Triple
T3564001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drop It Like It's Hot |
E75400
|
entity |
| Predicate | usesSlang |
P29199
|
FINISHED |
| Object | drop it like it's hot |
—
|
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: drop it like it's hot | Statement: [Drop It Like It's Hot, usesSlang, drop it like it's hot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesSlang Context triple: [Drop It Like It's Hot, usesSlang, drop it like it's hot]
-
A.
hasColloquialVariety
Indicates that one linguistic form, expression, or variety is an informal, colloquial counterpart or version of another.
-
B.
linguisticUsage
chosen
Indicates how a linguistic form, expression, or construction is used in language, such as its typical context, function, or register.
-
C.
isColloquialTerm
Indicates that one term is an informal or non-standard, colloquial way of referring to another term or concept.
-
D.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
-
E.
usesInclusiveLanguage
Indicates that the subject communicates in a way that avoids biased, exclusionary, or discriminatory language toward any group.
- 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_69ad85d512708190829c8b2d3a2ccfb8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0a76c008190a2056b6d990776fe |
completed | March 8, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69adb834779081908468e182d5f6cf02 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:21 p.m.