Triple
T14302022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold H. Burton |
E354588
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Robert Burton
Robert Burton is the son of Harold H. Burton, an American politician and U.S. Supreme Court Justice.
|
E1091527
|
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: Robert Burton | Statement: [Harold H. Burton, child, Robert Burton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Burton Context triple: [Harold H. Burton, child, Robert Burton]
-
A.
Robert Burton
Robert Burton was an American character actor active in mid-20th-century film and television, often appearing in supporting roles.
-
B.
John Camden Hotten
John Camden Hotten was a 19th-century English publisher, bookseller, and author known for his influential role in Victorian literary culture and for founding the firm that became Chatto & Windus.
-
C.
Henry Fowler
Henry Fowler was a prominent British railway engineer best known for designing influential steam locomotives in the early 20th century.
-
D.
John Warburton
John Warburton was a British-born actor known for his supporting roles in Hollywood films during the 1930s and 1940s.
-
E.
John Warburton
John Warburton was an 18th-century English antiquary and collector known for preserving and cataloguing rare manuscripts and historical documents.
- 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: Robert Burton Triple: [Harold H. Burton, child, Robert Burton]
Generated description
Robert Burton is the son of Harold H. Burton, an American politician and U.S. Supreme Court Justice.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Robert Burton Target entity description: Robert Burton is the son of Harold H. Burton, an American politician and U.S. Supreme Court Justice.
-
A.
Robert Burton
Robert Burton was an American character actor active in mid-20th-century film and television, often appearing in supporting roles.
-
B.
John Camden Hotten
John Camden Hotten was a 19th-century English publisher, bookseller, and author known for his influential role in Victorian literary culture and for founding the firm that became Chatto & Windus.
-
C.
Henry Fowler
Henry Fowler was a prominent British railway engineer best known for designing influential steam locomotives in the early 20th century.
-
D.
John Warburton
John Warburton was a British-born actor known for his supporting roles in Hollywood films during the 1930s and 1940s.
-
E.
John Warburton
John Warburton was an 18th-century English antiquary and collector known for preserving and cataloguing rare manuscripts and historical documents.
- 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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de717fc2348190bb6ba3109bd2871f |
completed | April 14, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d2883e081909c53170ef30b4125 |
completed | May 8, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69fd3dc46b408190b7c515156f81f1fc |
completed | May 8, 2026, 1:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3eb045488190b44812eb638807ff |
completed | May 8, 2026, 1:38 a.m. |
Created at: April 10, 2026, 1:12 a.m.