Triple
T7736501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Jackson (designate) |
E175394
|
entity |
| Predicate | positionDesignated |
P13202
|
FINISHED |
| Object | First Lady 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: First Lady of the United States | Statement: [Rachel Jackson (designate), positionDesignated, First Lady of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionDesignated Context triple: [Rachel Jackson (designate), positionDesignated, First Lady of the United States]
-
A.
sideDesignation
Indicates how an entity is classified or labeled with respect to a particular side, position, or alignment within a relationship or context.
-
B.
designedByPosition
Indicates that something is created or planned by an entity identified by a particular role or job position, rather than by a specific individual.
-
C.
politicalDesignation
chosen
Indicates that an entity is assigned or associated with a specific political status, role, or classification.
-
D.
positionEstablishedIn
Indicates the point in time or event when a particular position, role, or office was formally created or instituted.
-
E.
positionUse
Indicates how a particular position or role is utilized or functionally applied within a given context.
- 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_69c6995f9c60819092e386192bd63c6f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70402169481909b219dc5f4a64b9b |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c7016c4a748190a7012030edaefcee |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:06 p.m.