Triple
T20035395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Incognegro |
E497243
|
entity |
| Predicate | illustrator |
P9707
|
FINISHED |
| Object |
Warren Pleece
Warren Pleece is a British comic book artist and illustrator known for his distinctive work on graphic novels and series such as Incognegro.
|
E1411228
|
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: Warren Pleece | Statement: [Incognegro, illustrator, Warren Pleece]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warren Pleece Context triple: [Incognegro, illustrator, Warren Pleece]
-
A.
Ormond Beatty
Ormond Beatty was a 19th-century American educator and academic administrator best known for serving as president of Centre College in Kentucky.
-
B.
Peter Cummings
Peter Cummings was an architect known for designing notable British entertainment venues, including the Manchester Apollo theatre.
-
C.
Hugh Shearer
Hugh Shearer was a Jamaican politician, trade unionist, and the third Prime Minister of Jamaica, serving from 1967 to 1972.
-
D.
George W. Pepper
George W. Pepper was an American lawyer, legal scholar, and Republican U.S. Senator from Pennsylvania in the early 20th century.
-
E.
Richard Marden
Richard Marden was a British film editor known for his work on notable mid-20th-century films, including adaptations of classic literature.
- 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: Warren Pleece Triple: [Incognegro, illustrator, Warren Pleece]
Generated description
Warren Pleece is a British comic book artist and illustrator known for his distinctive work on graphic novels and series such as Incognegro.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Warren Pleece Target entity description: Warren Pleece is a British comic book artist and illustrator known for his distinctive work on graphic novels and series such as Incognegro.
-
A.
Ormond Beatty
Ormond Beatty was a 19th-century American educator and academic administrator best known for serving as president of Centre College in Kentucky.
-
B.
Peter Cummings
Peter Cummings was an architect known for designing notable British entertainment venues, including the Manchester Apollo theatre.
-
C.
Hugh Shearer
Hugh Shearer was a Jamaican politician, trade unionist, and the third Prime Minister of Jamaica, serving from 1967 to 1972.
-
D.
George W. Pepper
George W. Pepper was an American lawyer, legal scholar, and Republican U.S. Senator from Pennsylvania in the early 20th century.
-
E.
Richard Marden
Richard Marden was a British film editor known for his work on notable mid-20th-century films, including adaptations of classic literature.
- 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e76f8481909c006921cbbfd060 |
completed | April 20, 2026, 5:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0826fa8de8819093a1fcd445b3778a |
completed | May 16, 2026, 8:12 a.m. |
| NEDg | Description generation | batch_6a0827f11afc8190bf47bdc7feb99ed0 |
completed | May 16, 2026, 8:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0828692cac81908d9dc445a17751be |
completed | May 16, 2026, 8:18 a.m. |
Created at: April 11, 2026, 3:36 p.m.