Triple
T34083751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marries former escort Willa Ferreyra |
E874119
|
entity |
| Predicate | hasBridePortrayedBy |
P141904
|
FINISHED |
| Object | Justine Lupe |
—
|
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: Justine Lupe | Statement: [Marries former escort Willa Ferreyra, hasBridePortrayedBy, Justine Lupe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBridePortrayedBy Context triple: [Marries former escort Willa Ferreyra, hasBridePortrayedBy, Justine Lupe]
-
A.
fiancéePortrayedBy
chosen
Indicates that a character’s fiancée is depicted or played by a specific actor or performer.
-
B.
marriedToBeforeFameOf
Indicates that one person was married to another person before the latter became famous.
-
C.
isFianceeOf
Indicates that one person is the engaged-to-be-married partner of another person.
-
D.
bride
Indicates that an entity is a woman who is getting married or has just been married in relation to a wedding event or spouse.
-
E.
portrayedBySpouseOf
Indicates that something is portrayed or depicted by the spouse of a given entity.
- 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_69f349a61d448190b74642f325d3eb7a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 1, 2026, 1:52 a.m.