Triple
T17746273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karen Page |
E442998
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Wallace Wood
Wallace Wood was an influential American comic book artist and writer, best known for his work with EC Comics, Mad magazine, and early Marvel titles.
|
E1284720
|
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: Wallace Wood | Statement: [Karen Page, creator, Wallace Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wallace Wood Context triple: [Karen Page, creator, Wallace Wood]
-
A.
Wallace Woods
Wallace Woods is a historic residential neighborhood in Covington, Kentucky, known for its early 20th-century architecture and tree-lined streets.
-
B.
Wallace Akers
Wallace Akers was a British industrial chemist and administrator who played a key role in the United Kingdom’s early atomic energy and nuclear weapons research during World War II.
-
C.
Thomas Wall
Thomas Wall is a personal name shared by multiple individuals, including historical figures, professionals, and public personalities across various fields.
-
D.
Wally Dalton
Wally Dalton is an actor known for his role in the independent drama film "Wendy and Lucy."
-
E.
Wally Cassell
Wally Cassell was an Italian-born American character actor known for his supporting roles in numerous Hollywood films of the 1940s and 1950s.
- 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: Wallace Wood Triple: [Karen Page, creator, Wallace Wood]
Generated description
Wallace Wood was an influential American comic book artist and writer, best known for his work with EC Comics, Mad magazine, and early Marvel titles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wallace Wood Target entity description: Wallace Wood was an influential American comic book artist and writer, best known for his work with EC Comics, Mad magazine, and early Marvel titles.
-
A.
Wallace Woods
Wallace Woods is a historic residential neighborhood in Covington, Kentucky, known for its early 20th-century architecture and tree-lined streets.
-
B.
Wallace Akers
Wallace Akers was a British industrial chemist and administrator who played a key role in the United Kingdom’s early atomic energy and nuclear weapons research during World War II.
-
C.
Thomas Wall
Thomas Wall is a personal name shared by multiple individuals, including historical figures, professionals, and public personalities across various fields.
-
D.
Wally Dalton
Wally Dalton is an actor known for his role in the independent drama film "Wendy and Lucy."
-
E.
Wally Cassell
Wally Cassell was an Italian-born American character actor known for his supporting roles in numerous Hollywood films of the 1940s and 1950s.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ad26940819090696c2cb86fd606 |
completed | April 19, 2026, 6:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a024300b5048190ba50198a7637441d |
completed | May 11, 2026, 8:58 p.m. |
| NEDg | Description generation | batch_6a02449a76d8819099e72cd53f908255 |
completed | May 11, 2026, 9:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a024530b5dc8190a4776863cc01babf |
completed | May 11, 2026, 9:08 p.m. |
Created at: April 10, 2026, 10:09 a.m.