Triple
T2668527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dina Boluarte |
E55693
|
entity |
| Predicate | isFirstWomanToHoldOffice |
P4487
|
FINISHED |
| Object | President of Peru |
—
|
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: President of Peru | Statement: [Dina Boluarte, isFirstWomanToHoldOffice, President of Peru]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFirstWomanToHoldOffice Context triple: [Dina Boluarte, isFirstWomanToHoldOffice, President of Peru]
-
A.
isFirstFemaleHolderOfOffice
chosen
Indicates that a person is the first woman ever to hold a particular office or position.
-
B.
firstInOfficeTo
Indicates that one entity was the earliest or first to hold a particular office or position in relation to another entity or context.
-
C.
isFirstAfricanAmericanToHoldPosition
Indicates that a person is the first African American individual ever to occupy or serve in a specified position or role.
-
D.
servedAsFirstLadyOfTheUnitedStatesFrom
Indicates that a person held the role of First Lady of the United States during a specified time period.
-
E.
succeededAsFirstLadyOfTheUnitedStatesBy
Indicates that one person ceased serving as First Lady of the United States and was directly followed in that role by another specific person.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd98bacf48190b5633f1a3ec8f0cf |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd8190ad481908f3e14ac84d0940a |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.