Triple
T267873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metropolregion Mitteldeutschland |
E5771
|
entity |
| Predicate | hasSeat |
P3522
|
FINISHED |
| Object | Leipzig |
E38199
|
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: Leipzig | Statement: [Metropolregion Mitteldeutschland, hasSeat, Leipzig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leipzig Context triple: [Metropolregion Mitteldeutschland, hasSeat, Leipzig]
-
A.
Leipzig
chosen
Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
-
B.
Dresden
Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
-
C.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
-
D.
Rostock
Rostock is a historic Hanseatic city in northern Germany known for its significant seaport on the Baltic Sea and its long maritime and trading tradition.
-
E.
Lübeck
Lübeck is a historic Hanseatic city in northern Germany renowned for its medieval architecture and long-standing role as a key trading hub on the Baltic Sea.
- 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_69a2587daeb081909591b9d30f80a271 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dae4a0c8190a66cf6ed3889851c |
completed | Feb. 28, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7cf473e48819095390a5904429a9c |
completed | March 4, 2026, 6:20 a.m. |
Created at: Feb. 28, 2026, 2:56 a.m.