Triple
T12887329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nishi-Kawaguchi Station |
E308262
|
entity |
| Predicate | adjacentStation |
P5707
|
FINISHED |
| Object |
Warabi Station
Warabi Station is a railway station in Warabi, Saitama Prefecture, Japan, served by the JR East Keihin-Tōhoku Line.
|
E1768778
|
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: Warabi Station | Statement: [Nishi-Kawaguchi Station, adjacentStation, Warabi Station]
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: Warabi Station Triple: [Nishi-Kawaguchi Station, adjacentStation, Warabi Station]
Generated description
Warabi Station is a railway station in Warabi, Saitama Prefecture, Japan, served by the JR East Keihin-Tōhoku Line.
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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714415c08190aa9944b494a3ddad |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12a79f56988190b6f1322c79cb5cbf |
completed | May 24, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_6a12a836c29081909204e8050475b90a |
completed | May 24, 2026, 7:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12a8c42ba88190bff494510a3bbbcd |
completed | May 24, 2026, 7:29 a.m. |
Created at: April 9, 2026, 5:39 p.m.