Triple
T10603600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Usedomer Bäderbahn railway |
E275813
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Bansin station
Bansin station is a railway stop on Germany’s Baltic Sea island of Usedom serving the seaside resort of Bansin.
|
E1097443
|
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 station | Statement: [Usedomer Bäderbahn railway, hasStation, Bansin station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bansin station Context triple: [Usedomer Bäderbahn railway, hasStation, Bansin station]
-
A.
Bashamichi Station
Bashamichi Station is an underground railway station in Yokohama, Japan, providing access to the Minato Mirai waterfront district and surrounding urban attractions.
-
B.
Shinsen Station
Shinsen Station is a railway station in Tokyo, Japan, served by the Keio Inokashira Line and located near the Shibuya area.
-
C.
Kecun Station
Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
-
D.
Bentencho Station
Bentencho Station is a railway station in Osaka, Japan, serving as an interchange between the Osaka Loop Line and other local lines.
-
E.
Naha Station
Naha Station is a major railway terminal in Naha, Okinawa, serving as a key transportation hub for the city and surrounding region.
- 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 station Triple: [Usedomer Bäderbahn railway, hasStation, Bansin station]
Generated description
Bansin station is a railway stop on Germany’s Baltic Sea island of Usedom serving the seaside resort of Bansin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bansin station Target entity description: Bansin station is a railway stop on Germany’s Baltic Sea island of Usedom serving the seaside resort of Bansin.
-
A.
Bashamichi Station
Bashamichi Station is an underground railway station in Yokohama, Japan, providing access to the Minato Mirai waterfront district and surrounding urban attractions.
-
B.
Shinsen Station
Shinsen Station is a railway station in Tokyo, Japan, served by the Keio Inokashira Line and located near the Shibuya area.
-
C.
Kecun Station
Kecun Station is a major interchange stop on the Guangzhou Metro system in Guangzhou, China.
-
D.
Bentencho Station
Bentencho Station is a railway station in Osaka, Japan, serving as an interchange between the Osaka Loop Line and other local lines.
-
E.
Naha Station
Naha Station is a major railway terminal in Naha, Okinawa, serving as a key transportation hub for the city and surrounding region.
- 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_69fd54f264d48190be636796d694ceb1 |
completed | May 8, 2026, 3:13 a.m. |
| NEDg | Description generation | batch_69fd570e482881909532000eebd169d1 |
completed | May 8, 2026, 3:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd57710f648190a1344ac1363acce1 |
completed | May 8, 2026, 3:24 a.m. |
Created at: April 8, 2026, 7:32 p.m.