Triple

T23318363
Position Surface form Disambiguated ID Type / Status
Subject Laguna Blanca E590775 entity
Predicate hasFauna P950 FINISHED
Object vicuna NE NERFINISHED

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: vicuna | Statement: [Laguna Blanca, hasFauna, vicuna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: vicuna
Context triple: [Laguna Blanca, hasFauna, vicuna]
  • A. vicuña chosen
    The vicuña is a wild South American camelid native to the high Andes, known for its exceptionally fine and valuable wool.
  • B. Vichuquén
    Vichuquén is a small Chilean town and municipality known for its colonial architecture and proximity to Lake Vichuquén in the Maule Region.
  • C. Vicuña
    The vicuña is a small, wild South American camelid native to the high Andes, renowned for its exceptionally fine and valuable wool.
  • D. Vicuña
    Vicuña is a small Chilean town in the Elqui Valley, known for its clear skies, observatories, and production of pisco.
  • E. Wampis
    Wampis is an indigenous language of the Jivaroan family spoken by the Wampis people in the Amazonian region of northern Peru.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d1d32188190948eb76909d1dcc3 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1978372948190bad066893c4e0768 completed April 29, 2026, 5:30 a.m.
Created at: April 17, 2026, 5:07 p.m.