Triple
T3798733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hotel Transylvania 2 |
E91635
|
entity |
| Predicate | voiceActor |
P1507
|
FINISHED |
| Object | Kevin James |
E216473
|
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: Kevin James | Statement: [Hotel Transylvania 2, voiceActor, Kevin James]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin James Context triple: [Hotel Transylvania 2, voiceActor, Kevin James]
-
A.
Kevin James
chosen
Kevin James is an American actor and comedian best known for starring in the sitcom "The King of Queens" and films such as "Paul Blart: Mall Cop."
-
B.
Chris Penn
Chris Penn was an American character actor known for his roles in films such as "Reservoir Dogs," "Footloose," and "True Romance."
-
C.
Matt LeBlanc
Matt LeBlanc is an American actor best known for playing the lovable, dim-witted Joey Tribbiani on the hit sitcom "Friends" and its spin-off "Joey."
-
D.
Vince Vaughn
Vince Vaughn is an American actor and comedian known for his roles in hit comedies such as "Wedding Crashers," "Dodgeball," and "Old School."
-
E.
Luke Wilson
Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
- 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_69aed96354f48190a768966d6bd19b04 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee7a270908190ac30537a22f3d500 |
completed | March 9, 2026, 3:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5282f253c81908c18a30bb1025f99 |
completed | March 14, 2026, 9:19 a.m. |
Created at: March 9, 2026, 3:15 p.m.