Triple

T13637388
Position Surface form Disambiguated ID Type / Status
Subject Marie Gluesenkamp Perez E325883 entity
Predicate stateRepresentedIn P39037 FINISHED
Object Washington E20479 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: Washington | Statement: [Marie Gluesenkamp Perez, stateRepresentedIn, Washington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Washington
Context triple: [Marie Gluesenkamp Perez, stateRepresentedIn, Washington]
  • A. Washington chosen
    Washington is a U.S. state in the Pacific Northwest known for its diverse landscapes, technology industry centered around Seattle, and significant cultural and economic influence on the West Coast.
  • B. Washington
    Washington is a small rural town in Berkshire County in western Massachusetts, known for its forested landscape and quiet, sparsely populated character.
  • C. Washington
    Washington is a rapid transit station on Chicago's 'L' system, formerly serving the CTA Blue Line in the downtown Loop.
  • D. Washington
    Washington is a small town in Dutchess County, New York, known for its rural character and the village of Millbrook within its borders.
  • E. Washington
    Washington is the given first name of Uruguayan former professional footballer and coach Sebastián "Loco" Abreu.
  • 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_69d8076beddc8190a53156f5bea77f5e completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc5a84cc4819098a975e33250c89b completed April 12, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69f794307c288190a0f4629bf5e2b0d9 completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:51 p.m.