Triple

T15271007
Position Surface form Disambiguated ID Type / Status
Subject Monagas E365019 entity
Predicate borders P224 FINISHED
Object Anzoátegui E993346 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: Anzoátegui | Statement: [Monagas, borders, Anzoátegui]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anzoátegui
Context triple: [Monagas, borders, Anzoátegui]
  • A. Anzoátegui chosen
    Anzoátegui is a northeastern Venezuelan state known for its Caribbean coastline, oil industry, and diverse landscapes ranging from coastal plains to mountainous areas.
  • B. Rurrenabaque
    Rurrenabaque is a small Bolivian town known as a popular gateway to the Amazon rainforest and nearby Madidi National Park.
  • C. Moxos Province
    Moxos Province is an administrative division in Bolivia’s Beni Department, known for its Amazonian lowlands, indigenous Moxeño culture, and towns such as San Ignacio de Moxos.
  • D. Naguanagua
    Naguanagua is a suburban municipality and city in the state of Carabobo, Venezuela, known for its residential areas, commercial centers, and proximity to the regional capital Valencia.
  • E. Orellana Province
    Orellana Province is an Amazonian region in northeastern Ecuador known for its vast tropical rainforests, rich biodiversity, and significant oil reserves.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0094eac848190a1740ae1aa6b28e0 completed April 15, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff01e092408190aec1561e78ea0acb completed May 9, 2026, 9:44 a.m.
Created at: April 10, 2026, 3:14 a.m.