Triple
T1359932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA Waverley |
E29074
|
entity |
| Predicate | municipality |
P852
|
FINISHED |
| Object | Belmont |
E13139
|
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: Belmont | Statement: [MBTA Waverley, municipality, Belmont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belmont Context triple: [MBTA Waverley, municipality, Belmont]
-
A.
Belmont
chosen
Belmont is a suburban town in Middlesex County, Massachusetts, known for its residential character and proximity to Boston.
-
B.
Belmont
Belmont is a predominantly residential neighborhood in the central Bronx, New York City, historically known as the borough’s “Little Italy” for its Italian-American community and businesses.
-
C.
Belmont
Belmont is a residential neighborhood in West Philadelphia known for its historic rowhouses and proximity to Fairmount Park.
-
D.
Belmont
Belmont is a city on the San Francisco Peninsula in California, known for its suburban character, hilly terrain, and proximity to major Bay Area tech and transportation hubs.
-
E.
Belmont
Belmont is a suburban district within the London Borough of Harrow, known for its residential character and local shopping and community facilities.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b156b081909c99ada70a969fc0 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad08a7ed5c8190b9f99a6f4524eae8 |
completed | March 8, 2026, 5:27 a.m. |
Created at: March 1, 2026, 7:56 p.m.