Triple
T4709938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Into the Blue |
E104483
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Matt Luber
Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
|
E525612
|
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 Luber | Statement: [Into the Blue, producer, Matt Luber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Luber Context triple: [Into the Blue, producer, Matt Luber]
-
A.
Matt Graver
Matt Graver is a seasoned and morally ambiguous CIA operative who orchestrates covert operations against Mexican drug cartels in the film "Sicario."
-
B.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
C.
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.
-
D.
Brant Daugherty
Brant Daugherty is an American actor known for his roles in television series like "Pretty Little Liars" and films including the "Fifty Shades" franchise.
-
E.
Chris Stolte
Chris Stolte is a computer scientist and entrepreneur best known as a co-founder and former chief development officer of the data visualization company Tableau Software.
- 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 Luber Triple: [Into the Blue, producer, Matt Luber]
Generated description
Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matt Luber Target entity description: Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
-
A.
Matt Graver
Matt Graver is a seasoned and morally ambiguous CIA operative who orchestrates covert operations against Mexican drug cartels in the film "Sicario."
-
B.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
C.
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.
-
D.
Brant Daugherty
Brant Daugherty is an American actor known for his roles in television series like "Pretty Little Liars" and films including the "Fifty Shades" franchise.
-
E.
Chris Stolte
Chris Stolte is a computer scientist and entrepreneur best known as a co-founder and former chief development officer of the data visualization company Tableau Software.
- 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_69bd43eac3c08190af7e4020c6c3704c |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd63ee712c81908da60aa0df58efe0 |
completed | March 20, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf953024f88190abca4affb92eca13 |
completed | March 22, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69bf959d89f481908805f8c0cd5f18b3 |
completed | March 22, 2026, 7:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf95ef77b48190907c836fbc524f19 |
completed | March 22, 2026, 7:10 a.m. |
Created at: March 20, 2026, 1:17 p.m.