Triple
T12088020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanny |
E287859
|
entity |
| Predicate | hasMeaningInEnglishSlang |
P103428
|
FINISHED |
| Object | vulgar slang term in some English dialects |
—
|
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: vulgar slang term in some English dialects | Statement: [Fanny, hasMeaningInEnglishSlang, vulgar slang term in some English dialects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMeaningInEnglishSlang Context triple: [Fanny, hasMeaningInEnglishSlang, vulgar slang term in some English dialects]
-
A.
hasMeaningViaJohn
Indicates that something possesses or conveys its meaning specifically through John as the interpretive or mediating agent.
-
B.
hasMultipleMeanings
Indicates that a term, symbol, or expression is associated with more than one distinct meaning or interpretation.
-
C.
commonMeaning
Indicates that multiple entities share the same or very similar meaning or semantic interpretation.
-
D.
isColloquialTerm
Indicates that one term is an informal or non-standard, colloquial way of referring to another term or concept.
-
E.
letterMeaning
Indicates that a particular letter conveys a specific meaning, interpretation, or semantic content.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9178ad99c8190a54777b9bbe998bc |
completed | April 10, 2026, 3:30 p.m. |
| PD | Predicate disambiguation | batch_69d915000454819089fee00022055599 |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d9178814e081908f67e3846718530e |
completed | April 10, 2026, 3:30 p.m. |
Created at: April 8, 2026, 9:48 p.m.