Triple
T3966024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S-Bahn Nuremberg |
E92218
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object |
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
|
E402766
|
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: Hartmannshof | Statement: [S-Bahn Nuremberg, serves, Hartmannshof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hartmannshof Context triple: [S-Bahn Nuremberg, serves, Hartmannshof]
-
A.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
B.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
C.
Heidenfeld
Heidenfeld is a village in Bavaria, Germany, known as the birthplace of Cardinal Michael von Faulhaber.
-
D.
Erbdrostenhof
Erbdrostenhof is an 18th-century Baroque palace in Münster, Germany, renowned for its ornate architecture and historical significance.
-
E.
Steinfeld
Steinfeld is the surname of American actress and singer Hailee Steinfeld, known for her roles in films like "True Grit" and "Pitch Perfect 2" and for her pop music career.
- 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: Hartmannshof Triple: [S-Bahn Nuremberg, serves, Hartmannshof]
Generated description
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hartmannshof Target entity description: Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
A.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
B.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
C.
Heidenfeld
Heidenfeld is a village in Bavaria, Germany, known as the birthplace of Cardinal Michael von Faulhaber.
-
D.
Erbdrostenhof
Erbdrostenhof is an 18th-century Baroque palace in Münster, Germany, renowned for its ornate architecture and historical significance.
-
E.
Steinfeld
Steinfeld is the surname of American actress and singer Hailee Steinfeld, known for her roles in films like "True Grit" and "Pitch Perfect 2" and for her pop music career.
- 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_69aed96624188190ac8c45bb57ab72b5 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef976f4fc8190b2c16ab62c19cdb8 |
completed | March 9, 2026, 4:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b533be4a688190a7d011ae2858e6ed |
completed | March 14, 2026, 10:09 a.m. |
| NEDg | Description generation | batch_69b537cc86e88190bae10e740d8c3ec7 |
completed | March 14, 2026, 10:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b538595d2481908812ab03cdb94659 |
completed | March 14, 2026, 10:28 a.m. |
Created at: March 9, 2026, 3:32 p.m.