Triple
T4638544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unna |
E101592
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Kamen
Kamen is a town in North Rhine-Westphalia, Germany, known as a local industrial and transport hub in the Ruhr region.
|
E460596
|
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: Kamen | Statement: [Unna, locatedNear, Kamen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamen Context triple: [Unna, locatedNear, Kamen]
-
A.
Kamen
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
-
B.
Kato Nevrokopi
Kato Nevrokopi is a town in northern Greece known for its harsh winters and record-low temperatures, often considered one of the coldest inhabited places in the country.
-
C.
Kamiyama
Kamiyama is a Japanese surname borne by various individuals, including artists, athletes, and public figures.
-
D.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
E.
Kasada
Kasada is a traditional Hindu ritual and festival observed by the Tenggerese people of East Java, Indonesia, involving offerings cast into the crater of Mount Bromo.
- 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: Kamen Triple: [Unna, locatedNear, Kamen]
Generated description
Kamen is a town in North Rhine-Westphalia, Germany, known as a local industrial and transport hub in the Ruhr region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kamen Target entity description: Kamen is a town in North Rhine-Westphalia, Germany, known as a local industrial and transport hub in the Ruhr region.
-
A.
Kamen
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
-
B.
Kato Nevrokopi
Kato Nevrokopi is a town in northern Greece known for its harsh winters and record-low temperatures, often considered one of the coldest inhabited places in the country.
-
C.
Kamiyama
Kamiyama is a Japanese surname borne by various individuals, including artists, athletes, and public figures.
-
D.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
E.
Kasada
Kasada is a traditional Hindu ritual and festival observed by the Tenggerese people of East Java, Indonesia, involving offerings cast into the crater of Mount Bromo.
- 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_69bd43d3bc7c81908f81fcf380476b0f |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a64214481908a207e8070cc7a45 |
completed | March 20, 2026, 2:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be036d7aa081908b4b361dbae8ebc7 |
completed | March 21, 2026, 2:33 a.m. |
| NEDg | Description generation | batch_69be0542daf08190b792855c8129ac50 |
completed | March 21, 2026, 2:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be05c1dcd48190a08a5748e86a5ac8 |
completed | March 21, 2026, 2:43 a.m. |
Created at: March 20, 2026, 1:13 p.m.