Triple
T32513738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krusty the Clown Show |
E831003
|
entity |
| Predicate | fictionalHostProfession |
P34569
|
FINISHED |
| Object | clown |
—
|
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: clown | Statement: [Krusty the Clown Show, fictionalHostProfession, clown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalHostProfession Context triple: [Krusty the Clown Show, fictionalHostProfession, clown]
-
A.
fictionalOccupation
chosen
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
fictionalHostPersonality
Indicates that a person is characterized by a distinctive, often stylized or exaggerated, on-screen or on-stage host persona within a fictional context.
-
C.
fictionalProfessionSpecialty
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
-
D.
isFictionalCharacterHost
Indicates that one entity serves as the host or presenter in a context involving a fictional character.
-
E.
portrayedByProfession
Indicates that an entity is depicted or represented by someone acting in a specified professional capacity.
- 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_69f3492318348190ba37fb6b5f1d67f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fdd5fba5048190b7d430ae2054a1fd |
completed | May 8, 2026, 12:24 p.m. |
| PD | Predicate disambiguation | batch_69fdd35f76f88190a1854ea27132f9c7 |
completed | May 8, 2026, 12:13 p.m. |
Created at: May 1, 2026, 1 a.m.