Triple
T8510621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marystown |
E201442
|
entity |
| Predicate | formedByAmalgamationWith |
P402
|
FINISHED |
| Object | Beau Bois |
E738481
|
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: Beau Bois | Statement: [Marystown, formedByAmalgamationWith, Beau Bois]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beau Bois Context triple: [Marystown, formedByAmalgamationWith, Beau Bois]
-
A.
Beau Bois
chosen
Beau Bois is a small coastal community near Marystown on the Burin Peninsula of Newfoundland and Labrador, Canada.
-
B.
Arniquet
Arniquet is a commune located in the Sud Department of Haiti.
-
C.
Sambreville
Sambreville is a municipality in the Walloon region of Belgium, situated along the Sambre River and known for its industrial and mining heritage.
-
D.
Confignon
Confignon is a small municipality in the canton of Geneva in southwestern Switzerland, known for its residential character and proximity to the city of Geneva.
-
E.
Lauzerte
Lauzerte is a medieval hilltop village in southern France known for its well-preserved historic center and picturesque views over the surrounding countryside.
- 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_69ca8320e5748190ac2c585a0bba8193 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4578d9c8819096b3853d01c3ec11 |
completed | March 31, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6d2eef208190b505a64d01f656f4 |
completed | April 2, 2026, 1:20 p.m. |
Created at: March 30, 2026, 6:15 p.m.