Triple

T34824273
Position Surface form Disambiguated ID Type / Status
Subject Camilo Sanvezzo E1003872 entity
Predicate familyName P18 FINISHED
Object da Silva Sanvezzo
da Silva Sanvezzo is the surname of Brazilian professional footballer Camilo Sanvezzo, known for his goal-scoring exploits in leagues such as Major League Soccer and Liga MX.
E2114427 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: da Silva Sanvezzo | Statement: [Camilo Sanvezzo, familyName, da Silva Sanvezzo]
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: da Silva Sanvezzo
Triple: [Camilo Sanvezzo, familyName, da Silva Sanvezzo]
Generated description
da Silva Sanvezzo is the surname of Brazilian professional footballer Camilo Sanvezzo, known for his goal-scoring exploits in leagues such as Major League Soccer and Liga MX.

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_69f76db717088190811b4e744610f37d completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77ae090388190be38c8fe5d696a39 completed May 3, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37794a75fc81909b909cf6e3599039 completed June 21, 2026, 5:40 a.m.
NEDg Description generation batch_6a3779bf7c3881908b040541f78cd4a0 completed June 21, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_6a377a2101608190b4e7c725785c5201 completed June 21, 2026, 5:44 a.m.
Created at: May 3, 2026, 4 p.m.