Triple
T25737867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR West Nara Line |
E648125
|
entity |
| Predicate | usesRollingStock |
P5426
|
FINISHED |
| Object |
JR West 103 series
The JR West 103 series is a long-serving Japanese electric multiple unit train type operated by West Japan Railway Company, widely used on various commuter lines since the 1960s.
|
E1701402
|
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 West 103 series | Statement: [JR West Nara Line, usesRollingStock, JR West 103 series]
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 West 103 series Triple: [JR West Nara Line, usesRollingStock, JR West 103 series]
Generated description
The JR West 103 series is a long-serving Japanese electric multiple unit train type operated by West Japan Railway Company, widely used on various commuter lines since the 1960s.
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_69e7ab306eec8190b05c312c6ab186b8 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f5fd1726708190ab4382bf35e2db90 |
completed | May 2, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10ec9c59bc8190b226a90e22b58d3c |
completed | May 22, 2026, 11:54 p.m. |
| NEDg | Description generation | batch_6a10f0580c5081908dc79c2b6381eb60 |
completed | May 23, 2026, 12:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10f0a555888190bc7dabba41a86026 |
completed | May 23, 2026, 12:11 a.m. |
Created at: April 22, 2026, 3:36 a.m.