Triple

T13109362
Position Surface form Disambiguated ID Type / Status
Subject Brickleberry E310930 entity
Predicate creator P184 FINISHED
Object Waco O'Guin
Waco O'Guin is an American comedian, animator, and television producer best known for co-creating the adult animated series Brickleberry.
 E1021786 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: Waco O'Guin | Statement: [Brickleberry, creator, Waco O'Guin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Waco O'Guin
Context triple: [Brickleberry, creator, Waco O'Guin]
  • A. Justin Chambers
    Justin Chambers is an American actor best known for his long-running role as Dr. Alex Karev on the television medical drama "Grey’s Anatomy."
  • B. Orson Cox
    Orson Cox is the son of Scottish actor and physicist-turned-science-communicator Brian Cox.
  • C. Bryce Wood
    Bryce Wood was a prominent historian and scholar of inter-American relations whose work significantly advanced the study of Latin American politics and U.S.–Latin American diplomacy.
  • D. Shane Hurlbut
    Shane Hurlbut is an American cinematographer known for his work on major Hollywood films and his influential role in advancing digital cinematography techniques.
  • E. Logan Marshall-Green
    Logan Marshall-Green is an American actor and director known for his roles in films like "Prometheus" and "Upgrade" as well as various television series.
  • 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: Waco O'Guin
Triple: [Brickleberry, creator, Waco O'Guin]
Generated description
Waco O'Guin is an American comedian, animator, and television producer best known for co-creating the adult animated series Brickleberry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Waco O'Guin
Target entity description: Waco O'Guin is an American comedian, animator, and television producer best known for co-creating the adult animated series Brickleberry.
  • A. Justin Chambers
    Justin Chambers is an American actor best known for his long-running role as Dr. Alex Karev on the television medical drama "Grey’s Anatomy."
  • B. Orson Cox
    Orson Cox is the son of Scottish actor and physicist-turned-science-communicator Brian Cox.
  • C. Bryce Wood
    Bryce Wood was a prominent historian and scholar of inter-American relations whose work significantly advanced the study of Latin American politics and U.S.–Latin American diplomacy.
  • D. Shane Hurlbut
    Shane Hurlbut is an American cinematographer known for his work on major Hollywood films and his influential role in advancing digital cinematography techniques.
  • E. Logan Marshall-Green
    Logan Marshall-Green is an American actor and director known for his roles in films like "Prometheus" and "Upgrade" as well as various television series.
  • 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_69d806a872d08190a329806f8ff30df4 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9817ce07881909ec552bf861ac175 completed April 10, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e27bd8fc8190a81130b7cb3a8bd8 completed May 3, 2026, 5:51 a.m.
NEDg Description generation batch_69f6e37023d48190ba6a0b790ed23370 completed May 3, 2026, 5:56 a.m.
NED2 Entity disambiguation (via description) batch_69f6e40a13c8819084daf9b77b46a181 completed May 3, 2026, 5:58 a.m.
Created at: April 9, 2026, 9:05 p.m.