Triple

T16101144
Position Surface form Disambiguated ID Type / Status
Subject Seymour Benzer E390623 entity
Predicate givenName P17 FINISHED
Object Seymour E218368 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: Seymour | Statement: [Seymour Benzer, givenName, Seymour]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seymour
Context triple: [Seymour Benzer, givenName, Seymour]
  • A. Seymour chosen
    Seymour is a masculine given name of English origin that has been borne by various notable figures in fields such as business, politics, and the arts.
  • B. Seymour
    Seymour is a small town in New Haven County, Connecticut, known for its historic industrial roots along the Naugatuck River.
  • C. Seymour
    Seymour is a regional town in central Victoria, Australia, known as a key agricultural and transport hub on the route between Melbourne and Sydney.
  • D. Seymour
    Seymour is a small unincorporated community and suburban area in eastern Tennessee, situated near Knoxville in the foothills of the Great Smoky Mountains.
  • E. Seymour
    Seymour is a socially awkward, middle-aged record collector who becomes a central figure in the coming-of-age graphic novel and film "Ghost World."
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e1ff68686481909517eed4266729ca completed April 17, 2026, 9:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb9d6140819087f9b3dc549c4aec completed May 10, 2026, 2:21 a.m.
Created at: April 10, 2026, 5 a.m.