Triple
T7795078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roosevelt dime |
E180277
|
entity |
| Predicate | portraitSubjectRole |
P17608
|
FINISHED |
| Object | 32nd President of the United States |
—
|
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: 32nd President of the United States | Statement: [Roosevelt dime, portraitSubjectRole, 32nd President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraitSubjectRole Context triple: [Roosevelt dime, portraitSubjectRole, 32nd President of the United States]
-
A.
portraitSpecialization
Indicates that one entity specializes in creating or working with portraits, distinguishing a focused area of expertise within a broader artistic or professional domain.
-
B.
depictedSubject
Indicates that one entity visually represents or portrays another entity as its subject in an image or depiction.
-
C.
portraitArtist
Indicates that one entity is the artist who created a portrait depicting the other entity.
-
D.
speakerRole
Indicates the functional role or capacity in which an entity is acting as a speaker within a communicative event.
-
E.
depictsPersonRole
chosen
Indicates that an image or representation shows a person in a specific role, function, or capacity.
- 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:31 p.m.