Triple

T1347070
Position Surface form Disambiguated ID Type / Status
Subject Haarlem E28594 entity
Predicate hasTwinTown P919 FINISHED
Object Mutare E10728 NE FINISHED

How this triple was built (2 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: Mutare | Statement: [Haarlem, hasTwinTown, Mutare]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mutare
Context triple: [Haarlem, hasTwinTown, Mutare]
  • A. Mutare chosen
    Mutare is a major city in eastern Zimbabwe, serving as the capital of Manicaland Province and an important commercial and transport hub near the border with Mozambique.
  • B. Masvingo
    Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
  • C. 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.
  • D. Masvingo Province
    Masvingo Province is a region in southeastern Zimbabwe known for encompassing the historic Great Zimbabwe ruins and the city of Masvingo.
  • E. Mutare Airport
    Mutare Airport is a small public airport serving the city of Mutare in eastern Zimbabwe, primarily handling domestic and regional flights.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a49854eb3481908c7d56b2e449a290 completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c2406c488190b2c04d54d9c5e94c completed March 1, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69acde1555048190b73c1616d1979b08 completed March 8, 2026, 2:25 a.m.
Created at: March 1, 2026, 7:56 p.m.