Triple
T17929032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Othmarschen |
E448277
|
entity |
| Predicate | hasTransportConnection |
P845
|
FINISHED |
| Object |
Elbchaussee
Elbchaussee is a historic and scenic waterfront boulevard in Hamburg, Germany, known for its grand villas, parks, and views over the River Elbe.
|
E1368734
|
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: Elbchaussee | Statement: [Othmarschen, hasTransportConnection, Elbchaussee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elbchaussee Context triple: [Othmarschen, hasTransportConnection, Elbchaussee]
-
A.
Hedderichstraße
Hedderichstraße is a street in Frankfurt am Main, Germany, located in the Sachsenhausen district and connected to the city’s public transport network.
-
B.
Freßgass
Freßgass is a famous upscale shopping and dining street in central Frankfurt, known for its luxury boutiques, delicatessens, and vibrant café culture.
-
C.
Langenstraße
Langenstraße is a district or neighborhood within the town of Rüthen in North Rhine-Westphalia, Germany.
-
D.
Brehmstraße
Brehmstraße is a street in Düsseldorf, Germany, best known for lending its name to the historic ice hockey arena Eisstadion an der Brehmstraße.
-
E.
Chausseestraße
Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
- 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: Elbchaussee Triple: [Othmarschen, hasTransportConnection, Elbchaussee]
Generated description
Elbchaussee is a historic and scenic waterfront boulevard in Hamburg, Germany, known for its grand villas, parks, and views over the River Elbe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elbchaussee Target entity description: Elbchaussee is a historic and scenic waterfront boulevard in Hamburg, Germany, known for its grand villas, parks, and views over the River Elbe.
-
A.
Hedderichstraße
Hedderichstraße is a street in Frankfurt am Main, Germany, located in the Sachsenhausen district and connected to the city’s public transport network.
-
B.
Freßgass
Freßgass is a famous upscale shopping and dining street in central Frankfurt, known for its luxury boutiques, delicatessens, and vibrant café culture.
-
C.
Langenstraße
Langenstraße is a district or neighborhood within the town of Rüthen in North Rhine-Westphalia, Germany.
-
D.
Brehmstraße
Brehmstraße is a street in Düsseldorf, Germany, best known for lending its name to the historic ice hockey arena Eisstadion an der Brehmstraße.
-
E.
Chausseestraße
Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
- 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a5511a408190973cf5fa1f286a26 |
completed | April 19, 2026, 9:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0714355ec081909a1387b65749d627 |
completed | May 15, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_6a0714d428c88190b84f70042cf90312 |
completed | May 15, 2026, 12:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0715a8888081908d3009e19b1c7c2f |
completed | May 15, 2026, 12:46 p.m. |
Created at: April 10, 2026, 10:20 a.m.