Triple

T22294202
Position Surface form Disambiguated ID Type / Status
Subject Municipality of Cajeme E551074 entity
Predicate namedAfter P63 FINISHED
Object Cajeme 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: Cajeme | Statement: [Municipality of Cajeme, namedAfter, Cajeme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cajeme
Context triple: [Municipality of Cajeme, namedAfter, Cajeme]
  • A. Cajeme chosen
    Cajeme is a major municipality and agricultural and industrial center in the southern part of the Mexican state of Sonora, best known for its main city Ciudad Obregón.
  • B. Cajamar
    Cajamar is a municipality in the state of São Paulo, Brazil, known for its strategic location within the São Paulo metropolitan region and its growing industrial and logistics sectors.
  • C. Guayzimi
    Guayzimi is a small town in southeastern Ecuador that serves as an administrative and commercial center in the Amazonian region of Zamora-Chinchipe Province.
  • D. Pacasmayo
    Pacasmayo is a coastal city in northern Peru known for its long pier, surfing beaches, and colonial-era architecture.
  • E. Cáqueza
    Cáqueza is a small municipality and town in the Andean region of central Colombia, known for its rural landscapes and proximity to Bogotá in the department of Cundinamarca.
  • 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_69e11e45fb848190a1b2ae21296e3a5f completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1560f06008190b58e71f7c1bd46f7 completed April 29, 2026, 12:51 a.m.
Created at: April 16, 2026, 8:41 p.m.