Triple
T2322022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Swiss Confederacy |
E48202
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Lucerne |
E31684
|
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: Lucerne | Statement: [Old Swiss Confederacy, hasPart, Lucerne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lucerne Context triple: [Old Swiss Confederacy, hasPart, Lucerne]
-
A.
Lucerne
chosen
Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
-
B.
Alsike
Alsike is a small locality in Uppsala County, Sweden, known as a growing residential community within Knivsta Municipality.
-
C.
Rhoose
Rhoose is a coastal village in the Vale of Glamorgan in South Wales, known for its proximity to Cardiff Airport and views over the Bristol Channel.
-
D.
Cynara
Cynara is a genus of thistle-like flowering plants best known for including the cultivated artichoke.
-
E.
Galega
Galega is a small genus of flowering legumes in the pea family, best known for the species Galega officinalis, historically used as a medicinal plant and as a forage crop.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc6337e948190bb4860f7045914e1 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae896b357c8190a6cdf99d5292037e |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.