Triple
T6582787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenbachhaus |
E157339
|
entity |
| Predicate | operatedBy |
P86
|
FINISHED |
| Object | City of Munich |
E21335
|
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: City of Munich | Statement: [Lenbachhaus, operatedBy, City of Munich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Munich Context triple: [Lenbachhaus, operatedBy, City of Munich]
-
A.
Munich
chosen
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
B.
Stadt Nürnberg
Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
-
C.
Regensburg
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
-
D.
Augsburg
Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
-
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_69c6882b3a108190b3a9eb343ae4162c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae9238b481909278316ef195018d |
completed | March 27, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e42b72c481909f582f4f5b07e3d9 |
completed | March 27, 2026, 8:10 p.m. |
Created at: March 27, 2026, 1:54 p.m.