Triple

T6993772
Position Surface form Disambiguated ID Type / Status
Subject Tom Luddy E162150 entity
Predicate familyName P18 FINISHED
Object Luddy E253665 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: Luddy | Statement: [Tom Luddy, familyName, Luddy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luddy
Context triple: [Tom Luddy, familyName, Luddy]
  • A. Krannert
    Krannert is a surname most prominently associated with American philanthropists and the namesake of several educational and cultural institutions.
  • B. Luddy School of Informatics, Computing, and Engineering chosen
    The Luddy School of Informatics, Computing, and Engineering is a leading Indiana University school focused on computer science, informatics, data science, and engineering education and research.
  • C. Illinois Institute of Technology
    Illinois Institute of Technology is a private research university in Chicago known for its strong engineering, architecture, and technology programs.
  • D. Indiana Tech
    Indiana Tech is a private university known for its career-focused programs in engineering, computer science, business, and criminal justice.
  • E. Devry
    Devry is a surname most notably associated with American actress Elaine Devry, who appeared in numerous films and television shows from the 1950s onward.
  • 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_69c68856d7808190ab33ee914640281b completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dbeaa88c8190a49f8504c1793e1f completed March 27, 2026, 7:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a1adec88190a769ec7af0fa7b51 completed March 28, 2026, 5:41 a.m.
Created at: March 27, 2026, 2:32 p.m.