Triple

T13036098
Position Surface form Disambiguated ID Type / Status
Subject 42 (school) E326563 entity
Predicate hasCampus P116 FINISHED
Object 42 Cape Town E265535 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: 42 Cape Town | Statement: [42 (school), hasCampus, 42 Cape Town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 42 Cape Town
Context triple: [42 (school), hasCampus, 42 Cape Town]
  • A. Cape Town
    Cape Town is a major coastal city in South Africa known for its iconic Table Mountain, diverse culture, and role as the country’s legislative capital.
  • B. Johannesburg, South Africa
    Johannesburg, South Africa is the country’s largest city and economic hub, known for its role in the gold mining industry and as a major urban center in Gauteng province.
  • C. Johannesburg
    Johannesburg is a champion Thoroughbred racehorse and successful sire, best known for his unbeaten two-year-old season and victory in the 2001 Breeders’ Cup Juvenile.
  • D. Durban
    Durban is a major coastal city in South Africa known for its busy port, subtropical climate, and significant Indian community.
  • E. Muizenberg chosen
    Muizenberg is a seaside suburb of Cape Town, South Africa, known for its popular surfing beach and colorful Victorian beach huts.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97f2a71a0819098bb6cf8a4b2208a completed April 10, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff02c1448190b0b41beb427fae23 completed May 3, 2026, 7:53 a.m.
Created at: April 9, 2026, 8:55 p.m.