Triple
T21121696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon Boccanegra |
E520446
|
entity |
| Predicate | settingLocation |
P40
|
FINISHED |
| Object | Genoa |
—
|
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: Genoa | Statement: [Simon Boccanegra, settingLocation, Genoa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Genoa Context triple: [Simon Boccanegra, settingLocation, Genoa]
-
A.
Genoa
chosen
Genoa is a historic port city in northwestern Italy known for its significant maritime heritage, trade, and role as a major economic hub on the Ligurian coast.
-
B.
Genoa
Genoa is the codename for AMD’s fourth-generation EPYC server processors based on the Zen 4 architecture and the SP5 platform.
-
C.
Génova
Génova is a small municipality in Colombia’s Quindío Department, known for its coffee-growing traditions and Andean rural landscapes.
-
D.
Port of Genoa
The Port of Genoa is one of Italy’s largest and busiest seaports, serving as a major hub for commercial shipping, passenger ferries, and maritime trade in the Mediterranean.
-
E.
Livorno
Livorno is a port city on Italy’s western coast, historically notable for its diverse communities and significant Jewish population.
- 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e722348fe08190a1f079b7b0b5dd14 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 2:55 p.m.