Triple
T9151522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Starnberg |
E219595
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Krailling |
E773040
|
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: Krailling | Statement: [District of Starnberg, hasMunicipality, Krailling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krailling Context triple: [District of Starnberg, hasMunicipality, Krailling]
-
A.
Krailling
chosen
Krailling is a municipality in the district of Starnberg in Bavaria, Germany, known as a residential community within the Munich metropolitan area.
-
B.
Vöcklabruck
Vöcklabruck is a small historic town in Upper Austria known as a regional center near the Attersee lake and the foothills of the Alps.
-
C.
Lustenau
Lustenau is a large market town in the Austrian state of Vorarlberg, known for its location on the Rhine near the Swiss border and its strong textile industry heritage.
-
D.
Leoben
Leoben is a historic industrial and university city in the Austrian state of Styria, known especially for its steel industry and mining university.
-
E.
Schärding
Schärding is a historic Austrian town on the border with Germany, known for its well-preserved baroque old town and riverside setting.
- 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_69d121eb1c9881908aebf9e1dab72457 |
completed | April 4, 2026, 2:36 p.m. |
Created at: March 30, 2026, 7:20 p.m.