Triple

T15443182
Position Surface form Disambiguated ID Type / Status
Subject Piero Ferrari E369958 entity
Predicate mother P120 FINISHED
Object Lina Lardi
Lina Lardi was the partner of Enzo Ferrari and the mother of Piero Ferrari, connected to the founding family of the Ferrari automotive marque.
E1157869 NE FINISHED

How this triple was built (4 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: Lina Lardi | Statement: [Piero Ferrari, mother, Lina Lardi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lina Lardi
Context triple: [Piero Ferrari, mother, Lina Lardi]
  • A. Lina Esco
    Lina Esco is an American actress, filmmaker, and activist best known for her roles in television series such as "S.W.A.T." and for her advocacy on social and political issues.
  • B. Lina De Cesare
    Lina De Cesare is a Canadian business executive best known as a co-founder and longtime senior leader of the leisure airline and tour operator Air Transat.
  • C. Lina Romay
    Lina Romay was a Mexican-born actress and singer best known as a featured performer in Hollywood films of the 1940s and 1950s.
  • D. Lilia Vetti
    Lilia Vetti was the wife of famed French singer and actor Tino Rossi.
  • E. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Lina Lardi
Triple: [Piero Ferrari, mother, Lina Lardi]
Generated description
Lina Lardi was the partner of Enzo Ferrari and the mother of Piero Ferrari, connected to the founding family of the Ferrari automotive marque.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lina Lardi
Target entity description: Lina Lardi was the partner of Enzo Ferrari and the mother of Piero Ferrari, connected to the founding family of the Ferrari automotive marque.
  • A. Lina Esco
    Lina Esco is an American actress, filmmaker, and activist best known for her roles in television series such as "S.W.A.T." and for her advocacy on social and political issues.
  • B. Lina De Cesare
    Lina De Cesare is a Canadian business executive best known as a co-founder and longtime senior leader of the leisure airline and tour operator Air Transat.
  • C. Lina Romay
    Lina Romay was a Mexican-born actress and singer best known as a featured performer in Hollywood films of the 1940s and 1950s.
  • D. Lilia Vetti
    Lilia Vetti was the wife of famed French singer and actor Tino Rossi.
  • E. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • F. None of above. chosen

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_69d85a19180081909925012fbf4e62a3 completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ef55f5c8190a32b1b6ad1daf454 completed April 16, 2026, 1:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff21ab80288190a1a4df8b714bba66 completed May 9, 2026, 11:59 a.m.
NEDg Description generation batch_69ff2404d948819097b4e4e32489bb0d completed May 9, 2026, 12:09 p.m.
NED2 Entity disambiguation (via description) batch_69ff253d8428819092dab16b040c6e88 completed May 9, 2026, 12:14 p.m.
Created at: April 10, 2026, 3:21 a.m.