Triple
T7181817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Dierdorf |
E167464
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Dierdorf
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
|
E681270
|
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: Dierdorf | Statement: [Dan Dierdorf, familyName, Dierdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dierdorf Context triple: [Dan Dierdorf, familyName, Dierdorf]
-
A.
Duisdorf
Duisdorf is a district of Bonn, Germany, known as a residential area with local commerce and public services within the borough of Hardtberg.
-
B.
Wermsdorf
Wermsdorf is a municipality in Saxony, Germany, best known as the site of the large Baroque hunting lodge and former royal residence Hubertusburg Palace.
-
C.
Neuendorf
Neuendorf is a small village on the Baltic Sea island of Hiddensee in Germany, known for its traditional thatched houses and maritime character.
-
D.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
E.
Neudorf
Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
- 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: Dierdorf Triple: [Dan Dierdorf, familyName, Dierdorf]
Generated description
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dierdorf Target entity description: Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
-
A.
Duisdorf
Duisdorf is a district of Bonn, Germany, known as a residential area with local commerce and public services within the borough of Hardtberg.
-
B.
Wermsdorf
Wermsdorf is a municipality in Saxony, Germany, best known as the site of the large Baroque hunting lodge and former royal residence Hubertusburg Palace.
-
C.
Neuendorf
Neuendorf is a small village on the Baltic Sea island of Hiddensee in Germany, known for its traditional thatched houses and maritime character.
-
D.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
E.
Neudorf
Neudorf is a residential district of Strasbourg, France, known for its dense urban fabric, local commerce, and proximity to the city center.
- 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_69c6888a7c548190a3d39b52a393080f |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8bc25088190a7d7f3ba2461b5e9 |
completed | March 27, 2026, 8:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8a1fd9ecc8190a6a136778422f264 |
completed | March 29, 2026, 3:52 a.m. |
| NEDg | Description generation | batch_69c8a3a3cc2081909a5a2041cbdbe04f |
completed | March 29, 2026, 3:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8a4257d9c8190a6b13bc9d5491476 |
completed | March 29, 2026, 4:01 a.m. |
Created at: March 27, 2026, 2:49 p.m.