Triple
T31912232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Lightning |
E814716
|
entity |
| Predicate | containsSlangTermForMoonshine |
P146004
|
FINISHED |
| Object | white lightning |
—
|
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: white lightning | Statement: [White Lightning, containsSlangTermForMoonshine, white lightning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsSlangTermForMoonshine Context triple: [White Lightning, containsSlangTermForMoonshine, white lightning]
-
A.
hasSlang
chosen
Indicates that one entity is an informal, colloquial, or slang term referring to the other entity.
-
B.
usesSlangFrom
Indicates that one entity incorporates or employs slang expressions originating from another entity or source.
-
C.
hasMeaningInEnglishSlang
Indicates that something (such as a word, phrase, or expression) carries a particular meaning specifically within English slang usage.
-
D.
hasRhymingSlangExample
Indicates that one entity serves as an example of rhyming slang associated with another entity.
-
E.
hasLiqueur
Indicates that one entity contains, includes, or is accompanied by a liqueur.
- 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_69f348f109d88190b5005372c53d2fcd |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ffa9677be08190852c8ef6c2545fed |
completed | May 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69ffa6570e2c8190a9d7b37f12b91d9a |
completed | May 9, 2026, 9:25 p.m. |
Created at: May 1, 2026, 12:01 a.m.