Triple
T20891782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rothsee |
E514424
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Allersberg |
—
|
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: Allersberg | Statement: [Rothsee, locatedNear, Allersberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allersberg Context triple: [Rothsee, locatedNear, Allersberg]
-
A.
Allersberg
chosen
Allersberg is a market town in the Roth district of Bavaria, Germany, known for its historic center and proximity to the city of Nuremberg.
-
B.
Ausserberg
Ausserberg is a small Swiss mountain village and municipality in the canton of Valais, known for its scenic alpine setting and traditional rural character.
-
C.
Eichberg
Eichberg is a municipality in the Swiss canton of St. Gallen, situated in the Rhine Valley region near the border with Austria.
-
D.
Wackersberg
Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
-
E.
Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4f7ebe48190952a85547a0f31a1 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6d05f0bec8190a296db546bd34114 |
completed | April 21, 2026, 1:18 a.m. |
Created at: April 16, 2026, 12:46 p.m.