Triple

T3111867
Position Surface form Disambiguated ID Type / Status
Subject Manicaland Province E64969 entity
Predicate hasTown P847 FINISHED
Object Mutasa
Mutasa is a town located in Zimbabwe’s eastern Manicaland Province, known for its rural communities and proximity to the Eastern Highlands.
 E327525 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: Mutasa | Statement: [Manicaland Province, hasTown, Mutasa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mutasa
Context triple: [Manicaland Province, hasTown, Mutasa]
  • A. Murambi
    Murambi is a residential suburb of Mutare, a major city in eastern Zimbabwe.
  • B. Thoosa
    Thoosa is a minor sea nymph in Greek mythology, known primarily as the mother of the Cyclops Polyphemus by the sea god Poseidon.
  • C. Datooga
    Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
  • D. Martaban
    Martaban is a historic port city in southern Myanmar that once served as the capital of the Mon kingdom and a key hub in regional maritime trade.
  • E. Temara
    Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
  • 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: Mutasa
Triple: [Manicaland Province, hasTown, Mutasa]
Generated description
Mutasa is a town located in Zimbabwe’s eastern Manicaland Province, known for its rural communities and proximity to the Eastern Highlands.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mutasa
Target entity description: Mutasa is a town located in Zimbabwe’s eastern Manicaland Province, known for its rural communities and proximity to the Eastern Highlands.
  • A. Murambi
    Murambi is a residential suburb of Mutare, a major city in eastern Zimbabwe.
  • B. Thoosa
    Thoosa is a minor sea nymph in Greek mythology, known primarily as the mother of the Cyclops Polyphemus by the sea god Poseidon.
  • C. Datooga
    Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
  • D. Martaban
    Martaban is a historic port city in southern Myanmar that once served as the capital of the Mon kingdom and a key hub in regional maritime trade.
  • E. Temara
    Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada43b0b3c8190a828c9cfcf730ed9 completed March 8, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20397cbdc8190abdd93b502f9577f completed March 12, 2026, 12:06 a.m.
NEDg Description generation batch_69b204a8c5348190a2cb102b08fd6fa5 completed March 12, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_69b205bdf5c881908bc6ef7c3c30df65 completed March 12, 2026, 12:15 a.m.
Created at: March 8, 2026, 3:04 p.m.