Triple

T11953289
Position Surface form Disambiguated ID Type / Status
Subject Black, Alabama E284481 entity
Predicate county P75 FINISHED
Object Geneva County E134469 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: Geneva County | Statement: [Black, Alabama, county, Geneva County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Geneva County
Context triple: [Black, Alabama, county, Geneva County]
  • A. Geneva County chosen
    Geneva County is a rural county in southeastern Alabama known for its agriculture and small-town communities near the Florida state line.
  • B. Berrien County
    Berrien County is a rural county in south-central Georgia, United States, known for its agricultural economy and small-town communities.
  • C. Portage County
    Portage County is a county in central Wisconsin known for its mix of agricultural land, small communities, and the city of Stevens Point as its county seat.
  • D. Portage County
    Portage County is a county in northeastern Ohio known for its mix of rural communities, small cities, and proximity to the Akron and Cleveland metropolitan areas.
  • E. Huron County
    Huron County is a predominantly rural county in southwestern Ontario, Canada, known for its agriculture, small towns, and Lake Huron shoreline.
  • 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_69d6ab2db38c8190b1f0ed6663ef8ada completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90365da288190a132703df563de23 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4590584608190bc00840c43f115a0 completed May 1, 2026, 7:40 a.m.
Created at: April 8, 2026, 9:45 p.m.