Triple
T19887534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Oxenberg |
E477939
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Maya Van Dien |
—
|
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: Maya Van Dien | Statement: [Catherine Oxenberg, child, Maya Van Dien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maya Van Dien Context triple: [Catherine Oxenberg, child, Maya Van Dien]
-
A.
Maya Van Dien
chosen
Maya Van Dien is the daughter of American actress Catherine Oxenberg and actor Casper Van Dien.
-
B.
Celeste Van Dien
Celeste Van Dien is the daughter of American actress Catherine Oxenberg and actor Casper Van Dien.
-
C.
Daniela Ruah
Daniela Ruah is a Portuguese-American actress best known for playing Special Agent Kensi Blye on the television series "NCIS: Los Angeles."
-
D.
Alexandra Daddario
Alexandra Daddario is an American actress known for her roles in films such as the Percy Jackson series, San Andreas, and Baywatch, as well as the HBO series True Detective.
-
E.
Leslie Bega
Leslie Bega is an American actress best known for her roles on television series such as "Head of the Class" and "The Sopranos."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6590b12c08190bf44a3f3b2cb9122 |
completed | April 20, 2026, 4:49 p.m. |
Created at: April 10, 2026, 1:52 p.m.