Triple
T9112180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benedetto |
E218627
|
entity |
| Predicate | hasDiminutiveOrNickname |
P456
|
FINISHED |
| Object | Beppe |
E348036
|
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: Beppe | Statement: [Benedetto, hasDiminutiveOrNickname, Beppe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beppe Context triple: [Benedetto, hasDiminutiveOrNickname, Beppe]
-
A.
Beppe
chosen
Beppe is an Italian diminutive given name commonly used as a familiar or affectionate form of Giuseppe.
-
B.
Maurizio
Maurizio is an Italian given name, equivalent to Maurice, commonly used in Italy and among Italian-speaking communities.
-
C.
Giorgio
Giorgio is a given name, primarily the Italian form of George, used as a masculine first name.
-
D.
Gianni
Gianni is an Italian given name commonly used for men, often as a diminutive of Giovanni.
-
E.
Jep Gambardella
Jep Gambardella is a jaded, aging Roman journalist and socialite who reflects on beauty, decadence, and the passage of time in Paolo Sorrentino’s film "The Great Beauty."
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8495c448190b9bb3803fb2dda70 |
completed | April 1, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d03052716c8190835b0d3357a29ce5 |
completed | April 3, 2026, 9:25 p.m. |
Created at: March 30, 2026, 7:16 p.m.