Triple

T8788941
Position Surface form Disambiguated ID Type / Status
Subject Bohuslav Martinů E209110 entity
Predicate residence P75 FINISHED
Object Princeton E244565 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: Princeton | Statement: [Bohuslav Martinů, residence, Princeton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Princeton
Context triple: [Bohuslav Martinů, residence, Princeton]
  • A. Princeton chosen
    Princeton is a historic New Jersey town best known as the site of the pivotal 1777 Battle of Princeton during the American Revolutionary War and as home to Princeton University.
  • B. Princeton
    Princeton is a small city in southern West Virginia known as a regional hub for commerce, education, and access to outdoor recreation in the Appalachian region.
  • C. Princeton
    Princeton is a small town in Johnston County, North Carolina, known for its rural character and tight-knit community.
  • D. Princeton
    Princeton is a small city in central Wisconsin known for its historic downtown, outdoor recreation along the Fox River, and popular weekly flea market.
  • E. Princeton
    Princeton is a small but rapidly growing city in Collin County, Texas, situated in the northeastern part of the Dallas–Fort Worth metropolitan area.
  • 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_69ca836168108190bb43d3dc235c1f55 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f8b0c108190af53d4bb9b132c5c completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf890b49048190b49c784f84e23496 completed April 3, 2026, 9:31 a.m.
Created at: March 30, 2026, 6:43 p.m.