Triple

T7219254
Position Surface form Disambiguated ID Type / Status
Subject German East Africa E150213 entity
Predicate formerCapital P3417 FINISHED
Object Bagamoyo
Bagamoyo is a historic coastal town in present-day Tanzania that served as a major 19th-century East African trade and colonial center, including as an early administrative hub for German rule.
 E649659 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: Bagamoyo | Statement: [German East Africa, formerCapital, Bagamoyo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bagamoyo
Context triple: [German East Africa, formerCapital, Bagamoyo]
  • A. Mungaka
    Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
  • B. Ngamo
    Ngamo is a West Chadic language spoken primarily in northeastern Nigeria by the Ngamo people.
  • C. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • D. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • E. Buhera
    Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
  • 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: Bagamoyo
Triple: [German East Africa, formerCapital, Bagamoyo]
Generated description
Bagamoyo is a historic coastal town in present-day Tanzania that served as a major 19th-century East African trade and colonial center, including as an early administrative hub for German rule.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bagamoyo
Target entity description: Bagamoyo is a historic coastal town in present-day Tanzania that served as a major 19th-century East African trade and colonial center, including as an early administrative hub for German rule.
  • A. Mungaka
    Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
  • B. Ngamo
    Ngamo is a West Chadic language spoken primarily in northeastern Nigeria by the Ngamo people.
  • C. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • D. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • E. Buhera
    Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
  • 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_69c687effb44819092b95d07d0368c9f completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e9b1a7908190bd215ffb84592e32 completed March 27, 2026, 8:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cc014fb88190818e12b7abe90c0a completed March 28, 2026, 12:39 p.m.
NEDg Description generation batch_69c7ccf3cd4c8190babc9e0e6b9f4371 completed March 28, 2026, 12:43 p.m.
NED2 Entity disambiguation (via description) batch_69c7cd5cedd88190b72df89b068c4483 completed March 28, 2026, 12:45 p.m.
Created at: March 27, 2026, 2:53 p.m.