Triple
T9151515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Starnberg |
E219595
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Gauting |
E372340
|
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: Gauting | Statement: [District of Starnberg, hasMunicipality, Gauting]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gauting Context triple: [District of Starnberg, hasMunicipality, Gauting]
-
A.
Gauting
chosen
Gauting is a municipality in the district of Starnberg in Bavaria, Germany, known for its residential character and proximity to Munich.
-
B.
Erding
Erding is a Bavarian town northeast of Munich, best known for its historic center, Erdinger Weißbräu brewery, and large thermal spa complex.
-
C.
Holzkirchen
Holzkirchen is a market town in Upper Bavaria, Germany, known as a regional transport hub on the railway line between Munich and the Alpine foothills.
-
D.
Forchheim
Forchheim is a town in Upper Franconia, Bavaria, Germany, known for its historic old town and location along major regional rail and road routes.
-
E.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96cf4548190a3a45172f0e9d0ec |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2997e60b081908c4982db477de5a1 |
completed | April 5, 2026, 5:18 p.m. |
Created at: March 30, 2026, 7:20 p.m.