Triple
T32874020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Königinhof an der Elbe |
E840872
|
entity |
| Predicate | hasCzechToponym |
P114177
|
FINISHED |
| Object | Dvůr Králové nad Labem |
—
|
NE NERFINISHED |
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: Dvůr Králové nad Labem | Statement: [Königinhof an der Elbe, hasCzechToponym, Dvůr Králové nad Labem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCzechToponym Context triple: [Königinhof an der Elbe, hasCzechToponym, Dvůr Králové nad Labem]
-
A.
hasToponymicForm
Indicates that one entity is a toponymic (place-name-based) form or variant derived from another entity.
-
B.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
C.
hasTypeOfToponym
Indicates that one entity is classified as a specific type or category of toponym (place name) in relation to another entity.
-
D.
hasToponymy
Indicates a relationship where one entity possesses or is associated with the system, study, or set of place names (toponyms) of another entity.
-
E.
hasCanonicalToponym
chosen
Indicates that an entity is associated with its standard or officially recognized place name.
- 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_69f349436ee88190b72ee12d0f3f508e |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:18 a.m.