Triple
T21852465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Line trunk |
E539537
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Charles/MGH |
—
|
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: Charles/MGH | Statement: [Red Line trunk, hasStation, Charles/MGH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charles/MGH Context triple: [Red Line trunk, hasStation, 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 vehicle registration code for the town of Bad Mergentheim in the German state of Baden-Württemberg.
-
C.
MGH
MGH is the commonly used abbreviation for Michael Garron Hospital, a community teaching hospital in Toronto, Canada.
-
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 (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_69e0c47829648190bbe2d1d7033768ec |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0bd59bcbc819093829feb152e090d |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 16, 2026, 6:56 p.m.