Triple
T38117293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Heckling Hare |
E951825
|
entity |
| Predicate | starsCharacterInEarlyRole |
P95803
|
FINISHED |
| Object | Bugs Bunny |
—
|
NE NERFINISHED |
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: Bugs Bunny | Statement: [The Heckling Hare, starsCharacterInEarlyRole, Bugs Bunny]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: starsCharacterInEarlyRole Context triple: [The Heckling Hare, starsCharacterInEarlyRole, Bugs Bunny]
-
A.
playedEarlyRoleIn
Indicates that one entity contributed significantly to the initial or formative stages of another entity’s development, success, or emergence.
-
B.
screenDebutInMajorRoleFor
Indicates that one entity made their first significant on-screen appearance (major role) in a particular production or work.
-
C.
workedAsChildActorUnderName
Indicates that a person performed child acting roles using a particular stage or professional name.
-
D.
startedCareerAsChildActor
Indicates that a person began their professional career in acting during childhood.
-
E.
featuresActorEarlyPerformance
chosen
Indicates that the work includes one of the actor’s earlier or formative performances in their career.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fccbd826708190b5fab12c4236299a |
completed | May 7, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69fcc58838e08190b8fa54aa5c165f2d |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:21 p.m.