Triple
T12262119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EC dollar |
E292248
|
entity |
| Predicate | legalTenderIn |
P188
|
FINISHED |
| Object | Montserrat |
E17737
|
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: Montserrat | Statement: [EC dollar, legalTenderIn, Montserrat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montserrat Context triple: [EC dollar, legalTenderIn, Montserrat]
-
A.
Montserrat
chosen
Montserrat is a small Caribbean island and British Overseas Territory known for its volcanic activity and lush, mountainous landscape.
-
B.
Montserrat massif
Montserrat massif is a distinctive multi-peaked mountain range in Catalonia, Spain, famed for its unique rock formations and the Montserrat Monastery.
-
C.
Monte Grande
Monte Grande is a suburban city in the Buenos Aires metropolitan area of Argentina, known as the administrative seat of the Esteban Echeverría Partido.
-
D.
Monte
Monte was the nickname of Monte Irvin, a Hall of Fame American baseball player renowned as one of the early Black stars to break Major League Baseball’s color barrier.
-
E.
Monte
Monte is the costumed grizzly bear mascot who represents the University of Montana at athletic events and campus activities.
- 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cdb0a948190aeee4ca3c01f801e |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e65bc00819091e4fee3c3af6f4f |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.