Triple

T22123379
Position Surface form Disambiguated ID Type / Status
Subject Giovanni Profumo E546728 entity
Predicate employer P7 FINISHED
Object UniCredit NE NERFINISHED

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: UniCredit | Statement: [Giovanni Profumo, employer, UniCredit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UniCredit
Context triple: [Giovanni Profumo, employer, UniCredit]
  • A. UniCredit chosen
    UniCredit is a major Italian global banking and financial services group headquartered in Milan, with a strong presence across Europe.
  • B. Intesa Sanpaolo
    Intesa Sanpaolo is one of Italy’s largest banking groups, offering retail and corporate banking, asset management, and financial services across Europe and internationally.
  • C. Banca Commerciale Italiana
    Banca Commerciale Italiana was a major Italian commercial bank, historically one of the country’s most influential financial institutions before its merger into larger banking groups.
  • D. Banca IMI
    Banca IMI is the investment banking and capital markets arm of the Italian banking group Intesa Sanpaolo.
  • E. Raiffeisen banks
    Raiffeisen banks are a network of cooperative, locally owned financial institutions that provide retail banking services, primarily to individuals and small businesses, especially in rural and regional areas.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e39bf348190b541bfa16a7b71e0 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1297f3fb48190b6aaca18b40c37ab completed April 28, 2026, 9:41 p.m.
Created at: April 16, 2026, 8:31 p.m.