Triple
T14372970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krug |
E356400
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object | Krug Collection |
E356400
|
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: Krug Collection | Statement: [Krug, notableProduct, Krug Collection]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krug Collection Context triple: [Krug, notableProduct, Krug Collection]
-
A.
Krug
Krug is the traditional Cossack communal assembly that served as their highest decision-making and self-governing body.
-
B.
Krug
chosen
Krug is a prestigious Champagne house from France renowned for its luxury cuvées and craftsmanship, and is part of the LVMH portfolio of high-end wine and spirits brands.
-
C.
Al Krug
Al Krug is a fictional character appearing in the work "Poor Ophelia."
-
D.
Krug and Kellog series
The Krug and Kellog series is a collection of mystery novels featuring the investigative duo Krug and Kellog.
-
E.
Hennessy
Hennessy is a surname most prominently associated with John L. Hennessy, a renowned computer scientist and former president of Stanford University.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9007184c8190aebb003cb6548cc8 |
completed | April 14, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a36382481909a39ba5e51084051 |
completed | May 8, 2026, 5:52 a.m. |
Created at: April 10, 2026, 1:15 a.m.