Triple

T6471361
Position Surface form Disambiguated ID Type / Status
Subject Seaside Park E142359 entity
Predicate borders P224 FINISHED
Object Berkeley Township E366414 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: Berkeley Township | Statement: [Seaside Park, borders, Berkeley Township]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berkeley Township
Context triple: [Seaside Park, borders, Berkeley Township]
  • A. Warren Township
    Warren Township is a rural civil township located within Poweshiek County in the U.S. state of Iowa.
  • B. Butler Township
    Butler Township is a rural municipality in Luzerne County, Pennsylvania, known for its small communities, forests, and proximity to the Pocono region.
  • C. Harris Township
    Harris Township is a small rural municipality located within Ontario’s Timiskaming District in Canada.
  • D. Waterford Township chosen
    Waterford Township is a municipality in Camden County, New Jersey, known for its suburban communities and proximity to the Pine Barrens.
  • E. Peters Township
    Peters Township is a suburban municipality in Washington County, Pennsylvania, known for its residential communities and proximity to the Pittsburgh metropolitan area.
  • 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_69c008d3bf4c8190bcf798c5ba9d6fb3 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06a2fd4248190a789bf0301e2860a completed March 22, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d50423808190817cad8601490a77 completed March 27, 2026, 7:05 p.m.
Created at: March 22, 2026, 4:50 p.m.