Triple

T15012544
Position Surface form Disambiguated ID Type / Status
Subject WA-04 E377872 entity
Predicate containsCounty P5971 FINISHED
Object Grant County E412059 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: Grant County | Statement: [WA-04, containsCounty, Grant County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grant County
Context triple: [WA-04, containsCounty, Grant County]
  • A. Grant County chosen
    Grant County is a county in central Washington State known for its agricultural production, reservoirs, and outdoor recreation areas.
  • B. Grant County
    Grant County is a rural county in eastern West Virginia known for its mountainous terrain, outdoor recreation areas, and small communities.
  • C. Grant County
    Grant County is a county in east-central Indiana known for its small towns, agricultural landscape, and historical ties to figures like James Dean.
  • D. Mason County
    Mason County is a rural county in central Texas known for its ranching, hunting, and the historic town of Mason.
  • E. Mason County
    Mason County is a county in western Washington State known for its forests, waterways, and location along the southern reaches of Puget Sound.
  • 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_69d85cd3a3c881908c71fc424d459c17 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded7613cec8190ac25e3f68c5d0edf completed April 15, 2026, 12:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f24967c8190b0bdb84b88a0aaa3 completed May 9, 2026, 4:21 p.m.
Created at: April 10, 2026, 2:55 a.m.