Triple
T1560225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Shore |
E33301
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Rockland |
E188157
|
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: Rockland | Statement: [South Shore, hasTown, Rockland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rockland Context triple: [South Shore, hasTown, Rockland]
-
A.
Rockland
chosen
Rockland is a small suburban town in Plymouth County, Massachusetts, known for its residential character and proximity to Boston.
-
B.
Cape Ann
Cape Ann is a rocky peninsula on the northeastern coast of Massachusetts known for its historic fishing communities, maritime heritage, and scenic New England coastline.
-
C.
Levant, Maine
Levant, Maine is a small rural town located in Penobscot County in central Maine, known for its agricultural character and close proximity to the city of Bangor.
-
D.
Yarmouth
Yarmouth is a coastal town on Cape Cod in Massachusetts known for its beaches, historic villages, and tourism.
-
E.
Yarmouth
Yarmouth is a small historic port town and popular tourist destination on the western side of the Isle of Wight in England.
- 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_69a885ef9cf48190b0af0f5ce3d02231 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90885d6208190b4b7d6ad336d4d16 |
completed | March 5, 2026, 4:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad719b6c988190a525539d1a29d8d4 |
completed | March 8, 2026, 12:54 p.m. |
Created at: March 4, 2026, 7:27 p.m.