Triple
T2091938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amanda Seyfried |
E32689
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Seyfried
Seyfried is the surname of American actress and singer Amanda Seyfried, known for roles in films like "Mean Girls," "Mamma Mia!" and "Les Misérables."
|
E230774
|
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: Seyfried | Statement: [Amanda Seyfried, familyName, Seyfried]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seyfried Context triple: [Amanda Seyfried, familyName, Seyfried]
-
A.
Scheider
Scheider is the surname of American actor Roy Scheider, best known for his role as Chief Brody in the film "Jaws."
-
B.
Sarandos
Sarandos is the surname of Ted Sarandos, the longtime chief content officer and co-CEO of Netflix known for shaping the company’s original programming strategy.
-
C.
William Lundigan
William Lundigan was an American film and television actor active from the 1930s through the 1960s, known for roles in dramas, war films, and early TV series.
-
D.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
E.
Kees Cook
Kees Cook is a prominent open-source and Linux kernel security developer known for his extensive work on hardening the Linux kernel and improving software security practices.
- 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: Seyfried Triple: [Amanda Seyfried, familyName, Seyfried]
Generated description
Seyfried is the surname of American actress and singer Amanda Seyfried, known for roles in films like "Mean Girls," "Mamma Mia!" and "Les Misérables."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seyfried Target entity description: Seyfried is the surname of American actress and singer Amanda Seyfried, known for roles in films like "Mean Girls," "Mamma Mia!" and "Les Misérables."
-
A.
Scheider
Scheider is the surname of American actor Roy Scheider, best known for his role as Chief Brody in the film "Jaws."
-
B.
Sarandos
Sarandos is the surname of Ted Sarandos, the longtime chief content officer and co-CEO of Netflix known for shaping the company’s original programming strategy.
-
C.
William Lundigan
William Lundigan was an American film and television actor active from the 1930s through the 1960s, known for roles in dramas, war films, and early TV series.
-
D.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
E.
Kees Cook
Kees Cook is a prominent open-source and Linux kernel security developer known for his extensive work on hardening the Linux kernel and improving software security practices.
- 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba7626d081908c9c0f18942e128d |
completed | March 7, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2746fb3481909e0b7fdbd4748245 |
completed | March 9, 2026, 1:49 a.m. |
| NEDg | Description generation | batch_69ae2868815881908d6163ab84060ec2 |
completed | March 9, 2026, 1:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae28ce87c8819097e0b5dab045d9a1 |
completed | March 9, 2026, 1:56 a.m. |
Created at: March 4, 2026, 7:43 p.m.