Triple
T6767590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disloyal: A Memoir |
E154761
|
entity |
| Predicate | describesRoleOfSubject |
P11526
|
FINISHED |
| Object | personal attorney to Donald Trump |
—
|
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: personal attorney to Donald Trump | Statement: [Disloyal: A Memoir, describesRoleOfSubject, personal attorney to Donald Trump]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: describesRoleOfSubject Context triple: [Disloyal: A Memoir, describesRoleOfSubject, personal attorney to Donald Trump]
-
A.
describesRoleAs
chosen
Indicates that one entity specifies or characterizes the role or function played by another entity.
-
B.
roleInText
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
-
C.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
D.
depictsPersonRole
Indicates that an image or representation shows a person in a specific role, function, or capacity.
-
E.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
- 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_69c688109c1c8190added9a221292af0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d231b79c81908a4f7fa8f253706d |
completed | March 27, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69c6d094105881909c5806eb4afa6306 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:12 p.m.