Triple

T17992871
Position Surface form Disambiguated ID Type / Status
Subject Office of the City Manager of Watertown E430419 entity
Predicate jurisdiction P82 FINISHED
Object Watertown 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: Watertown | Statement: [Office of the City Manager of Watertown, jurisdiction, Watertown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Watertown
Context triple: [Office of the City Manager of Watertown, jurisdiction, Watertown]
  • A. Watertown
    Watertown is a principal city in northeastern South Dakota known as a regional commercial center and home to Lake Kampeska and the Redlin Art Center.
  • B. Watertown
    Watertown is a small city in Wilson County, Tennessee, known for its historic downtown and role as a regional community hub.
  • C. Watertown chosen
    Watertown is a small American city governed under a council–manager system, with a professional city manager overseeing its municipal operations.
  • D. Watertown, Massachusetts
    Watertown, Massachusetts is a historic suburban city just west of Boston, known for its early industrial development and significant Armenian-American community.
  • E. City of Watertown
    The City of Watertown is a small upstate New York city that serves as a regional hub for education, commerce, and services in Jefferson County.
  • 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_69d8b90364248190a37381adea932f42 completed April 10, 2026, 8:46 a.m.
NER Named-entity recognition batch_69e4b2a0f8588190b6090c7cce60a35f completed April 19, 2026, 10:46 a.m.
Created at: April 10, 2026, 10:23 a.m.