Triple
T5271287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camille Keaton |
E119262
|
entity |
| Predicate | portraysCharacterType |
P49328
|
FINISHED |
| Object | revenge-seeking survivor |
—
|
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: revenge-seeking survivor | Statement: [Camille Keaton, portraysCharacterType, revenge-seeking survivor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysCharacterType Context triple: [Camille Keaton, portraysCharacterType, revenge-seeking survivor]
-
A.
depictsCharacterType
chosen
Indicates that one entity visually represents or portrays a character of a specified type or role.
-
B.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
C.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
D.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
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_69bd446c38e081908cdaf113bdf86790 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77c71268819094f9f5203eed392d |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:51 p.m.