Triple
T10442101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hof district |
E246193
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Münchberg
Münchberg is a town in the Upper Franconia region of Bavaria, Germany, known historically for its textile industry and as a local commercial center.
|
E941217
|
NE FINISHED |
How this triple was built (4 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: Münchberg | Statement: [Hof district, contains, Münchberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Münchberg Context triple: [Hof district, contains, Münchberg]
-
A.
Meißenheim
Meißenheim is a small municipality in southwestern Germany’s Baden-Württemberg region, situated within the Ortenau district near the Rhine River.
-
B.
Tirschenreuth
Tirschenreuth is a town in northeastern Bavaria, Germany, known for its historic town center and surrounding lake and pond landscapes.
-
C.
Mitterfels
Mitterfels is a market town in the Straubing-Bogen district of Lower Bavaria, Germany, known for its historic castle and scenic location in the Bavarian Forest foothills.
-
D.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
E.
Löbau
Löbau is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and location in the Lusatian Highlands.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Münchberg Triple: [Hof district, contains, Münchberg]
Generated description
Münchberg is a town in the Upper Franconia region of Bavaria, Germany, known historically for its textile industry and as a local commercial center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Münchberg Target entity description: Münchberg is a town in the Upper Franconia region of Bavaria, Germany, known historically for its textile industry and as a local commercial center.
-
A.
Meißenheim
Meißenheim is a small municipality in southwestern Germany’s Baden-Württemberg region, situated within the Ortenau district near the Rhine River.
-
B.
Tirschenreuth
Tirschenreuth is a town in northeastern Bavaria, Germany, known for its historic town center and surrounding lake and pond landscapes.
-
C.
Mitterfels
Mitterfels is a market town in the Straubing-Bogen district of Lower Bavaria, Germany, known for its historic castle and scenic location in the Bavarian Forest foothills.
-
D.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
E.
Löbau
Löbau is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and location in the Lusatian Highlands.
- F. None of above. chosen
Provenance (5 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fb9ebf488190ae776bd65e94cb00 |
completed | April 7, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef8185c6e08190949020a80c24f2b8 |
completed | April 27, 2026, 3:32 p.m. |
| NEDg | Description generation | batch_69ef96ab29d48190b225504856007384 |
completed | April 27, 2026, 5:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69efd64bfa7081909715aa64d80fadf3 |
completed | April 27, 2026, 9:34 p.m. |
Created at: April 6, 2026, 12:15 p.m.