Triple
T49986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston |
E981
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Beantown |
E99458
|
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: Beantown | Statement: [Boston, hasNickname, Beantown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beantown Context triple: [Boston, hasNickname, Beantown]
-
A.
Beantown
chosen
Beantown is a popular nickname for the city of Boston, Massachusetts, often used in informal and cultural references to the city.
-
B.
North End, Boston
North End, Boston is the city's oldest residential neighborhood, famed for its historic sites like the Paul Revere House and its vibrant Italian-American culture, restaurants, and festivals.
-
C.
Boston Common
Boston Common is a historic central public park in downtown Boston and the oldest city park in the United States.
-
D.
Revere, Massachusetts
Revere, Massachusetts is a coastal city just north of Boston known for Revere Beach, the oldest public beach in the United States.
-
E.
Downtown Boston
Downtown Boston is the city’s historic and commercial core, known for its dense cluster of landmarks, offices, and shopping areas.
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24af56cc88190a898f8bf2a283820 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b82d2d5c8190b5cd8779fcbc8e81 |
completed | March 4, 2026, 4:42 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.