Triple
T13932889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How High |
E335035
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Chuck Davis
Chuck Davis is an actor known for appearing in the stoner comedy film "How High."
|
E1069292
|
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: Chuck Davis | Statement: [How High, castMember, Chuck Davis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chuck Davis Context triple: [How High, castMember, Chuck Davis]
-
A.
Marc Davis
Marc Davis was a legendary Disney animator and one of Walt Disney’s original “Nine Old Men,” renowned for designing iconic characters and attractions for Disneyland.
-
B.
Gerry Davis
Gerry Davis was a British television writer and script editor best known for his work on Doctor Who, including co-creating the iconic Cybermen.
-
C.
Gerry Davis
Gerry Davis is a longtime Major League Baseball umpire known for working numerous postseason games and serving as a crew chief in multiple World Series.
-
D.
George Davis
George Davis was an actor known for appearing in early 20th-century films, including the silent comedy "The Circus."
-
E.
George Davis
George Davis was a Hall of Fame American baseball shortstop and manager from the late 19th and early 20th centuries, best known for his star play with the New York Giants and Chicago White Sox.
- 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: Chuck Davis Triple: [How High, castMember, Chuck Davis]
Generated description
Chuck Davis is an actor known for appearing in the stoner comedy film "How High."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chuck Davis Target entity description: Chuck Davis is an actor known for appearing in the stoner comedy film "How High."
-
A.
Marc Davis
Marc Davis was a legendary Disney animator and one of Walt Disney’s original “Nine Old Men,” renowned for designing iconic characters and attractions for Disneyland.
-
B.
Gerry Davis
Gerry Davis was a British television writer and script editor best known for his work on Doctor Who, including co-creating the iconic Cybermen.
-
C.
Gerry Davis
Gerry Davis is a longtime Major League Baseball umpire known for working numerous postseason games and serving as a crew chief in multiple World Series.
-
D.
George Davis
George Davis was an actor known for appearing in early 20th-century films, including the silent comedy "The Circus."
-
E.
George Davis
George Davis was a Hall of Fame American baseball shortstop and manager from the late 19th and early 20th centuries, best known for his star play with the New York Giants and Chicago White Sox.
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2cf28df081908d897d7b9ec7939d |
completed | April 14, 2026, 12:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce8452648190b7392d75eb1ca874 |
completed | May 3, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69f7cf37cd7c81908f4da2495403bc6c |
completed | May 3, 2026, 10:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7cfee80a881909de648b20043bf6d |
completed | May 3, 2026, 10:45 p.m. |
Created at: April 9, 2026, 10:17 p.m.