Triple

T9699913
Position Surface form Disambiguated ID Type / Status
Subject Luca Cardelli E234748 entity
Predicate name P16 FINISHED
Object Luca Cardelli E234748 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: Luca Cardelli | Statement: [Luca Cardelli, name, Luca Cardelli]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luca Cardelli
Context triple: [Luca Cardelli, name, Luca Cardelli]
  • A. Luca Cardelli chosen
    Luca Cardelli is an Italian computer scientist known for his influential work in type theory, programming language design, and the development of the Modula-3 and Polyphonic C# languages.
  • B. Gordon Plotkin
    Gordon Plotkin is a British computer scientist renowned for his foundational contributions to programming language semantics and domain theory.
  • C. Andre Scedrov
    Andre Scedrov is a mathematician known for his work in category theory, logic, and theoretical computer science.
  • D. David L. Parnas
    David L. Parnas is a pioneering software engineer and computer scientist best known for introducing key concepts in software modularity and information hiding that shaped modern software engineering.
  • E. Gerard J. Holzmann
    Gerard J. Holzmann is a computer scientist best known for creating the SPIN model checker and for his influential work in formal verification and software reliability.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d6eab0c8190abac1b009d625975 completed April 1, 2026, 10:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1912a551c8190bd6b48790f117f71 completed April 4, 2026, 10:31 p.m.
Created at: March 30, 2026, 8:18 p.m.