Triple

T21716447
Position Surface form Disambiguated ID Type / Status
Subject Casita E536042 entity
Predicate affectedSettlement P8239 FINISHED
Object El 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: El Porvenir | Statement: [Casita, affectedSettlement, El Porvenir]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: El Porvenir
Context triple: [Casita, affectedSettlement, El Porvenir]
  • A. El Porvenir chosen
    El Porvenir is a small Caribbean coastal town in Panama that serves as the administrative center of the indigenous Guna Yala comarca.
  • B. El Porvenir
    El Porvenir is a locality in the municipality of Ensenada in Baja California, Mexico, known for its role in the wine-producing Guadalupe Valley region.
  • C. El Porvenir
    El Porvenir is a town in the Atlántida Department of northern Honduras, known as a small coastal community near the Caribbean Sea.
  • D. Guayaramerín
    Guayaramerín is a Bolivian town and river port in the Beni Department, located on the Mamoré River near the border with Brazil.
  • E. Avanceña
    Avanceña is a Filipino surname associated with several notable figures in the Philippines, including public servants and cultural personalities.
  • 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_69e0c46c6dd88190a595375fa6ebd701 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69efd96ab4c88190b76f4a6b7c855039 completed April 27, 2026, 9:47 p.m.
Created at: April 16, 2026, 6:47 p.m.