Triple
T5577871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Mottola |
E146365
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Mottola |
E146365
|
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: Mottola | Statement: [Tony Mottola, familyName, Mottola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mottola Context triple: [Tony Mottola, familyName, Mottola]
-
A.
Mottola
chosen
Mottola is an Italian surname most prominently associated with American music executive Tommy Mottola.
-
B.
Mott
Mott is a surname most notably associated with Sir Nevill Mott, the Nobel Prize–winning British physicist recognized for his work on the electronic structure of magnetic and disordered systems.
-
C.
Vacone
Vacone is a small historic hilltop village in the Lazio region of central Italy, known for its scenic countryside and traditional rural character.
-
D.
Cipollone
Cipollone is an Italian surname most prominently associated with Pat Cipollone, a lawyer who served as White House Counsel under U.S. President Donald Trump.
-
E.
Alvito
Alvito is a small Portuguese municipality in the Alentejo region, known for its historic castle and traditional rural landscape.
- 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_69c008ffed108190a084602227af6157 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0206ae4808190971d89243db94475 |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c059f3f6648190af2fd9a5e7cc125b |
completed | March 22, 2026, 9:07 p.m. |
Created at: March 22, 2026, 3:37 p.m.