Triple
T12215275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toggenburg valley |
E291065
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Krummenau |
E973021
|
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: Krummenau | Statement: [Toggenburg valley, hasSettlement, Krummenau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krummenau Context triple: [Toggenburg valley, hasSettlement, Krummenau]
-
A.
Krummenau
chosen
Krummenau is a village in the Toggenburg region of the Swiss canton of St. Gallen, known for its rural alpine setting and traditional Swiss character.
-
B.
Weidenau
Weidenau is a district of the city of Siegen in North Rhine-Westphalia, Germany.
-
C.
Schwabhausen
Schwabhausen is a municipality in Bavaria, Germany, known for its rural character and location within the greater Munich metropolitan region.
-
D.
Schmargendorf
Schmargendorf is a residential locality in southwestern Berlin known for its quiet streets, historic buildings, and proximity to the Grunewald forest.
-
E.
Eichenau
Eichenau is a municipality in Upper Bavaria, Germany, known as a residential suburb west of Munich within the Fürstenfeldbruck district.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c931cec819083ca19be06a33e1c |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eac128408190a29b4f8a6e1240dd |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 8, 2026, 9:51 p.m.