Triple
T4866857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rainy Day Women #12 & 35 |
E108990
|
entity |
| Predicate | hasWordplay |
P37943
|
FINISHED |
| Object | pun on stoning and intoxication |
—
|
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: pun on stoning and intoxication | Statement: [Rainy Day Women #12 & 35, hasWordplay, pun on stoning and intoxication]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWordplay Context triple: [Rainy Day Women #12 & 35, hasWordplay, pun on stoning and intoxication]
-
A.
isPlayOnWordsWith
chosen
Indicates a relationship where one expression is a pun or wordplay that depends on, echoes, or cleverly twists the wording or meaning of another expression.
-
B.
spellingGimmick
Indicates a distinctive or unconventional way of spelling something used for effect or branding rather than standard orthography.
-
C.
hasRhymingSlangExample
Indicates that one entity serves as an example of rhyming slang associated with another entity.
-
D.
sharesSpellingWith
Indicates that two entities have identical or substantially identical written forms (i.e., they are spelled the same way).
-
E.
hasMeaningViaJohn
Indicates that something possesses or conveys its meaning specifically through John as the interpretive or mediating agent.
- 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_69bd440d96a48190b0c87069adef2af1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d7a42f88190bb1ef7261bcbc2a8 |
completed | March 20, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69bd6c27334481909ba8ac80854f7d8e |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:26 p.m.