Triple

T841544
Position Surface form Disambiguated ID Type / Status
Subject Southern Africa E18188 entity
Predicate hasLandmark P105 FINISHED
Object Table Mountain E53885 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: Table Mountain | Statement: [Southern Africa, hasLandmark, Table Mountain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Table Mountain
Context triple: [Southern Africa, hasLandmark, Table Mountain]
  • A. Table Mountain chosen
    Table Mountain is a flat-topped mountain overlooking Cape Town in South Africa, famous for its distinctive plateau, rich biodiversity, and status as a major natural landmark and tourist attraction.
  • B. Mount Victoria
    Mount Victoria is a small historic village and mountain locality at the western edge of the Blue Mountains in New South Wales, Australia, known for its heritage architecture and scenic views.
  • C. Star Mountain
    Star Mountain is the English translation of the Nahuatl name for Pico de Orizaba, the highest volcano in North America and a prominent peak in Mexico.
  • D. Mount Lee
    Mount Lee is a hill in the Hollywood Hills of Los Angeles best known as the site overlooking the iconic Hollywood Sign.
  • E. Elephant Mountain
    Elephant Mountain is a popular hiking spot in Taipei known for its short trail and panoramic views of the city skyline and Taipei 101.
  • 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_69a49389f44881909a608fb27d89f247 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4abe6f0dc8190a1bebb5e21f4ceac completed March 1, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7b8472f188190b470893c76b20ccf completed March 4, 2026, 4:42 a.m.
Created at: March 1, 2026, 7:38 p.m.