Triple
T34083750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marries former escort Willa Ferreyra |
E874119
|
entity |
| Predicate | hasGroomPortrayedBy |
P110499
|
FINISHED |
| Object | Alan Ruck |
—
|
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: Alan Ruck | Statement: [Marries former escort Willa Ferreyra, hasGroomPortrayedBy, Alan Ruck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGroomPortrayedBy Context triple: [Marries former escort Willa Ferreyra, hasGroomPortrayedBy, Alan Ruck]
-
A.
portrayedBySpouseOf
Indicates that something is portrayed or depicted by the spouse of a given entity.
-
B.
portrayedBy
Indicates that one entity serves as the actor or performer who represents or plays the role of another entity in a work or medium.
-
C.
hasGroom
chosen
Indicates that an entity has a groom, i.e., is associated with a male partner in a marriage or wedding relationship.
-
D.
isRomanticLeadOf
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
-
E.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
- 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_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 1, 2026, 1:52 a.m.