Triple
T14120848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin Lima |
E339899
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Kevin Lima |
E339899
|
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 Lima | Statement: [Kevin Lima, name, Kevin Lima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Lima Context triple: [Kevin Lima, name, Kevin Lima]
-
A.
Kevin Lima
chosen
Kevin Lima is an American film director, animator, and screenwriter best known for his work on Disney animated features such as "A Goofy Movie," "Tarzan," and the live-action/animated film "Enchanted."
-
B.
Tony Almeida
Tony Almeida is a key fictional Counter Terrorist Unit agent in the television series "24," known for his complex loyalties and evolving role across multiple seasons.
-
C.
Carlos Lyra
Carlos Lyra is a Brazilian singer, guitarist, and composer recognized as one of the key pioneers and songwriters of the bossa nova genre.
-
D.
Daniel Lugo
Daniel Lugo is the ambitious, bodybuilding ringleader of the criminal scheme at the center of the dark comedy crime film "Pain & Gain."
-
E.
Brian Pimental
Brian Pimental is an American animator, storyboard artist, and director known for his work on several major Disney animated films, including contributing to the story of Beauty and the Beast.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de60942a588190beff0058a92f7051 |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf07feb48190b7519204b4f789b4 |
completed | May 7, 2026, 6:50 p.m. |
Created at: April 9, 2026, 10:22 p.m.