Triple

T11099517
Position Surface form Disambiguated ID Type / Status
Subject Anatole de Baudot E262466 entity
Predicate familyName P18 FINISHED
Object de Baudot E262466 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: de Baudot | Statement: [Anatole de Baudot, familyName, de Baudot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: de Baudot
Context triple: [Anatole de Baudot, familyName, de Baudot]
  • A. Anatole de Baudot chosen
    Anatole de Baudot was a French architect and theorist known for pioneering the use of reinforced concrete and for his influential role in late 19th-century ecclesiastical and public architecture.
  • B. Gottfried Ungerboeck
    Gottfried Ungerboeck is an electrical engineer best known for pioneering trellis-coded modulation, a breakthrough in digital communications that significantly improved data transmission reliability and efficiency.
  • C. Burrus
    Burrus is a variant form of the name Burr, used as a personal or family name.
  • D. Postel
    Postel is a surname most prominently associated with Jon Postel, a pioneering computer scientist and key architect of the early Internet.
  • E. Robert M. Fano
    Robert M. Fano was an influential Italian-American computer scientist and information theorist known for his foundational contributions to coding theory and for co-developing Shannon–Fano coding.
  • 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_69d6aa9a40d88190a373e2c7e48285db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79a0c46308190889b94c23ebaca62 completed April 9, 2026, 12:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e7eca9bc8190b43bae081d97d804 completed April 18, 2026, 8:22 p.m.
Created at: April 8, 2026, 9:27 p.m.