Triple
T1285230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tommy Mottola |
E27418
|
entity |
| Predicate | parent |
P120
|
FINISHED |
| Object |
Matthew Mottola
Matthew Mottola is the son of music executive Tommy Mottola.
|
E227401
|
NE FINISHED |
How this triple was built (4 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: Matthew Mottola | Statement: [Tommy Mottola, parent, Matthew Mottola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Mottola Context triple: [Tommy Mottola, parent, Matthew Mottola]
-
A.
Andrew Miano
Andrew Miano is an American film producer known for his work on independent and critically acclaimed movies, often collaborating with director Tom Ford and others.
-
B.
Matt Chesse
Matt Chesse is an American film editor known for his work on numerous feature films, including the thriller "Money Monster."
-
C.
Michael Cerenzie
Michael Cerenzie is a film producer known for his work on independent and genre films in Hollywood.
-
D.
Michael De Luca
Michael De Luca is an American film producer and studio executive known for overseeing and producing a wide range of major Hollywood films across genres.
-
E.
Paul Merolla
Paul Merolla is a neuroscientist and engineer best known as a co-founder of Neuralink, the neurotechnology company developing brain–computer interfaces.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Matthew Mottola Triple: [Tommy Mottola, parent, Matthew Mottola]
Generated description
Matthew Mottola is the son of music executive Tommy Mottola.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Mottola Target entity description: Matthew Mottola is the son of music executive Tommy Mottola.
-
A.
Andrew Miano
Andrew Miano is an American film producer known for his work on independent and critically acclaimed movies, often collaborating with director Tom Ford and others.
-
B.
Matt Chesse
Matt Chesse is an American film editor known for his work on numerous feature films, including the thriller "Money Monster."
-
C.
Michael Cerenzie
Michael Cerenzie is a film producer known for his work on independent and genre films in Hollywood.
-
D.
Michael De Luca
Michael De Luca is an American film producer and studio executive known for overseeing and producing a wide range of major Hollywood films across genres.
-
E.
Paul Merolla
Paul Merolla is a neuroscientist and engineer best known as a co-founder of Neuralink, the neurotechnology company developing brain–computer interfaces.
- F. None of above. chosen
Provenance (5 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_69a496d4ec448190ad653b2590c46711 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0b6dda48190a2e79084adea6ec1 |
completed | March 1, 2026, 10:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fae5aa08190b6aa50b543a175b8 |
completed | March 9, 2026, 1:17 a.m. |
| NEDg | Description generation | batch_69ae204fe6148190915219beb27128bc |
completed | March 9, 2026, 1:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae20d09c748190aebbfb88f0eedbaa |
completed | March 9, 2026, 1:22 a.m. |
Created at: March 1, 2026, 7:51 p.m.