Triple

T12038378
Position Surface form Disambiguated ID Type / Status
Subject Kendall/MIT E286597 entity
Predicate adjacentStationOnRedLine P24861 FINISHED
Object Charles/MGH E5258 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: Charles/MGH | Statement: [Kendall/MIT, adjacentStationOnRedLine, Charles/MGH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charles/MGH
Context triple: [Kendall/MIT, adjacentStationOnRedLine, Charles/MGH]
  • A. Charles/MGH chosen
    Charles/MGH is an elevated Massachusetts Bay Transportation Authority subway station in Boston located near Massachusetts General Hospital and the Charles River.
  • B. MGH
    MGH is the commonly used abbreviation for Michael Garron Hospital, a community teaching hospital in Toronto, Canada.
  • C. MGH
    MGH is the vehicle registration code for the town of Bad Mergentheim in the German state of Baden-Württemberg.
  • D. Mass General Brigham
    Mass General Brigham is a large, Boston-based nonprofit healthcare system and academic medical network that includes Massachusetts General Hospital and Brigham and Women’s Hospital among its flagship institutions.
  • E. Boston Medical Center
    Boston Medical Center is a major academic medical center and safety-net hospital in Boston known for providing comprehensive care and serving a large underserved population.
  • 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_69d6ab4669e48190b59246358b0383ab completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9040a8be881908f4841145a7b4e86 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49d8a9af881909e28783b0d83ed82 completed May 1, 2026, 12:33 p.m.
Created at: April 8, 2026, 9:47 p.m.