Triple

T30425416
Position Surface form Disambiguated ID Type / Status
Subject Vila Isabel E774013 entity
Predicate hasMainStreet P461 FINISHED
Object Avenida 28 de Setembro
Avenida 28 de Setembro is a principal thoroughfare in the Vila Isabel neighborhood of Rio de Janeiro, known for its commercial activity and local cultural life.
E1923464 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: Avenida 28 de Setembro | Statement: [Vila Isabel, hasMainStreet, Avenida 28 de Setembro]
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: Avenida 28 de Setembro
Triple: [Vila Isabel, hasMainStreet, Avenida 28 de Setembro]
Generated description
Avenida 28 de Setembro is a principal thoroughfare in the Vila Isabel neighborhood of Rio de Janeiro, known for its commercial activity and local cultural life.

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_69f22491ba248190b9a4776ca8e42d02 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f686678c388190b1ffcbfb8f964e22 completed May 2, 2026, 11:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2863c1579c8190a89a1e4f7d8ee0d9 completed June 9, 2026, 7:04 p.m.
NEDg Description generation batch_6a2867f80e2c81909ea8c4da72eb9753 completed June 9, 2026, 7:22 p.m.
NED2 Entity disambiguation (via description) batch_6a286885f2cc8190bc4de1e4f7239f4e completed June 9, 2026, 7:24 p.m.
Created at: April 29, 2026, 8:06 p.m.