Triple

T11010857
Position Surface form Disambiguated ID Type / Status
Subject Marondera E260242 entity
Predicate formerName P65 FINISHED
Object Marandellas
Marandellas is the former colonial-era name of Marondera, a town in eastern Zimbabwe known as an agricultural and educational center.
 E899459 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: Marandellas | Statement: [Marondera, formerName, Marandellas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marandellas
Context triple: [Marondera, formerName, Marandellas]
  • A. Arandis
    Arandis is a small mining town in Namibia known primarily for its proximity to the Rossing uranium mine.
  • B. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • C. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • 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: Marandellas
Triple: [Marondera, formerName, Marandellas]
Generated description
Marandellas is the former colonial-era name of Marondera, a town in eastern Zimbabwe known as an agricultural and educational center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marandellas
Target entity description: Marandellas is the former colonial-era name of Marondera, a town in eastern Zimbabwe known as an agricultural and educational center.
  • A. Arandis
    Arandis is a small mining town in Namibia known primarily for its proximity to the Rossing uranium mine.
  • B. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • C. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • 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_69d6aa9687448190b28d353b1b6a610e completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797899a388190aa8fe813eac4dba7 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e374ac78348190a8c0a5a7a736b24b completed April 18, 2026, 12:10 p.m.
NEDg Description generation batch_69e378dcc92c8190952d4acfee2a309c completed April 18, 2026, 12:28 p.m.
NED2 Entity disambiguation (via description) batch_69e37be75a588190abb9569ef1e87279 completed April 18, 2026, 12:41 p.m.
Created at: April 8, 2026, 9:25 p.m.