Triple
T13448682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 45 Years |
E320549
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Sam Alexander
Sam Alexander is an actor known for his role in the film "45 Years."
|
E1040262
|
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: Sam Alexander | Statement: [45 Years, castMember, Sam Alexander]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Alexander Context triple: [45 Years, castMember, Sam Alexander]
-
A.
Miguel O'Hara
Miguel O'Hara is a futuristic version of Spider-Man from the year 2099, known for his advanced technology, genetic enhancements, and role as Spider-Man 2099 in Marvel Comics.
-
B.
Sam Harper
Sam Harper is a screenwriter best known for writing family-friendly comedy films such as "Cheaper by the Dozen" and "Freaky Friday."
-
C.
Morgan Grimes
Morgan Grimes is a comedic, loyal best friend and later spy ally in the TV series "Chuck," known for his nerdy charm and personal growth from slacker to capable operative.
-
D.
Owen Harper
Owen Harper is a central character in the British sci-fi series "Torchwood," serving as the team's acerbic and brilliant medical officer.
-
E.
Alexis Colby
Alexis Colby is a glamorous, scheming socialite and businesswoman who became one of television's most iconic villains on the 1980s prime-time soap opera "Dynasty."
- 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: Sam Alexander Triple: [45 Years, castMember, Sam Alexander]
Generated description
Sam Alexander is an actor known for his role in the film "45 Years."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sam Alexander Target entity description: Sam Alexander is an actor known for his role in the film "45 Years."
-
A.
Miguel O'Hara
Miguel O'Hara is a futuristic version of Spider-Man from the year 2099, known for his advanced technology, genetic enhancements, and role as Spider-Man 2099 in Marvel Comics.
-
B.
Sam Harper
Sam Harper is a screenwriter best known for writing family-friendly comedy films such as "Cheaper by the Dozen" and "Freaky Friday."
-
C.
Morgan Grimes
Morgan Grimes is a comedic, loyal best friend and later spy ally in the TV series "Chuck," known for his nerdy charm and personal growth from slacker to capable operative.
-
D.
Owen Harper
Owen Harper is a central character in the British sci-fi series "Torchwood," serving as the team's acerbic and brilliant medical officer.
-
E.
Alexis Colby
Alexis Colby is a glamorous, scheming socialite and businesswoman who became one of television's most iconic villains on the 1980s prime-time soap opera "Dynasty."
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaef758b08190b9aa5ec7082cd417 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73999f8388190b2c578e063341178 |
completed | May 3, 2026, 12:03 p.m. |
| NEDg | Description generation | batch_69f73af2b37481908c4d282c1335fe08 |
completed | May 3, 2026, 12:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f73b959de88190959335353242031b |
completed | May 3, 2026, 12:12 p.m. |
Created at: April 9, 2026, 9:41 p.m.