Triple

T17868977
Position Surface form Disambiguated ID Type / Status
Subject Guntersville, Alabama E446779 entity
Predicate namedAfter P63 FINISHED
Object John Gunter 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: John Gunter | Statement: [Guntersville, Alabama, namedAfter, John Gunter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Gunter
Context triple: [Guntersville, Alabama, namedAfter, John Gunter]
  • A. John Gunter chosen
    John Gunter was an individual significant enough in local or regional history that the city of Gunter, Texas, was named in his honor.
  • B. William Leitch
    William Leitch was a 19th-century Scottish astronomer and clergyman noted for being an early theorist of space travel and rocketry.
  • C. Martin P. Catherwood
    Martin P. Catherwood was an American labor and industrial relations expert who served in prominent public roles in New York State government.
  • D. William Girdler
    William Girdler was an American filmmaker best known for his low-budget horror and exploitation films of the 1970s.
  • E. William Gaxton
    William Gaxton was an American stage and film actor best known for his leading roles in Broadway musical comedies during the early to mid-20th century.
  • 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_69d8b9f4c22c819093c2680434472894 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49aa18df881908a8ff40b7de19268 completed April 19, 2026, 9:04 a.m.
Created at: April 10, 2026, 10:18 a.m.