Triple
T18365196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheinfelden (Aargau) |
E440023
|
entity |
| Predicate | hasOldTownCharacter |
P105746
|
FINISHED |
| Object | medieval street pattern |
—
|
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: medieval street pattern | Statement: [Rheinfelden (Aargau), hasOldTownCharacter, medieval street pattern]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOldTownCharacter Context triple: [Rheinfelden (Aargau), hasOldTownCharacter, medieval street pattern]
-
A.
oldTownCharacter
chosen
Indicates that something possesses the distinctive qualities or atmosphere typically associated with an old town.
-
B.
hasSmallTownCharacter
Indicates that something possesses the qualities or atmosphere typically associated with a small town, such as intimacy, familiarity, and a close-knit community feel.
-
C.
isInOldTownArea
Indicates that an entity is located within the designated old town area of a place.
-
D.
hasDowntownCharacteristic
Indicates that something possesses a feature, quality, or attribute typically associated with a downtown area.
-
E.
hasSuburbanCharacter
Indicates that something possesses qualities or features typically associated with suburban areas, such as lower density, residential focus, and car-oriented development.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5174d31608190851a5bab6878c203 |
completed | April 19, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69e44fed3fdc81908f4ed6a81db42416 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:38 a.m.