Triple
T16144791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rapenburg |
E391750
|
entity |
| Predicate | hasDutchName |
P744
|
FINISHED |
| Object |
Rapenburg
Rapenburg is a historic canal and street in the city center of Leiden, Netherlands, known for its picturesque architecture and academic significance.
|
E1195867
|
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: Rapenburg | Statement: [Rapenburg, hasDutchName, Rapenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rapenburg Context triple: [Rapenburg, hasDutchName, Rapenburg]
-
A.
Operngasse
Operngasse is a street in central Vienna, Austria, running near major landmarks such as the Vienna State Opera and Karlsplatz.
-
B.
Königsplatz
Königsplatz is a historic square in Berlin that played a significant role in the city’s political and cultural life, especially during the 19th and early 20th centuries.
-
C.
Königsplatz
Königsplatz is a central square in the Bavarian city of Fürth, Germany, known as an important local transport and urban hub.
-
D.
Königsplatz
Königsplatz is a historic square in Munich, Germany, renowned for its neoclassical architecture and prominent museums forming part of the city’s cultural district.
-
E.
Ernsgaden
Ernsgaden is a small Bavarian municipality located in the district of Pfaffenhofen an der Ilm in southern Germany.
- 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: Rapenburg Triple: [Rapenburg, hasDutchName, Rapenburg]
Generated description
Rapenburg is a historic canal and street in the city center of Leiden, Netherlands, known for its picturesque architecture and academic significance.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rapenburg Target entity description: Rapenburg is a historic canal and street in the city center of Leiden, Netherlands, known for its picturesque architecture and academic significance.
-
A.
Operngasse
Operngasse is a street in central Vienna, Austria, running near major landmarks such as the Vienna State Opera and Karlsplatz.
-
B.
Königsplatz
Königsplatz is a central square in the Bavarian city of Fürth, Germany, known as an important local transport and urban hub.
-
C.
Königsplatz
Königsplatz is a historic square in Berlin that played a significant role in the city’s political and cultural life, especially during the 19th and early 20th centuries.
-
D.
Königsplatz
Königsplatz is a historic square in Munich, Germany, renowned for its neoclassical architecture and prominent museums forming part of the city’s cultural district.
-
E.
Ernsgaden
Ernsgaden is a small Bavarian municipality located in the district of Pfaffenhofen an der Ilm in southern Germany.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d92b0408190a010bd8e5193aa36 |
completed | April 17, 2026, 11:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff2b9322c8190a773681679f9ad79 |
completed | May 10, 2026, 2:51 a.m. |
| NEDg | Description generation | batch_69fff39371648190b3f694df00ff4f3e |
completed | May 10, 2026, 2:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff47a52cc8190b0ad7c37ea444159 |
completed | May 10, 2026, 2:59 a.m. |
Created at: April 10, 2026, 5:01 a.m.