Triple
T5392813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balm bei Günsberg |
E120373
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object | Hubersdorf |
E185445
|
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: Hubersdorf | Statement: [Balm bei Günsberg, hasNeighboringMunicipality, Hubersdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hubersdorf Context triple: [Balm bei Günsberg, hasNeighboringMunicipality, Hubersdorf]
-
A.
Hubersdorf
chosen
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
B.
Heinersdorf
Heinersdorf is a residential locality in the borough of Pankow in Berlin, Germany, known for its suburban character and proximity to the city center.
-
C.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
D.
Tussenhausen
Tussenhausen is a municipality in the district of Unterallgäu in Bavaria, Germany, known for its rural character and small villages such as Mattsies.
-
E.
Hermsdorf
Hermsdorf is a residential locality in the Berlin borough of Reinickendorf, known for its green surroundings and village-like character on the city’s northern edge.
- 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_69bd46354c648190a38b26f107010a96 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd871b81d08190993928e2c6251226 |
completed | March 20, 2026, 5:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfecee7e0c8190bb22c6102bda46f5 |
completed | March 22, 2026, 1:21 p.m. |
Created at: March 20, 2026, 2:04 p.m.