Triple
T9172302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ozaukee County |
E220108
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Thiensville
Thiensville is a small village in southeastern Wisconsin, known as a suburban community within the Milwaukee metropolitan area.
|
E781633
|
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: Thiensville | Statement: [Ozaukee County, contains, Thiensville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thiensville Context triple: [Ozaukee County, contains, Thiensville]
-
A.
Velten
Velten is a small town in the German state of Brandenburg, known historically for its stove and ceramics industry and its location just northwest of Berlin.
-
B.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
-
C.
Belleville
Belleville is a vibrant, historically working-class neighborhood in northeastern Paris known for its multicultural character, street art, and lively food scene.
-
D.
Belleville
Belleville is a small village in south-central Wisconsin, known for its rural character and proximity to the Madison metropolitan area.
-
E.
Belleville
Belleville is a small Canadian city in southeastern Ontario, known as a regional service and commercial hub on the Bay of Quinte.
- 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: Thiensville Triple: [Ozaukee County, contains, Thiensville]
Generated description
Thiensville is a small village in southeastern Wisconsin, known as a suburban community within the Milwaukee metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thiensville Target entity description: Thiensville is a small village in southeastern Wisconsin, known as a suburban community within the Milwaukee metropolitan area.
-
A.
Velten
Velten is a small town in the German state of Brandenburg, known historically for its stove and ceramics industry and its location just northwest of Berlin.
-
B.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
-
C.
Belleville
Belleville is a vibrant, historically working-class neighborhood in northeastern Paris known for its multicultural character, street art, and lively food scene.
-
D.
Belleville
Belleville is a small village in south-central Wisconsin, known for its rural character and proximity to the Madison metropolitan area.
-
E.
Belleville
Belleville is a small Canadian city in southeastern Ontario, known as a regional service and commercial hub on the Bay of Quinte.
- 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_69ca83e467108190abcae6a33b3d4dad |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccbf9fe79c819082f335c2fdd1c7d3 |
completed | April 1, 2026, 6:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0549d81d88190bea0995ad0016437 |
completed | April 4, 2026, midnight |
| NEDg | Description generation | batch_69d0553c2b58819086a651863f884064 |
completed | April 4, 2026, 12:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0562e41bc8190801c75e962600df2 |
completed | April 4, 2026, 12:07 a.m. |
Created at: March 30, 2026, 7:22 p.m.