Triple
T18349109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nuremberg Exhibition Centre |
E439616
|
entity |
| Predicate | significantEvent |
P259
|
FINISHED |
| Object |
FeuerTrutz
FeuerTrutz is a leading German trade fair and conference focused on preventive fire protection, held annually in Nuremberg.
|
E1319588
|
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: FeuerTrutz | Statement: [Nuremberg Exhibition Centre, significantEvent, FeuerTrutz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FeuerTrutz Context triple: [Nuremberg Exhibition Centre, significantEvent, FeuerTrutz]
-
A.
Brennen
Brennen is a given name, typically used as a variant spelling of Brennan.
-
B.
Rhein Fire
Rhein Fire was a professional American football team based in Düsseldorf, Germany that competed in NFL Europe and was known for its strong fan support and multiple World Bowl appearances.
-
C.
Vuren
Vuren is a village and former municipality in the Dutch province of Gelderland, known for its location along the River Waal and its historic fortifications.
-
D.
Nebelschütz
Nebelschütz is a small municipality in eastern Saxony, Germany, known for its Sorbian cultural heritage and rural setting.
-
E.
Warmbrodt
Warmbrodt is a surname most notably associated with Sara Sothern, the American stage actress and mother of film star Elizabeth Taylor.
- 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: FeuerTrutz Triple: [Nuremberg Exhibition Centre, significantEvent, FeuerTrutz]
Generated description
FeuerTrutz is a leading German trade fair and conference focused on preventive fire protection, held annually in Nuremberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FeuerTrutz Target entity description: FeuerTrutz is a leading German trade fair and conference focused on preventive fire protection, held annually in Nuremberg.
-
A.
Brennen
Brennen is a given name, typically used as a variant spelling of Brennan.
-
B.
Rhein Fire
Rhein Fire was a professional American football team based in Düsseldorf, Germany that competed in NFL Europe and was known for its strong fan support and multiple World Bowl appearances.
-
C.
Vuren
Vuren is a village and former municipality in the Dutch province of Gelderland, known for its location along the River Waal and its historic fortifications.
-
D.
Nebelschütz
Nebelschütz is a small municipality in eastern Saxony, Germany, known for its Sorbian cultural heritage and rural setting.
-
E.
Warmbrodt
Warmbrodt is a surname most notably associated with Sara Sothern, the American stage actress and mother of film star Elizabeth Taylor.
- 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e514f66a4c8190a0ba53d3b349178a |
completed | April 19, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03cc28b80c8190819b989478bb62bd |
completed | May 13, 2026, 12:56 a.m. |
| NEDg | Description generation | batch_6a03cd1858c481909f78fb1a4c27437c |
completed | May 13, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03ce9e3dd08190b912fe9e9f552053 |
completed | May 13, 2026, 1:06 a.m. |
Created at: April 10, 2026, 10:37 a.m.