Triple
T5238366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura Moretti |
E118278
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Moretti |
E118278
|
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: Moretti | Statement: [Laura Moretti, familyName, Moretti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moretti Context triple: [Laura Moretti, familyName, Moretti]
-
A.
Moretti
chosen
Moretti is an Italian surname borne by numerous notable figures in fields such as film, literature, and the arts.
-
B.
Marchetti
Marchetti is an Italian surname borne by various notable figures in sports, arts, and public life.
-
C.
Magreglio
Magreglio is a small municipality in the Province of Como in Italy’s Lombardy region, known for its scenic location near Lake Como and the famous Madonna del Ghisallo cycling sanctuary.
-
D.
Fuselli
Fuselli is a central fictional character in the World War I novel "Active Service" by Stephen Crane, representing the experiences and attitudes of an ordinary soldier.
-
E.
Bruschi
Bruschi is the surname of Tedy Bruschi, a former NFL linebacker best known for his career with the New England Patriots.
- 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_69bd4467db0881909b3b0982df32cc8f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b290b88819095bc99c234260d25 |
completed | March 20, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf29008b38819084e59078210626b2 |
completed | March 21, 2026, 11:25 p.m. |
Created at: March 20, 2026, 1:49 p.m.