Triple
T16779127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Truman Burbank |
E407810
|
entity |
| Predicate | loveInterest |
P7325
|
FINISHED |
| Object |
Lauren Garland
Lauren Garland is a key character in the film "The Truman Show," portrayed as Truman Burbank's true love who tries to reveal to him the artificial nature of his televised world.
|
E1240795
|
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: Lauren Garland | Statement: [Truman Burbank, loveInterest, Lauren Garland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Garland Context triple: [Truman Burbank, loveInterest, Lauren Garland]
-
A.
Rebecca Garland
Rebecca Garland is one of the children of Merrick Garland, the U.S. Attorney General and former federal judge.
-
B.
Lauren Dunn
Lauren Dunn is a music video director known for her creative visual work on contemporary artists’ projects.
-
C.
Lauren Booth
Lauren Booth is a British journalist, broadcaster, and activist known for her work in media and her high-profile conversion to Islam.
-
D.
Alison Garland
Alison Garland is a British actress best known for her role in Mike Leigh’s film "All or Nothing."
-
E.
Lauren Parkinson
Lauren Parkinson is an American actress best known for her role in the fantasy action film "Avengers Grimm."
- 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: Lauren Garland Triple: [Truman Burbank, loveInterest, Lauren Garland]
Generated description
Lauren Garland is a key character in the film "The Truman Show," portrayed as Truman Burbank's true love who tries to reveal to him the artificial nature of his televised world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lauren Garland Target entity description: Lauren Garland is a key character in the film "The Truman Show," portrayed as Truman Burbank's true love who tries to reveal to him the artificial nature of his televised world.
-
A.
Rebecca Garland
Rebecca Garland is one of the children of Merrick Garland, the U.S. Attorney General and former federal judge.
-
B.
Lauren Dunn
Lauren Dunn is a music video director known for her creative visual work on contemporary artists’ projects.
-
C.
Lauren Booth
Lauren Booth is a British journalist, broadcaster, and activist known for her work in media and her high-profile conversion to Islam.
-
D.
Alison Garland
Alison Garland is a British actress best known for her role in Mike Leigh’s film "All or Nothing."
-
E.
Lauren Parkinson
Lauren Parkinson is an American actress best known for her role in the fantasy action film "Avengers Grimm."
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b21401b881909bbbc7382e851a90 |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfc0dcd081909f715e0f2aad67c7 |
completed | May 10, 2026, 6:34 p.m. |
| NEDg | Description generation | batch_6a00d0780bd881909a9c820625cda9c3 |
completed | May 10, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d1366f4c8190b5627f67402d96a0 |
completed | May 10, 2026, 6:40 p.m. |
Created at: April 10, 2026, 5:22 a.m.