Triple
T12791547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Day |
E305775
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
William Day
William Day is a relatively common personal name shared by multiple individuals across various professions and historical periods.
|
E1005819
|
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: William Day | Statement: [Day, hasNotableBearer, William Day]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: William Day Context triple: [Day, hasNotableBearer, William Day]
-
A.
Robert Day
Robert Day was a British film and television director known for his work on mid-20th-century adventure, horror, and genre films.
-
B.
Stephen Daye
Stephen Daye was a colonial American printer best known for producing the Bay Psalm Book, the first book printed in British North America.
-
C.
Thomas Sampson
Thomas Sampson was a 16th-century English Puritan theologian and churchman known for his role in the early English Reformation and involvement with the Geneva Bible.
-
D.
Thomas Parkhurst
Thomas Parkhurst was a 17th-century London bookseller and publisher known for issuing prominent Puritan and religious works.
-
E.
Edward Willett
Edward Willett is a Canadian science fiction and fantasy author known for novels such as "Marseguro" and for hosting "The Worldshapers" podcast.
- 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: William Day Triple: [Day, hasNotableBearer, William Day]
Generated description
William Day is a relatively common personal name shared by multiple individuals across various professions and historical periods.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: William Day Target entity description: William Day is a relatively common personal name shared by multiple individuals across various professions and historical periods.
-
A.
Robert Day
Robert Day was a British film and television director known for his work on mid-20th-century adventure, horror, and genre films.
-
B.
Stephen Daye
Stephen Daye was a colonial American printer best known for producing the Bay Psalm Book, the first book printed in British North America.
-
C.
Thomas Sampson
Thomas Sampson was a 16th-century English Puritan theologian and churchman known for his role in the early English Reformation and involvement with the Geneva Bible.
-
D.
Thomas Parkhurst
Thomas Parkhurst was a 17th-century London bookseller and publisher known for issuing prominent Puritan and religious works.
-
E.
Edward Willett
Edward Willett is a Canadian science fiction and fantasy author known for novels such as "Marseguro" and for hosting "The Worldshapers" podcast.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6b55248190ab938e69eb263612 |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b9418b08190a61ee4ec4283767c |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69cc60c488190a5a71e25c075e9ff |
completed | May 3, 2026, 12:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69d870a588190aa1444209085ed7e |
completed | May 3, 2026, 12:57 a.m. |
Created at: April 9, 2026, 5:30 p.m.