Triple

T20672175
Position Surface form Disambiguated ID Type / Status
Subject Heidelberg Township, Lebanon County, Pennsylvania E508054 entity
Predicate county P75 FINISHED
Object Lebanon County NE NERFINISHED

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: Lebanon County | Statement: [Heidelberg Township, Lebanon County, Pennsylvania, county, Lebanon County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lebanon County
Context triple: [Heidelberg Township, Lebanon County, Pennsylvania, county, Lebanon County]
  • A. Lebanon County chosen
    Lebanon County is a county in south-central Pennsylvania known for its mix of rural farmland, small towns, and historical communities such as the city of Lebanon.
  • B. Greene County
    Greene County is a rural county in western Alabama known for its historical significance in the Black Belt region and its predominantly African American population.
  • C. Greene County
    Greene County is a rural county in eastern North Carolina known for its agricultural landscape and small-town communities.
  • D. Greene County
    Greene County is a rural county in western Illinois known for its agricultural landscape and small communities.
  • E. Greene County
    Greene County is a rural county in eastern New York State known for encompassing a significant portion of the scenic Catskill Mountains.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c1164881909a3bf1e3ddb2bc32 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6b5cb1fc88190805f623e93a70368 completed April 20, 2026, 11:24 p.m.
Created at: April 16, 2026, 11:44 a.m.