Triple
T20332295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hilzingen |
E492513
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Binningen
Binningen is a small village in the municipality of Hilzingen in the district of Konstanz, in the state of Baden-Württemberg in southwestern Germany.
|
E1451516
|
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: Binningen | Statement: [Hilzingen, hasSubdivision, Binningen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Binningen Context triple: [Hilzingen, hasSubdivision, Binningen]
-
A.
Binningen
Binningen is a municipality in the canton of Basel-Landschaft in northwestern Switzerland, located just southwest of the city of Basel.
-
B.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
-
C.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
D.
Grenchen
Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
-
E.
Neuenegg
Neuenegg is a Swiss municipality in the canton of Bern, known for its rural character and location near the city of Bern.
- 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: Binningen Triple: [Hilzingen, hasSubdivision, Binningen]
Generated description
Binningen is a small village in the municipality of Hilzingen in the district of Konstanz, in the state of Baden-Württemberg in southwestern Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Binningen Target entity description: Binningen is a small village in the municipality of Hilzingen in the district of Konstanz, in the state of Baden-Württemberg in southwestern Germany.
-
A.
Binningen
Binningen is a municipality in the canton of Basel-Landschaft in northwestern Switzerland, located just southwest of the city of Basel.
-
B.
Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
-
C.
Bönigen
Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
-
D.
Grenchen
Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
-
E.
Neuenegg
Neuenegg is a Swiss municipality in the canton of Bern, known for its rural character and location near the city of Bern.
- 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_69e0b4a1a09881908d97270d6971a25a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e677e886a08190952b828fedd2a411 |
completed | April 20, 2026, 7 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08f8a248248190b739bffba2d40afb |
completed | May 16, 2026, 11:07 p.m. |
| NEDg | Description generation | batch_6a08fd01f8008190948668995b3616e0 |
completed | May 16, 2026, 11:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08fd71000c8190b72164d00be3be4d |
completed | May 16, 2026, 11:27 p.m. |
Created at: April 16, 2026, 11:22 a.m.