Operations Division
The Subway Operations Division is the unit of the Sendai City Transportation Bureau responsible for managing and operating the city’s subway system.
|
E2281544
|
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: Subway Operations Division | Statement: [Sendai City Transportation Bureau, hasPart, Subway Operations Division]
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: Subway Operations Division Triple: [Sendai City Transportation Bureau, hasPart, Subway Operations Division]
Generated description
The Subway Operations Division is the unit of the Sendai City Transportation Bureau responsible for managing and operating the city’s subway system.
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_69f76f0124408190bb39c3040734846b |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcdc6dcaa081909520d0920b0df51d |
completed | May 7, 2026, 6:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4205cf5f0c81908a4f2e95c940385c |
completed | June 29, 2026, 5:42 a.m. |
| NEDg | Description generation | batch_6a4207fdfa9c81908b46586b1204bd22 |
completed | June 29, 2026, 5:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a42086985288190a3cf7b7f45859926 |
completed | June 29, 2026, 5:53 a.m. |
Created at: May 3, 2026, 4:33 p.m.