Triple
T8797636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taunusstein |
E209327
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Bleidenstadt
Bleidenstadt is a district of the town of Taunusstein in the Rheingau-Taunus region of Hesse, Germany, known for its historic church and small-town character.
|
E795416
|
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: Bleidenstadt | Statement: [Taunusstein, hasSubdivision, Bleidenstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bleidenstadt Context triple: [Taunusstein, hasSubdivision, Bleidenstadt]
-
A.
Eberhardzell
Eberhardzell is a rural municipality in the district of Biberach in the German state of Baden-Württemberg.
-
B.
Steinlach
Steinlach is a small river in the German state of Baden-Württemberg that flows through the city of Tübingen before joining the Neckar.
-
C.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
D.
Siegsdorf
Siegsdorf is a Bavarian town in southeastern Germany known for its scenic Alpine surroundings and proximity to the Chiemsee lake.
-
E.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
- 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: Bleidenstadt Triple: [Taunusstein, hasSubdivision, Bleidenstadt]
Generated description
Bleidenstadt is a district of the town of Taunusstein in the Rheingau-Taunus region of Hesse, Germany, known for its historic church and small-town character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bleidenstadt Target entity description: Bleidenstadt is a district of the town of Taunusstein in the Rheingau-Taunus region of Hesse, Germany, known for its historic church and small-town character.
-
A.
Eberhardzell
Eberhardzell is a rural municipality in the district of Biberach in the German state of Baden-Württemberg.
-
B.
Steinlach
Steinlach is a small river in the German state of Baden-Württemberg that flows through the city of Tübingen before joining the Neckar.
-
C.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
D.
Siegsdorf
Siegsdorf is a Bavarian town in southeastern Germany known for its scenic Alpine surroundings and proximity to the Chiemsee lake.
-
E.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
- 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_69ca836240888190a62b262e56a69d2f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fa370d08190885ef65e3a3e56d3 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100a931988190aaff56f16057ea90 |
completed | April 4, 2026, 12:14 p.m. |
| NEDg | Description generation | batch_69d10144380481909ef3f68f621c0be7 |
completed | April 4, 2026, 12:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1019faa188190a8945d469a763207 |
completed | April 4, 2026, 12:18 p.m. |
Created at: March 30, 2026, 6:44 p.m.