Triple
T11883941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Rough and Ready |
E282727
|
entity |
| Predicate | notableBearerOffice |
P59478
|
FINISHED |
| Object | 12th 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: 12th president of the United States | Statement: [Old Rough and Ready, notableBearerOffice, 12th president of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableBearerOffice Context triple: [Old Rough and Ready, notableBearerOffice, 12th president of the United States]
-
A.
notableOfficer
Indicates that an entity has served as a significant or distinguished officer of another entity, such as an organization, institution, or group.
-
B.
notableFormerOfficeHolderRole
chosen
Indicates that an entity previously held a particular official position or role that is considered notable.
-
C.
notableBearerFullName
Indicates that a full personal name is that of a notable or well-known bearer associated with the referenced entity.
-
D.
notableOfficeHolder
Indicates that an entity is a significant or distinguished holder of a particular office or position associated with another entity.
-
E.
notableHolderOccupation
Indicates that a person notably associated with an entity (e.g., an award, office, or title) held a particular occupation or professional role.
- 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_69d6ab2a90b08190a4e818821cc93e6d |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d3a02ad4819090faef0e0be732ee |
completed | April 10, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69d8bb272f88819090c37c944c5a60ab |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:44 p.m.