Triple
T2533900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cockney |
E56224
|
entity |
| Predicate | hasRhymingSlangExample |
P40929
|
FINISHED |
| Object | "apples and pears" meaning "stairs" |
—
|
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: "apples and pears" meaning "stairs" | Statement: [Cockney, hasRhymingSlangExample, "apples and pears" meaning "stairs"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRhymingSlangExample Context triple: [Cockney, hasRhymingSlangExample, "apples and pears" meaning "stairs"]
-
A.
hasColloquialVariety
Indicates that one linguistic form, expression, or variety is an informal, colloquial counterpart or version of another.
-
B.
hasTraditionalRhyme
Indicates that something is associated with or characterized by a conventional or culturally established rhyme.
-
C.
isColloquialTerm
Indicates that one term is an informal or non-standard, colloquial way of referring to another term or concept.
-
D.
refrainWord
Indicates that one entity avoids using, mentioning, or expressing a particular word or term in relation to another entity or context.
-
E.
usedPhrase
Indicates that one entity employed or expressed a particular phrase in speech, writing, or another form of communication.
- F. None of above. chosen
Provenance (4 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd64a2194819097c66cbeb37fe859 |
completed | March 7, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69abd0c4a5dc819097812db50443420a |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd648487881908ce8ca22def77294 |
completed | March 7, 2026, 7:39 a.m. |
Created at: March 6, 2026, 9:47 p.m.