Triple
T23341072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gounod’s Roméo et Juliette |
E591734
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Verona |
—
|
NE NERFINISHED |
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: Verona | Statement: [Gounod’s Roméo et Juliette, setting, Verona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verona Context triple: [Gounod’s Roméo et Juliette, setting, Verona]
-
A.
Verona
chosen
Verona is a historic city in northern Italy renowned for its well-preserved Roman architecture and its association with Shakespeare’s "Romeo and Juliet."
-
B.
Verona
Verona is a small borough in Allegheny County, Pennsylvania, situated along the Allegheny River just northeast of Pittsburgh.
-
C.
Verona
Verona is a small rural town in the Bega Valley region of New South Wales, Australia.
-
D.
Padua
Padua is a historic city in northern Italy renowned as a major cultural and academic center, home to one of Europe’s oldest universities.
-
E.
Brescia
Brescia is a historic industrial and cultural city in northern Italy, known for its Roman and medieval architecture and its role as an economic hub.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f198329d9c8190992627afa9b54bed |
completed | April 29, 2026, 5:33 a.m. |
Created at: April 17, 2026, 5:18 p.m.