Triple
T12090219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep State |
E287921
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Max Easton
Max Easton is a former MI6 agent drawn back into the world of espionage as the conflicted protagonist of the television thriller series "Deep State."
|
E969031
|
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: Max Easton | Statement: [Deep State, mainCharacter, Max Easton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max Easton Context triple: [Deep State, mainCharacter, Max Easton]
-
A.
Alex Munday
Alex Munday is a stylish, tech-savvy private investigator and martial arts expert from the Charlie’s Angels film series.
-
B.
Spence Olchin
Spence Olchin is a socially awkward, nerdy friend character from the sitcom "The King of Queens," known for his quirky personality and close ties to the main couple.
-
C.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
D.
Andrew Hynes
Andrew Hynes was an American military officer and early Kentucky pioneer known for his role in the region’s frontier development.
-
E.
Jason Weston
Jason Weston is a machine learning researcher known for his contributions to areas such as large-scale learning, natural language processing, and neural networks.
- 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: Max Easton Triple: [Deep State, mainCharacter, Max Easton]
Generated description
Max Easton is a former MI6 agent drawn back into the world of espionage as the conflicted protagonist of the television thriller series "Deep State."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Max Easton Target entity description: Max Easton is a former MI6 agent drawn back into the world of espionage as the conflicted protagonist of the television thriller series "Deep State."
-
A.
Alex Munday
Alex Munday is a stylish, tech-savvy private investigator and martial arts expert from the Charlie’s Angels film series.
-
B.
Spence Olchin
Spence Olchin is a socially awkward, nerdy friend character from the sitcom "The King of Queens," known for his quirky personality and close ties to the main couple.
-
C.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
D.
Andrew Hynes
Andrew Hynes was an American military officer and early Kentucky pioneer known for his role in the region’s frontier development.
-
E.
Jason Weston
Jason Weston is a machine learning researcher known for his contributions to areas such as large-scale learning, natural language processing, and neural networks.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915161f848190a6355c1e372eadaa |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a6d74888190aab150f1ceb2e9f1 |
completed | May 2, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_69f60bda16e48190af8abc0aa8ef41f0 |
completed | May 2, 2026, 2:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60cd1668881908f43d895fcfba0aa |
completed | May 2, 2026, 2:40 p.m. |
Created at: April 8, 2026, 9:48 p.m.