Triple

T2515875
Position Surface form Disambiguated ID Type / Status
Subject Island of Usedom E55410 entity
Predicate hasPart P35 FINISHED
Object Bansin
Bansin is a seaside resort town on Germany’s Baltic Sea coast, known as one of the “Kaiserbäder” (Imperial Spas) on the island of Usedom.
 E275805 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 | Statement: [Island of Usedom, hasPart, Bansin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bansin
Context triple: [Island of Usedom, hasPart, Bansin]
  • A. Paju
    Paju is a city in South Korea near the Demilitarized Zone, known for its historical sites, cultural complexes, and role as a border hub with North Korea.
  • B. Nambui
    Nambui was a Mongol empress consort of the Yuan dynasty and a prominent wife of Kublai Khan, influential in the imperial court after the death of his first empress.
  • C. Bokakhat
    Bokakhat is a small town in Assam, India, known primarily as a gateway and service hub for visitors to Kaziranga National Park.
  • D. Bisharin
    Bisharin are a subgroup of the Beja people, traditionally semi-nomadic pastoralists inhabiting parts of northeastern Sudan and southern Egypt.
  • E. Bomunsan
    Bomunsan is a prominent mountain and recreational area in Daejeon, South Korea, known for its hiking trails, temples, and city views.
  • 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
Triple: [Island of Usedom, hasPart, Bansin]
Generated description
Bansin is a seaside resort town on Germany’s Baltic Sea coast, known as one of the “Kaiserbäder” (Imperial Spas) on the island of Usedom.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bansin
Target entity description: Bansin is a seaside resort town on Germany’s Baltic Sea coast, known as one of the “Kaiserbäder” (Imperial Spas) on the island of Usedom.
  • A. Paju
    Paju is a city in South Korea near the Demilitarized Zone, known for its historical sites, cultural complexes, and role as a border hub with North Korea.
  • B. Nambui
    Nambui was a Mongol empress consort of the Yuan dynasty and a prominent wife of Kublai Khan, influential in the imperial court after the death of his first empress.
  • C. Bokakhat
    Bokakhat is a small town in Assam, India, known primarily as a gateway and service hub for visitors to Kaziranga National Park.
  • D. Bisharin
    Bisharin are a subgroup of the Beja people, traditionally semi-nomadic pastoralists inhabiting parts of northeastern Sudan and southern Egypt.
  • E. Bomunsan
    Bomunsan is a prominent mountain and recreational area in Daejeon, South Korea, known for its hiking trails, temples, and city views.
  • 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_69ab49e4749c8190813311efd1630f1b completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd20db7e0819096d901eb20ae65e5 completed March 7, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2b9aa5cc81908c2e09ce18f2e98e completed March 9, 2026, 8:20 p.m.
NEDg Description generation batch_69af508c28f48190afc4aa1bc3c9adf3 completed March 9, 2026, 10:58 p.m.
NED2 Entity disambiguation (via description) batch_69af5155f85081908dd4a1859d0f7907 completed March 9, 2026, 11:01 p.m.
Created at: March 6, 2026, 9:46 p.m.