Triple
T19823233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniele Barbaro |
E476250
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Daniele |
—
|
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: Daniele | Statement: [Daniele Barbaro, givenName, Daniele]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniele Context triple: [Daniele Barbaro, givenName, Daniele]
-
A.
Daniele
chosen
Daniele is an Italian given name commonly used for males, equivalent to the English name Daniel.
-
B.
Stefano
Stefano is the Italian given name corresponding to the Hungarian name István, commonly used as a male first name in Italy.
-
C.
Danilo
Danilo is a masculine given name used in various Slavic and Romance languages, generally equivalent to Daniel and meaning "God is my judge."
-
D.
Paolo
Paolo is the Italian form of the given name Paul, commonly used in Italy and other Italian-speaking communities.
-
E.
Pietro
Pietro is the Italian given name equivalent to "Peter," commonly used in Italy and among Italian-speaking communities.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6550070c4819099e1f057b9a8849e |
completed | April 20, 2026, 4:32 p.m. |
Created at: April 10, 2026, 1:50 p.m.