Triple
T20669682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werner Lorant |
E507986
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
LR Ahlen
LR Ahlen is a German football club that has competed in the country’s professional league system, including the 2. Bundesliga.
|
E1444739
|
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: LR Ahlen | Statement: [Werner Lorant, employer, LR Ahlen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LR Ahlen Context triple: [Werner Lorant, employer, LR Ahlen]
-
A.
Lohse
Lohse is a German surname borne by various notable individuals in fields such as science, sports, and the arts.
-
B.
Anschutz
Anschutz is a notable American surname most prominently associated with the wealthy business and philanthropic family led by billionaire Philip Anschutz.
-
C.
Nodell
Nodell is a surname most notably associated with Martin Nodell, the American comic book artist who co-created the original Green Lantern for DC Comics.
-
D.
Eisele
Eisele is a surname most notably associated with Donn F. Eisele, an American astronaut who flew on the Apollo 7 mission.
-
E.
Griese
Griese is a surname most prominently associated with Bob Griese, the Hall of Fame American football quarterback.
- 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: LR Ahlen Triple: [Werner Lorant, employer, LR Ahlen]
Generated description
LR Ahlen is a German football club that has competed in the country’s professional league system, including the 2. Bundesliga.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LR Ahlen Target entity description: LR Ahlen is a German football club that has competed in the country’s professional league system, including the 2. Bundesliga.
-
A.
Lohse
Lohse is a German surname borne by various notable individuals in fields such as science, sports, and the arts.
-
B.
Anschutz
Anschutz is a notable American surname most prominently associated with the wealthy business and philanthropic family led by billionaire Philip Anschutz.
-
C.
Nodell
Nodell is a surname most notably associated with Martin Nodell, the American comic book artist who co-created the original Green Lantern for DC Comics.
-
D.
Eisele
Eisele is a surname most notably associated with Donn F. Eisele, an American astronaut who flew on the Apollo 7 mission.
-
E.
Griese
Griese is a surname most prominently associated with Bob Griese, the Hall of Fame American football quarterback.
- 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5c735048190a01cb7692928d66e |
completed | April 20, 2026, 11:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08cd64aa4081908cf843a32e99ae01 |
completed | May 16, 2026, 8:02 p.m. |
| NEDg | Description generation | batch_6a08d175eefc8190a5178c0f70f7d79f |
completed | May 16, 2026, 8:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08d23e8ac48190be1726c8e4913b1a |
completed | May 16, 2026, 8:23 p.m. |
Created at: April 16, 2026, 11:44 a.m.