Triple
T5904441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Dundreary |
E131306
|
entity |
| Predicate | isParodiedAs |
P10352
|
FINISHED |
| Object | bumbling peer |
—
|
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: bumbling peer | Statement: [Lord Dundreary, isParodiedAs, bumbling peer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isParodiedAs Context triple: [Lord Dundreary, isParodiedAs, bumbling peer]
-
A.
parodies
chosen
Indicates that one entity imitates another in an exaggerated or humorous way, often to criticize or comment on the original.
-
B.
hasHumorousTreatmentOf
Indicates that one entity presents or portrays another entity in a humorous, comedic, or joking manner.
-
C.
namedAfterFictionalCharacter
Indicates that one entity has been given its name in honor of, or derived from, a fictional character.
-
D.
wasPortrayedAs
Indicates that one entity has been depicted or represented in the form or role of another entity, typically within some medium or context.
-
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_69c0085864a88190a569c05ff7d65f29 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03ee10b308190afe38b904ae7c5f7 |
completed | March 22, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69c0334fcf6481908e8e74105de9d49b |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.