Triple

T9810805
Position Surface form Disambiguated ID Type / Status
Subject Issaquah High School E238264 entity
Predicate stateServed P82 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: [Issaquah High School, stateServed, Washington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Washington
Context triple: [Issaquah High School, stateServed, 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 town in Dutchess County, New York, known for its rural character and the village of Millbrook within its borders.
  • C. Washington
    Washington is a small rural town in Berkshire County in western Massachusetts, known for its forested landscape and quiet, sparsely populated character.
  • D. Washington
    Washington is a town in the City of Sunderland in Tyne and Wear, England, historically part of County Durham and often noted for its links to the family of George Washington.
  • 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_69ca84defac48190abc1148804f184c1 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb2214a7c8190b516acf64e2b85db completed April 2, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1e40ba788819084c3c01518a72e4e completed April 5, 2026, 4:24 a.m.
Created at: March 30, 2026, 8:30 p.m.