Triple
T4521848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trolls (film) |
E103286
|
entity |
| Predicate | voiceActor |
P1507
|
FINISHED |
| Object |
Ron Funches
Ron Funches is an American stand-up comedian and actor known for his warm, laid-back delivery and distinctive voice work in animated films and television.
|
E483964
|
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: Ron Funches | Statement: [Trolls (film), voiceActor, Ron Funches]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ron Funches Context triple: [Trolls (film), voiceActor, Ron Funches]
-
A.
Roy Furman
Roy Furman is an American theater producer and financier known for backing numerous successful Broadway productions.
-
B.
Frank Fisk
Frank Fisk is an individual notable enough to be recognized as a bearer of the surname Fisk.
-
C.
Kevin Duckworth
Kevin Duckworth was an American NBA center best known for his All-Star years with the Portland Trail Blazers in the late 1980s and early 1990s.
-
D.
Dan Knechtges
Dan Knechtges is an American director and choreographer known for his work on numerous Broadway and Off-Broadway musicals.
-
E.
Joe Noland
Joe Noland is a fictional character from the television series "The District," which follows the professional and personal lives of law enforcement officials in Washington, D.C.
- 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: Ron Funches Triple: [Trolls (film), voiceActor, Ron Funches]
Generated description
Ron Funches is an American stand-up comedian and actor known for his warm, laid-back delivery and distinctive voice work in animated films and television.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ron Funches Target entity description: Ron Funches is an American stand-up comedian and actor known for his warm, laid-back delivery and distinctive voice work in animated films and television.
-
A.
Roy Furman
Roy Furman is an American theater producer and financier known for backing numerous successful Broadway productions.
-
B.
Frank Fisk
Frank Fisk is an individual notable enough to be recognized as a bearer of the surname Fisk.
-
C.
Kevin Duckworth
Kevin Duckworth was an American NBA center best known for his All-Star years with the Portland Trail Blazers in the late 1980s and early 1990s.
-
D.
Dan Knechtges
Dan Knechtges is an American director and choreographer known for his work on numerous Broadway and Off-Broadway musicals.
-
E.
Joe Noland
Joe Noland is a fictional character from the television series "The District," which follows the professional and personal lives of law enforcement officials in Washington, D.C.
- 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_69bd43dba59881908cf59b31df8c7ae1 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd574bd6908190b939d92b5809b101 |
completed | March 20, 2026, 2:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be89c2485881908797f4a3560a0b04 |
completed | March 21, 2026, 12:06 p.m. |
| NEDg | Description generation | batch_69be8aaa3eac8190876fc8c892cb7c3f |
completed | March 21, 2026, 12:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be8b164c8c8190b03e7e4892b6aa9e |
completed | March 21, 2026, 12:12 p.m. |
Created at: March 20, 2026, 1:02 p.m.