Triple

T32820789
Position Surface form Disambiguated ID Type / Status
Subject Farroupilha Revolution E839428 entity
Predicate leader P981 FINISHED
Object Antônio de Souza Neto
Antônio de Souza Neto was a Brazilian military officer and revolutionary figure best known for his prominent role in the 19th-century separatist movement in Rio Grande do Sul.
E2028721 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: Antônio de Souza Neto | Statement: [Farroupilha Revolution, leader, Antônio de Souza Neto]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Antônio de Souza Neto
Triple: [Farroupilha Revolution, leader, Antônio de Souza Neto]
Generated description
Antônio de Souza Neto was a Brazilian military officer and revolutionary figure best known for his prominent role in the 19th-century separatist movement in Rio Grande do Sul.

Provenance (5 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_69f3493df9008190a8f5d843dcd77704 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cdd5aab48190bf7226caea67c909 completed May 3, 2026, 4:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34c66e655c8190b9ad180507f6067f completed June 19, 2026, 4:32 a.m.
NEDg Description generation batch_6a34c84409a88190a8eaaf78b0fa0666 completed June 19, 2026, 4:40 a.m.
NED2 Entity disambiguation (via description) batch_6a34c8ee94288190a861ceefa0941d53 completed June 19, 2026, 4:43 a.m.
Created at: May 1, 2026, 1:15 a.m.