Triple
T21994200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tell Me a Story |
E543161
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Matt Lauria
Matt Lauria is an American actor known for his roles in television dramas such as "Friday Night Lights," "Kingdom," and "Tell Me a Story."
|
E1515374
|
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: Matt Lauria | Statement: [Tell Me a Story, stars, Matt Lauria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Lauria Context triple: [Tell Me a Story, stars, Matt Lauria]
-
A.
Matt Lattanzi
Matt Lattanzi is an American actor and former dancer best known for his roles in 1980s films and for his marriage to singer and actress Olivia Newton-John.
-
B.
Matt Luber
Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
-
C.
Matt Lutsky
Matt Lutsky is a television writer and producer best known for co-creating the dark comedy series "On Becoming a God in Central Florida."
-
D.
Greg Latta
Greg Latta was an American professional football tight end who played in the World Football League and the NFL, most notably for the Chicago Bears.
-
E.
Tim Mattia
Tim Mattia is a British director known for creating high-profile music videos and visual content for major contemporary artists.
- 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: Matt Lauria Triple: [Tell Me a Story, stars, Matt Lauria]
Generated description
Matt Lauria is an American actor known for his roles in television dramas such as "Friday Night Lights," "Kingdom," and "Tell Me a Story."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matt Lauria Target entity description: Matt Lauria is an American actor known for his roles in television dramas such as "Friday Night Lights," "Kingdom," and "Tell Me a Story."
-
A.
Matt Lattanzi
Matt Lattanzi is an American actor and former dancer best known for his roles in 1980s films and for his marriage to singer and actress Olivia Newton-John.
-
B.
Matt Luber
Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
-
C.
Matt Lutsky
Matt Lutsky is a television writer and producer best known for co-creating the dark comedy series "On Becoming a God in Central Florida."
-
D.
Greg Latta
Greg Latta was an American professional football tight end who played in the World Football League and the NFL, most notably for the Chicago Bears.
-
E.
Tim Mattia
Tim Mattia is a British director known for creating high-profile music videos and visual content for major contemporary artists.
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127639bf48190800b3fa3c1527983 |
completed | April 28, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a7b5e56588190bd9be43375ecb86a |
completed | May 18, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_6a0a7c73537481908c644642f825569f |
completed | May 18, 2026, 2:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a7d29f5a08190b41cad11e2192727 |
completed | May 18, 2026, 2:44 a.m. |
Created at: April 16, 2026, 8:17 p.m.