Triple

T32909618
Position Surface form Disambiguated ID Type / Status
Subject Vivaldão Stadium E841836 entity
Predicate namedAfter P63 FINISHED
Object Gilberto Mestrinho de Medeiros Raposo
Gilberto Mestrinho de Medeiros Raposo was a prominent Brazilian politician who served multiple terms as governor of the state of Amazonas.
E2035387 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: Gilberto Mestrinho de Medeiros Raposo | Statement: [Vivaldão Stadium, namedAfter, Gilberto Mestrinho de Medeiros Raposo]
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: Gilberto Mestrinho de Medeiros Raposo
Triple: [Vivaldão Stadium, namedAfter, Gilberto Mestrinho de Medeiros Raposo]
Generated description
Gilberto Mestrinho de Medeiros Raposo was a prominent Brazilian politician who served multiple terms as governor of the state of Amazonas.

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_69f34946a5208190bbd79f0fec4323bd completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d09d9d008190981c4394ab0f2a89 completed May 3, 2026, 4:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34effd975c819087c3af0a9703842c completed June 19, 2026, 7:30 a.m.
NEDg Description generation batch_6a34fb0302d88190b0ae27220c8bd833 completed June 19, 2026, 8:17 a.m.
NED2 Entity disambiguation (via description) batch_6a34fb5f5a048190b29c2dca71573603 completed June 19, 2026, 8:18 a.m.
Created at: May 1, 2026, 1:19 a.m.