Triple
T4419316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Kong (2005 film) |
E95057
|
entity |
| Predicate | portraysCharacterViaMotionCapture |
P37849
|
FINISHED |
| Object | Andy Serkis as King Kong |
—
|
LITERAL 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: Andy Serkis as King Kong | Statement: [King Kong (2005 film), portraysCharacterViaMotionCapture, Andy Serkis as King Kong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysCharacterViaMotionCapture Context triple: [King Kong (2005 film), portraysCharacterViaMotionCapture, Andy Serkis as King Kong]
-
A.
protagonistMotionCapture
Indicates that motion capture data is being recorded or applied specifically to the story’s protagonist character.
-
B.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
C.
portrayedVia
chosen
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
D.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
E.
portraysFictionalEntity
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
- F. None of above.
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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3551e7c6c819090fa5dfb5ac58e4c |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.