Triple
T19846434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limburg-Weilburg |
E476870
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Merenberg
Merenberg is a small town in the Limburg-Weilburg district of the German state of Hesse.
|
E1399239
|
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: Merenberg | Statement: [Limburg-Weilburg, contains, Merenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Merenberg Context triple: [Limburg-Weilburg, contains, Merenberg]
-
A.
Moorenweis
Moorenweis is a rural municipality in Upper Bavaria, Germany, known for its agricultural landscape and small-village character.
-
B.
Huldenberg
Huldenberg is a rural municipality in the Flemish Brabant province of Belgium, known for its hilly landscape, forests, and residential villages near Brussels.
-
C.
Lippinghausen
Lippinghausen is a locality within the municipality of Hiddenhausen in the German state of North Rhine-Westphalia.
-
D.
Merzen
Merzen is a rural municipality in Lower Saxony, Germany, known for its agricultural character and location within the Osnabrück region.
-
E.
Mahlberg
Mahlberg is a small town and municipality in the Ortenau district of Baden-Württemberg in southwestern 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: Merenberg Triple: [Limburg-Weilburg, contains, Merenberg]
Generated description
Merenberg is a small town in the Limburg-Weilburg district of the German state of Hesse.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Merenberg Target entity description: Merenberg is a small town in the Limburg-Weilburg district of the German state of Hesse.
-
A.
Moorenweis
Moorenweis is a rural municipality in Upper Bavaria, Germany, known for its agricultural landscape and small-village character.
-
B.
Huldenberg
Huldenberg is a rural municipality in the Flemish Brabant province of Belgium, known for its hilly landscape, forests, and residential villages near Brussels.
-
C.
Lippinghausen
Lippinghausen is a locality within the municipality of Hiddenhausen in the German state of North Rhine-Westphalia.
-
D.
Merzen
Merzen is a rural municipality in Lower Saxony, Germany, known for its agricultural character and location within the Osnabrück region.
-
E.
Mahlberg
Mahlberg is a small town and municipality in the Ortenau district of Baden-Württemberg in southwestern 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.