Triple
T14710234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Twilight (2008 film) |
E345528
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Mark Morgan
Mark Morgan is a film producer best known for his work on the commercially successful Twilight saga adaptations.
|
E1115084
|
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: Mark Morgan | Statement: [Twilight (2008 film), producer, Mark Morgan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Morgan Context triple: [Twilight (2008 film), producer, Mark Morgan]
-
A.
Mike Morgan
Mike Morgan is a benefactor known for sponsoring the Stonewall Book Award, which honors outstanding LGBTQ+ literature.
-
B.
Sean Akins
Sean Akins is a television producer and creative director best known for helping develop and shape Cartoon Network’s influential Toonami programming block.
-
C.
Sam Houser
Sam Houser is a British video game producer and co-founder of Rockstar Games, best known for leading the creation of the Grand Theft Auto series.
-
D.
Mark Bryant
Mark Bryant is a relatively common personal name shared by multiple individuals, including professionals in fields such as sports, politics, and academia.
-
E.
Gene Morgan
Gene Morgan was an American film actor active in the early 20th century, known for supporting roles in Hollywood productions including the 1932 drama "Blonde Venus."
- 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: Mark Morgan Triple: [Twilight (2008 film), producer, Mark Morgan]
Generated description
Mark Morgan is a film producer best known for his work on the commercially successful Twilight saga adaptations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Morgan Target entity description: Mark Morgan is a film producer best known for his work on the commercially successful Twilight saga adaptations.
-
A.
Mike Morgan
Mike Morgan is a benefactor known for sponsoring the Stonewall Book Award, which honors outstanding LGBTQ+ literature.
-
B.
Sean Akins
Sean Akins is a television producer and creative director best known for helping develop and shape Cartoon Network’s influential Toonami programming block.
-
C.
Sam Houser
Sam Houser is a British video game producer and co-founder of Rockstar Games, best known for leading the creation of the Grand Theft Auto series.
-
D.
Mark Bryant
Mark Bryant is a relatively common personal name shared by multiple individuals, including professionals in fields such as sports, politics, and academia.
-
E.
Darin Scott
Darin Scott is an American filmmaker and screenwriter known for his work on crime and thriller films, including co-writing the 2006 action drama "Waist Deep."
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb9814e0c8190984ac30d276499cc |
completed | April 14, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf08d59b48190a1ddd2aed6ed756e |
completed | May 8, 2026, 2:17 p.m. |
| NEDg | Description generation | batch_69fdf2728df881909609d4e6177c7841 |
completed | May 8, 2026, 2:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdf30dab20819085589da4e869fb7e |
completed | May 8, 2026, 2:28 p.m. |
Created at: April 10, 2026, 1:28 a.m.