Triple

T10603306
Position Surface form Disambiguated ID Type / Status
Subject Bansin E275805 entity
Predicate transport P230 FINISHED
Object Bansin railway station
Bansin railway station is a local train station serving the seaside resort of Bansin on the island of Usedom in northeastern Germany.
 E874591 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: Bansin railway station | Statement: [Bansin, transport, Bansin railway station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bansin railway station
Context triple: [Bansin, transport, Bansin railway station]
  • A. Nodahanshin Station
    Nodahanshin Station is a railway station in Osaka, Japan, serving as the western terminus of the Osaka Metro Sennichimae Line and providing access to the Noda Hanshin area.
  • B. Chonu Station
    Chonu Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
  • C. Shinsen Station
    Shinsen Station is a railway station in Tokyo, Japan, served by the Keio Inokashira Line and located near the Shibuya area.
  • D. Nopo Station
    Nopo Station is a major subway and bus terminal in Busan, South Korea, serving as a key transportation hub for the northeastern part of the city.
  • E. Kecun Station
    Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
  • 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: Bansin railway station
Triple: [Bansin, transport, Bansin railway station]
Generated description
Bansin railway station is a local train station serving the seaside resort of Bansin on the island of Usedom in northeastern Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bansin railway station
Target entity description: Bansin railway station is a local train station serving the seaside resort of Bansin on the island of Usedom in northeastern Germany.
  • A. Nodahanshin Station
    Nodahanshin Station is a railway station in Osaka, Japan, serving as the western terminus of the Osaka Metro Sennichimae Line and providing access to the Noda Hanshin area.
  • B. Chonu Station
    Chonu Station is a stop on the Pyongyang Metro system in North Korea’s capital city.
  • C. Shinsen Station
    Shinsen Station is a railway station in Tokyo, Japan, served by the Keio Inokashira Line and located near the Shibuya area.
  • D. Nopo Station
    Nopo Station is a major subway and bus terminal in Busan, South Korea, serving as a key transportation hub for the northeastern part of the city.
  • E. Kecun Station
    Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6ded6d698819084f96f46ea941461 completed April 8, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95eaffcd0819098e0a06a731b602f completed April 10, 2026, 8:33 p.m.
NEDg Description generation batch_69d961aaf71881908289244e0a490492 completed April 10, 2026, 8:46 p.m.
NED2 Entity disambiguation (via description) batch_69d9623cf54081908abcdc88e13d5176 completed April 10, 2026, 8:49 p.m.
Created at: April 8, 2026, 7:32 p.m.