Triple
T17897159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Road Runner |
E447458
|
entity |
| Predicate | definingGag |
P94189
|
FINISHED |
| Object | Coyote’s traps backfire |
—
|
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: Coyote’s traps backfire | Statement: [Road Runner, definingGag, Coyote’s traps backfire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: definingGag Context triple: [Road Runner, definingGag, Coyote’s traps backfire]
-
A.
notableGag
Indicates that something features a particularly memorable or significant joke, comedic moment, or running gag.
-
B.
hasRecurringGag
chosen
Indicates that a particular joke, situation, or comedic element repeatedly appears in relation to an entity (such as a character, series, or work).
-
C.
gimmick
Indicates that an entity uses or features a novel, attention-grabbing trick or device primarily intended to attract interest rather than provide substantive value.
-
D.
usedForHumor
Indicates that something is employed with the intention of being funny, amusing, or comical.
-
E.
hasIronicMeaning
Indicates that something conveys a meaning opposite to or incongruent with its literal expression, creating an ironic effect.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d8045748190a4e8c4684439a96b |
completed | April 19, 2026, 9:16 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.