Triple
T19846437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limburg-Weilburg |
E476870
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Weilmünster
Weilmünster is a municipality in the Limburg-Weilburg district of Hesse, Germany, known for its rural character and location in the Lahn valley region.
|
E1399242
|
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: Weilmünster | Statement: [Limburg-Weilburg, contains, Weilmünster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weilmünster Context triple: [Limburg-Weilburg, contains, Weilmünster]
-
A.
Altmünster
Altmünster is a market town in Upper Austria, situated on the shores of Lake Traunsee and known for its scenic Alpine surroundings.
-
B.
Altenmünster
Altenmünster is a municipality in the Swabian region of Bavaria, Germany, known for its rural character and location within the Augsburg district.
-
C.
Münnerstadt
Münnerstadt is a historic small town in northern Bavaria, Germany, known for its well-preserved medieval architecture and location in the spa region of Lower Franconia.
-
D.
Kirchlauter
Kirchlauter is a small municipality in northern Bavaria, Germany, known for its rural character and location within the Haßberge region.
-
E.
Kleinmünster
Kleinmünster is a small locality in the Haßberge district of northern Bavaria, Germany.
- 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: Weilmünster Triple: [Limburg-Weilburg, contains, Weilmünster]
Generated description
Weilmünster is a municipality in the Limburg-Weilburg district of Hesse, Germany, known for its rural character and location in the Lahn valley region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Weilmünster Target entity description: Weilmünster is a municipality in the Limburg-Weilburg district of Hesse, Germany, known for its rural character and location in the Lahn valley region.
-
A.
Altmünster
Altmünster is a market town in Upper Austria, situated on the shores of Lake Traunsee and known for its scenic Alpine surroundings.
-
B.
Altenmünster
Altenmünster is a municipality in the Swabian region of Bavaria, Germany, known for its rural character and location within the Augsburg district.
-
C.
Münnerstadt
Münnerstadt is a historic small town in northern Bavaria, Germany, known for its well-preserved medieval architecture and location in the spa region of Lower Franconia.
-
D.
Kirchlauter
Kirchlauter is a small municipality in northern Bavaria, Germany, known for its rural character and location within the Haßberge region.
-
E.
Kleinmünster
Kleinmünster is a small locality in the Haßberge district of northern Bavaria, Germany.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65809da2c8190bb579ef42513b74d |
completed | April 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07d43cfb388190b48f433c00026617 |
completed | May 16, 2026, 2:19 a.m. |
| NEDg | Description generation | batch_6a07d81e5bc881909b109afa7cf71384 |
completed | May 16, 2026, 2:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07d91c50648190a2a9f13daad545fd |
completed | May 16, 2026, 2:40 a.m. |
Created at: April 10, 2026, 1:51 p.m.