Triple
T3775466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaisermühlen |
E83295
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
UNO City
UNO City is a major United Nations complex in Vienna that hosts several UN organizations and international agencies.
|
E386465
|
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: UNO City | Statement: [Kaisermühlen, knownFor, UNO City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UNO City Context triple: [Kaisermühlen, knownFor, UNO City]
-
A.
Canon City
Canon City is a small city in central Colorado known for its historic downtown, proximity to the Royal Gorge, and outdoor recreation along the Arkansas River.
-
B.
STL City
STL City is a common shorthand name for the city of St. Louis, Missouri, particularly used in sports and local branding contexts.
-
C.
River City
River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
-
D.
River City
River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
-
E.
River City
River City is a popular nickname for Richmond, Virginia, highlighting the city's location along the James River and its historic riverfront character.
- 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: UNO City Triple: [Kaisermühlen, knownFor, UNO City]
Generated description
UNO City is a major United Nations complex in Vienna that hosts several UN organizations and international agencies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UNO City Target entity description: UNO City is a major United Nations complex in Vienna that hosts several UN organizations and international agencies.
-
A.
Canon City
Canon City is a small city in central Colorado known for its historic downtown, proximity to the Royal Gorge, and outdoor recreation along the Arkansas River.
-
B.
STL City
STL City is a common shorthand name for the city of St. Louis, Missouri, particularly used in sports and local branding contexts.
-
C.
River City
River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
-
D.
River City
River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
-
E.
River City
River City is a popular nickname for Richmond, Virginia, highlighting the city's location along the James River and its historic riverfront character.
- 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_69ad8b235e608190b5a2b1d1bfcef50b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc5ac9688190bc921cd3ba1d0580 |
completed | March 8, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e53209888190823412fabacbc914 |
completed | March 14, 2026, 4:33 a.m. |
| NEDg | Description generation | batch_69b4e60c23608190977198b6344ff09d |
completed | March 14, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4e686bf2c8190aac01d6c1014c1d4 |
completed | March 14, 2026, 4:39 a.m. |
Created at: March 8, 2026, 3:36 p.m.