Triple
T34083741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marries former escort Willa Ferreyra |
E874119
|
entity |
| Predicate | involvesPastProfession |
P107616
|
FINISHED |
| Object | escort |
—
|
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: escort | Statement: [Marries former escort Willa Ferreyra, involvesPastProfession, escort]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesPastProfession Context triple: [Marries former escort Willa Ferreyra, involvesPastProfession, escort]
-
A.
hasPastOccupation
chosen
Indicates that an entity previously held a particular job, role, or occupation in the past.
-
B.
involvedOccupationOf
Indicates that an entity participates in or is associated with a particular occupation or professional role.
-
C.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
D.
economicRolePast
Indicates that an entity previously held a specific economic function, position, or role in the past.
-
E.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
- 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_69fd864235b481908738dbb69556bc62 |
completed | May 8, 2026, 6:44 a.m. |
| PD | Predicate disambiguation | batch_69fd8373b6bc819091c554f29ee17fec |
completed | May 8, 2026, 6:32 a.m. |
Created at: May 1, 2026, 1:52 a.m.