Triple
T5072672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Butterbeer |
E114316
|
entity |
| Predicate | realWorldVariants |
P4680
|
FINISHED |
| Object | cold Butterbeer |
—
|
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: cold Butterbeer | Statement: [Butterbeer, realWorldVariants, cold Butterbeer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: realWorldVariants Context triple: [Butterbeer, realWorldVariants, cold Butterbeer]
-
A.
variant
chosen
Indicates that one entity is an alternative form, version, or variation of another entity.
-
B.
usedInVariant
Indicates that something (such as a component, feature, or element) is utilized or included within a particular variant or version of a larger entity.
-
C.
variantCount
Indicates the number of distinct variants associated with a given entity or item.
-
D.
affectsVariant
Indicates that one entity has an influence or impact on a specific variant or version of another entity.
-
E.
viewVariesAmong
Indicates that the way something is viewed, perceived, or interpreted differs across multiple entities or contexts.
- 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_69bd443cf28c8190ad371d603563dbdd |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74cfa4348190bc50590117a6bcf9 |
completed | March 20, 2026, 4:24 p.m. |
| PD | Predicate disambiguation | batch_69bd7157fe608190b4515d56fdd0a616 |
completed | March 20, 2026, 4:10 p.m. |
Created at: March 20, 2026, 1:39 p.m.