Triple

T21697088
Position Surface form Disambiguated ID Type / Status
Subject Department of Pando E535550 entity
Predicate hasTown P847 FINISHED
Object Porvenir 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: Porvenir | Statement: [Department of Pando, hasTown, Porvenir]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Porvenir
Context triple: [Department of Pando, hasTown, Porvenir]
  • A. Porvenir
    Porvenir is a small Chilean town on Tierra del Fuego that serves as an important fishing and service port on the Strait of Magellan.
  • B. Porvenir chosen
    Porvenir is a small town that serves as the capital of Pando Department in northern Bolivia, near the border with Brazil.
  • C. Niquero
    Niquero is a coastal town and municipality in Granma Province, Cuba, known for its historical role in the Cuban Revolution and proximity to important coastal and natural areas.
  • D. Prestea
    Prestea is a mining town in southwestern Ghana known for its significant gold deposits and long history of gold extraction.
  • E. Guandacol
    Guandacol is a small town in northwestern Argentina, located in La Rioja Province and known for its arid landscapes and nearby paleontological sites.
  • 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_69e0c46a6ee481908836e1420fb78c9b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef9b7b8da48190a863ac45eb4465a8 completed April 27, 2026, 5:23 p.m.
Created at: April 16, 2026, 6:45 p.m.