Triple
T26337906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judge William |
E662567
|
entity |
| Predicate | authorshipRole |
P134962
|
FINISHED |
| Object | author of the ethical viewpoint in Either/Or |
—
|
LITERAL FINISHED |
How this triple was built (2 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: author of the ethical viewpoint in Either/Or | Statement: [Judge William, authorshipRole, author of the ethical viewpoint in Either/Or]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: authorshipRole Context triple: [Judge William, authorshipRole, author of the ethical viewpoint in Either/Or]
-
A.
notableAuthorRole
Indicates that an entity holds a significant or distinguished role as an author in relation to another entity.
-
B.
hasContributorRoleOfAuthor
chosen
Indicates that an entity participates as a contributor specifically in the role of an author in relation to another entity.
-
C.
declaredRoleOfAuthor
Indicates that a specific role or capacity has been explicitly stated or assigned to an author in relation to a work or contribution.
-
D.
associatedWithAuthorRole
Indicates that an entity is connected to another entity specifically in the capacity or role of an author.
-
E.
coAuthorOccupation
Indicates that two or more co-authors share the same or closely related professional occupation.
- F. None of above.
Provenance (3 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_69ee81304194819092e20e0fae3aee07 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f69edbb7648190bd89c57e0932eac1 |
completed | May 3, 2026, 1:03 a.m. |
| PD | Predicate disambiguation | batch_69f69d17e8d48190b30bcc2f4bd81eb2 |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 26, 2026, 10:37 p.m.