Triple
T21799489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnny "Drama" Chase |
E538197
|
entity |
| Predicate | hasPetWithinFiction |
P134676
|
FINISHED |
| Object | turtle (pet in some storylines) |
—
|
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: turtle (pet in some storylines) | Statement: [Johnny "Drama" Chase, hasPetWithinFiction, turtle (pet in some storylines)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPetWithinFiction Context triple: [Johnny "Drama" Chase, hasPetWithinFiction, turtle (pet in some storylines)]
-
A.
hasFictionalPet
chosen
Indicates that an entity has, owns, or is associated with a pet that is fictional or imaginary.
-
B.
hasFictionalAgriculturalCharacter
Indicates that an entity features or includes a character associated with agriculture within a fictional context.
-
C.
hasChildInFiction
Indicates that a fictional work or character includes another character as their child within the fictional narrative.
-
D.
hasAnimal
Indicates that one entity possesses, keeps, or is associated with an animal.
-
E.
hasNotableFictionalBearer
Indicates that an entity is associated with at least one well-known fictional character that bears its name or designation.
- 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_69e0c4733f4081909a86622e7e6d15d2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f077fd59048190ae4e50f4aee919e7 |
completed | April 28, 2026, 9:03 a.m. |
| PD | Predicate disambiguation | batch_69e6be751ce881909badced245ef76c7 |
completed | April 21, 2026, 12:01 a.m. |
Created at: April 16, 2026, 6:53 p.m.