Triple
T21828588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paolo Frisi |
E538923
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Pisa |
—
|
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: Pisa | Statement: [Paolo Frisi, workLocation, Pisa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pisa Context triple: [Paolo Frisi, workLocation, Pisa]
-
A.
Pisa
Pisa is an ancient city in the Peloponnese region of Greece, historically significant as a center near Olympia and associated with early Greek myth and athletics.
-
B.
Pisa
chosen
Pisa is a historic Italian city in Tuscany best known for its iconic Leaning Tower and as a significant center of medieval trade, learning, and architecture.
-
C.
Florence
Florence is a city in northwestern Alabama known as part of the Muscle Shoals metropolitan area and for its rich musical and cultural heritage.
-
D.
Florence
Florence is a neighborhood in South Los Angeles known for its dense urban character, diverse working-class community, and proximity to major transportation corridors.
-
E.
Florence
Florence is a small town in Fremont County, Colorado, known for its historic downtown and role as a gateway to nearby outdoor attractions.
- 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_69e0c475cda88190987d08f23caebdc1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f091344c848190b1675432a8c255f2 |
completed | April 28, 2026, 10:51 a.m. |
Created at: April 16, 2026, 6:54 p.m.