Triple

T9929
Position Surface form Disambiguated ID Type / Status
Subject Chelsea, Massachusetts E201 entity
Predicate state P87 FINISHED
Object Massachusetts E37 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: Massachusetts | Statement: [Chelsea, Massachusetts, state, Massachusetts]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Massachusetts
Context triple: [Chelsea, Massachusetts, state, Massachusetts]
  • A. Massachusetts chosen
    Massachusetts is a U.S. state in New England known for its pivotal role in American history, prestigious universities, and major cultural and economic centers like Boston.
  • B. New Hampshire
    New Hampshire is a small New England state in the northeastern United States known for its mountainous landscapes, early presidential primary, and “Live Free or Die” motto.
  • C. Connecticut
    Connecticut is a small New England state in the northeastern United States known for its colonial history, affluent suburbs, and role as a financial and educational hub.
  • D. Maryland
    Maryland is a Mid-Atlantic U.S. state known for its Chesapeake Bay shoreline, colonial history, and proximity to the nation’s capital.
  • E. Vermont
    Vermont is a small, rural New England state in the northeastern United States, known for its Green Mountains, maple syrup production, and picturesque towns.
  • 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_69a23bb612708190b09f25385e4b63d1 completed Feb. 28, 2026, 12:49 a.m.
NER Named-entity recognition batch_69a23ff2f0508190806663ab2463cd41 completed Feb. 28, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3232a3f8c81909aaf3479415828f4 completed Feb. 28, 2026, 5:17 p.m.
Created at: Feb. 28, 2026, 12:54 a.m.