Triple
T3904553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NFL Europe |
E90573
|
entity |
| Predicate | hadFormerTeam |
P15460
|
FINISHED |
| Object |
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.
|
E397174
|
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: Rhein Fire | Statement: [NFL Europe, hadFormerTeam, Rhein Fire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rhein Fire Context triple: [NFL Europe, hadFormerTeam, Rhein Fire]
-
A.
Brennen
Brennen is a given name, typically used as a variant spelling of Brennan.
-
B.
Bad Ems
Bad Ems is a historic spa town in western Germany, renowned for its mineral springs and picturesque location along the Lahn River.
-
C.
The Fire
The Fire is a Major League Soccer club based in Chicago, Illinois, known formally as Chicago Fire FC.
-
D.
Bucksturm
Bucksturm is a historic medieval tower in Osnabrück, Germany, known for its former use as a city fortification and prison.
-
E.
The Burning
The Burning is a 1981 American slasher film, notable for its summer-camp setting and early special effects work by makeup artist Tom Savini.
- 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: Rhein Fire Triple: [NFL Europe, hadFormerTeam, Rhein Fire]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rhein Fire Target entity description: 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.
-
A.
Brennen
Brennen is a given name, typically used as a variant spelling of Brennan.
-
B.
Bad Ems
Bad Ems is a historic spa town in western Germany, renowned for its mineral springs and picturesque location along the Lahn River.
-
C.
The Fire
The Fire is a Major League Soccer club based in Chicago, Illinois, known formally as Chicago Fire FC.
-
D.
Bucksturm
Bucksturm is a historic medieval tower in Osnabrück, Germany, known for its former use as a city fortification and prison.
-
E.
The Burning
The Burning is a 1981 American slasher film, notable for its summer-camp setting and early special effects work by makeup artist Tom Savini.
- 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef90e5f408190abf8353e153d1558 |
completed | March 9, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51ca9d32881908538065ee71b31c2 |
completed | March 14, 2026, 8:30 a.m. |
| NEDg | Description generation | batch_69b51d8510c08190a88a1a8f044b3c59 |
completed | March 14, 2026, 8:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b51e08c7ac8190aab4b270258cdb69 |
completed | March 14, 2026, 8:36 a.m. |
Created at: March 9, 2026, 3:22 p.m.