Triple

T12504414
Position Surface form Disambiguated ID Type / Status
Subject General Felipe Varela Department E298910 entity
Predicate hasNotableLocality P40907 FINISHED
Object Aicuña E989987 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: Aicuña | Statement: [General Felipe Varela Department, hasNotableLocality, Aicuña]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aicuña
Context triple: [General Felipe Varela Department, hasNotableLocality, Aicuña]
  • A. Aicuña chosen
    Aicuña is a small rural village in northwestern Argentina, known for its traditional adobe architecture and preserved cultural heritage.
  • B. Agoncillo
    Agoncillo is a lakeside municipality in the Philippine province of Batangas known for its proximity to Taal Lake and Taal Volcano.
  • C. Itunyoso
    Itunyoso is a region associated with the Trique people, an indigenous group of Oaxaca, Mexico, known for their distinct language and cultural traditions.
  • D. Tamuín
    Tamuín is a municipality in the Mexican state of San Luis Potosí, known for its Huastec cultural heritage and proximity to important archaeological and natural sites.
  • E. Bassignana
    Bassignana is a municipality in the Piedmont region of northern Italy, situated near the confluence of the Tanaro and Po rivers.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94dfcea188190a929db1aabe1a286 completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65eac74608190a6f1941ed5a05212 completed May 2, 2026, 8:29 p.m.
Created at: April 8, 2026, 9:57 p.m.