Triple
T19460910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meet the Robinsons |
E486864
|
entity |
| Predicate | voiceCastMember |
P9616
|
FINISHED |
| Object | Tom Selleck |
—
|
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: Tom Selleck | Statement: [Meet the Robinsons, voiceCastMember, Tom Selleck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Selleck Context triple: [Meet the Robinsons, voiceCastMember, Tom Selleck]
-
A.
Tom Selleck
chosen
Tom Selleck is an American actor best known for his starring role as private investigator Thomas Magnum in the television series "Magnum, P.I."
-
B.
Robert Davi
Robert Davi is an American actor, singer, and director best known for his tough-guy roles in films such as "Die Hard" and the James Bond movie "Licence to Kill."
-
C.
Richard Gere
Richard Gere is an American actor known for his leading roles in films such as "American Gigolo," "An Officer and a Gentleman," and "Pretty Woman."
-
D.
James Woods
James Woods is an American actor known for his intense performances in film and television, including acclaimed roles in movies such as "Salvador," "Videodrome," and "Casino."
-
E.
Eric S. Roberts
Eric S. Roberts is a prominent computer scientist and educator known for his influential work in computer science pedagogy, curriculum development, and widely used textbooks.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c983f481908b2684dc4380b889 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.