Triple
T38052343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leinenkugel’s Maibock |
E949797
|
entity |
| Predicate | originStyleRegion |
P100363
|
FINISHED |
| Object | Germany |
E1728
|
NE 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: Germany | Statement: [Leinenkugel’s Maibock, originStyleRegion, Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originStyleRegion Context triple: [Leinenkugel’s Maibock, originStyleRegion, Germany]
-
A.
notableStyleRegion
Indicates that a particular style, manner, or artistic approach is especially characteristic of or prominent within a specific geographic region.
-
B.
regionFrom
chosen
Indicates that something originates from, is derived from, or is associated with a particular geographic or administrative region.
-
C.
originSubregion
Indicates the specific subregional area from which an entity originates or is derived.
-
D.
usesRegionalStyle
Indicates that one entity employs or applies a style, method, or convention characteristic of a particular geographic region in relation to another entity or context.
-
E.
hasSubregionStyle
Indicates a stylistic relationship in which one region exhibits a specific style that is characteristic of a subregion within it.
- F. None of above.
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_69f76f000cf081908c11fb5443b392e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a415d1c9f748190a87010559e1c7ad4 |
completed | June 28, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:20 p.m.