Triple

T2353864
Position Surface form Disambiguated ID Type / Status
Subject Mashonaland E47508 entity
Predicate hasMajorCity P316 FINISHED
Object Bindura
Bindura is a town in northern Zimbabwe that serves as the administrative and commercial center of the surrounding mining and agricultural region.
 E257664 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: Bindura | Statement: [Mashonaland, hasMajorCity, Bindura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bindura
Context triple: [Mashonaland, hasMajorCity, Bindura]
  • A. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • B. 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.
  • C. Mpanda
    Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
  • D. Nzega
    Nzega is a town and district in western Tanzania that serves as an important commercial and transport hub within the Tabora Region.
  • E. Gwanda
    Gwanda is a small Zimbabwean town that serves as an administrative and commercial hub in the country’s arid south, known historically for cattle ranching and gold mining.
  • 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: Bindura
Triple: [Mashonaland, hasMajorCity, Bindura]
Generated description
Bindura is a town in northern Zimbabwe that serves as the administrative and commercial center of the surrounding mining and agricultural region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bindura
Target entity description: Bindura is a town in northern Zimbabwe that serves as the administrative and commercial center of the surrounding mining and agricultural region.
  • A. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • B. 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.
  • C. Mpanda
    Mpanda is a town in western Tanzania that serves as an important administrative and commercial hub for the surrounding region.
  • D. Nzega
    Nzega is a town and district in western Tanzania that serves as an important commercial and transport hub within the Tabora Region.
  • E. Gwanda
    Gwanda is a small Zimbabwean town that serves as an administrative and commercial hub in the country’s arid south, known historically for cattle ranching and gold mining.
  • 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_69a88a1b678c8190bce986922ba60ce0 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc6fa6ecc8190821c9d5db341cf19 completed March 7, 2026, 6:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae9633ce3c81908581e7e0f8211ac1 completed March 9, 2026, 9:43 a.m.
NEDg Description generation batch_69ae971aa3bc8190b0b8b216106dca90 completed March 9, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ae9795cf048190bc7a01ef86c12138 completed March 9, 2026, 9:49 a.m.
Created at: March 4, 2026, 7:54 p.m.