Triple
T12917294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Tölz-Wolfratshausen |
E309018
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Dietramszell |
E848374
|
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: Dietramszell | Statement: [Bad Tölz-Wolfratshausen, hasPart, Dietramszell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dietramszell Context triple: [Bad Tölz-Wolfratshausen, hasPart, Dietramszell]
-
A.
Dietramszell
chosen
Dietramszell is a rural Bavarian municipality in southern Germany, known for its scenic countryside and historic monastery complex.
-
B.
Marlenheim
Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
-
C.
Eberhardzell
Eberhardzell is a rural municipality in the district of Biberach in the German state of Baden-Württemberg.
-
D.
Deisenhausen
Deisenhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
-
E.
Balzhausen
Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a1e8088190af697629baecf59f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a2796cc81908b6d4cf71f39e88a |
completed | May 8, 2026, 5:52 a.m. |
Created at: April 9, 2026, 5:41 p.m.