Triple

T17096888
Position Surface form Disambiguated ID Type / Status
Subject 1914–15 Star E414871 entity
Predicate awardingCountry P2729 FINISHED
Object South Africa E3669 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: South Africa | Statement: [1914–15 Star, awardingCountry, South Africa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: South Africa
Context triple: [1914–15 Star, awardingCountry, South Africa]
  • A. South Africa chosen
    South Africa is a country at the southern tip of the African continent, known for its cultural and linguistic diversity, complex history of apartheid and democratic transition, and significant economic and political influence in the region.
  • B. Transkei, South Africa
    Transkei, South Africa was a former bantustan in the southeastern part of the country, historically designated for Xhosa-speaking people during the apartheid era.
  • C. Jong Suid-Afrika
    Jong Suid-Afrika was the original name of the Afrikaner Broederbond, a secretive and influential Afrikaner nationalist organization in South Africa.
  • D. Kwaluseni
    Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
  • E. Tiszanána
    Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbfe92988190aa066745ca9791d5 completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012eedfd7c8190b267dedd403f5f2b completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:35 a.m.