Triple
T5902006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theodor Mommsen |
E131249
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Gardingen
Gardingen is a small locality in northern Germany best known as the birthplace of the renowned classical scholar and historian Theodor Mommsen.
|
E552932
|
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: Gardingen | Statement: [Theodor Mommsen, placeOfBirth, Gardingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gardingen Context triple: [Theodor Mommsen, placeOfBirth, Gardingen]
-
A.
Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
-
B.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
C.
Gard
Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
-
D.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
E.
Alsergrund
Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, 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: Gardingen Triple: [Theodor Mommsen, placeOfBirth, Gardingen]
Generated description
Gardingen is a small locality in northern Germany best known as the birthplace of the renowned classical scholar and historian Theodor Mommsen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gardingen Target entity description: Gardingen is a small locality in northern Germany best known as the birthplace of the renowned classical scholar and historian Theodor Mommsen.
-
A.
Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
-
B.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
C.
Gard
Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
-
D.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
E.
Alsergrund
Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, 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_69c0085864a88190a569c05ff7d65f29 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03735e6f8819084eded3b45f5e4ed |
completed | March 22, 2026, 6:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b16297588190928693a7c31ec0a7 |
completed | March 23, 2026, 3:20 a.m. |
| NEDg | Description generation | batch_69c0b1fabe448190be7d93b1f8c17c2a |
completed | March 23, 2026, 3:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0b29fbec8819092b117bd40e3731f |
completed | March 23, 2026, 3:25 a.m. |
Created at: March 22, 2026, 3:58 p.m.