Triple
T15296593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Van Dyke |
E365675
|
entity |
| Predicate | notableRelative |
P367
|
FINISHED |
| Object | Wes Van Dyke |
E949778
|
NE 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: Wes Van Dyke | Statement: [Christian Van Dyke, notableRelative, Wes Van Dyke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wes Van Dyke Context triple: [Christian Van Dyke, notableRelative, Wes Van Dyke]
-
A.
Wes Van Dyke
chosen
Wes Van Dyke is an American actor and member of the Van Dyke entertainment family, known for his appearances in film and television.
-
B.
Marc Wydell
Marc Wydell is a central character in the animated film "Ron's Gone Wrong," depicted as a socially awkward middle-schooler whose malfunctioning robot friend helps him navigate friendship and growing up in a hyper-connected digital world.
-
C.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
D.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
E.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e036848c1881908fbaaae0216d6d27 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00baf8a2648190adf3ad3af118187f |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 3:15 a.m.