Triple
T5680427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Columbine |
E125184
|
entity |
| Predicate | relatedCharacterType |
P65727
|
FINISHED |
| Object | zanni |
—
|
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: zanni | Statement: [Columbine, relatedCharacterType, zanni]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedCharacterType Context triple: [Columbine, relatedCharacterType, zanni]
-
A.
relatedCharacter
Indicates that one character has a specified relationship or association with another character.
-
B.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
C.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
-
D.
associatedWithCharacterRole
Indicates that one entity has a connection or linkage to a specific character role played or held by another entity.
-
E.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
- F. None of above. chosen
Provenance (4 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_69c0082a884c8190a79001bae658941f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0248751bc8190b12aaa42d1ef17e3 |
completed | March 22, 2026, 5:19 p.m. |
| PD | Predicate disambiguation | batch_69c021be59088190a81c880957f666ab |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c024861bc88190a17782c1982fbb3e |
completed | March 22, 2026, 5:19 p.m. |
Created at: March 22, 2026, 3:44 p.m.