Triple
T35623067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elderly Man River |
E1029370
|
entity |
| Predicate | hasHumorousCommentaryOn |
P43127
|
FINISHED |
| Object | censorship in entertainment |
—
|
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: censorship in entertainment | Statement: [Elderly Man River, hasHumorousCommentaryOn, censorship in entertainment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHumorousCommentaryOn Context triple: [Elderly Man River, hasHumorousCommentaryOn, censorship in entertainment]
-
A.
hasHumorousTreatmentOf
chosen
Indicates that one entity presents or portrays another entity in a humorous, comedic, or joking manner.
-
B.
hasCommentaryOn
Indicates that one entity provides commentary, explanation, or evaluative remarks about another entity.
-
C.
hasCommentaryIn
Indicates that an entity is discussed, analyzed, or annotated within a specific commentary work or source.
-
D.
hasHumorousSubplotActor
Indicates that an actor participates in or is responsible for a humorous subplot within a larger work.
-
E.
hasNotableSatire
Indicates that one entity is recognized for containing or exemplifying a significant satirical treatment of the other entity.
- 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_69f76e0709408190bbe322bf1707ef6b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:05 p.m.