Triple
T32816331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carla Pestalozzi |
E839301
|
entity |
| Predicate | fiancéEmployer |
P175523
|
FINISHED |
| Object | Federal Bureau of Investigation |
—
|
NE NERFINISHED |
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: Federal Bureau of Investigation | Statement: [Carla Pestalozzi, fiancéEmployer, Federal Bureau of Investigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fiancéEmployer Context triple: [Carla Pestalozzi, fiancéEmployer, Federal Bureau of Investigation]
-
A.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
B.
namedForEmployer
Indicates that an entity is named after, or in honor of, its employer.
-
C.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
-
D.
isFianceeOf
Indicates that one person is the engaged-to-be-married partner of another person.
-
E.
employerSide
Indicates that the subject participates in or represents the employer’s position, interests, or perspective within an employment relationship or dispute.
- F. None of above. chosen
Provenance (4 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_69f3493df9008190a8f5d843dcd77704 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d210fc80819091ed8961aa2cddfb |
completed | May 3, 2026, 4:41 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d16b79dc8190ab0d4657f2ef9a5b |
completed | May 3, 2026, 4:39 a.m. |
Created at: May 1, 2026, 1:15 a.m.