Triple
T5821153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landstraße |
E129110
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Landstraßer Hauptstraße
Landstraßer Hauptstraße is a major shopping and traffic artery in Vienna’s 3rd district, known for its mix of historic buildings, retail stores, and local services.
|
E550524
|
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: Landstraßer Hauptstraße | Statement: [Landstraße, contains, Landstraßer Hauptstraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Landstraßer Hauptstraße Context triple: [Landstraße, contains, Landstraßer Hauptstraße]
-
A.
Gerichtstraße
Gerichtstraße is a street in Berlin, Germany, located in the Wedding district and known for its mix of residential buildings, commercial spaces, and cultural venues.
-
B.
Siesmayerstraße
Siesmayerstraße is a street in Frankfurt am Main, Germany, known for bordering the historic Palmengarten botanical garden.
-
C.
Schwartzkopffstraße
Schwartzkopffstraße is a Berlin U-Bahn station on the U6 line located in the central district of the city.
-
D.
Schulstraße
Schulstraße is a nearby street in the vicinity of Leopoldplatz in Berlin, Germany.
-
E.
Scharnweberstraße
Scharnweberstraße is a station on Berlin’s U6 U-Bahn line serving the Reinickendorf district in the north of the city.
- 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: Landstraßer Hauptstraße Triple: [Landstraße, contains, Landstraßer Hauptstraße]
Generated description
Landstraßer Hauptstraße is a major shopping and traffic artery in Vienna’s 3rd district, known for its mix of historic buildings, retail stores, and local services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Landstraßer Hauptstraße Target entity description: Landstraßer Hauptstraße is a major shopping and traffic artery in Vienna’s 3rd district, known for its mix of historic buildings, retail stores, and local services.
-
A.
Gerichtstraße
Gerichtstraße is a street in Berlin, Germany, located in the Wedding district and known for its mix of residential buildings, commercial spaces, and cultural venues.
-
B.
Siesmayerstraße
Siesmayerstraße is a street in Frankfurt am Main, Germany, known for bordering the historic Palmengarten botanical garden.
-
C.
Schwartzkopffstraße
Schwartzkopffstraße is a Berlin U-Bahn station on the U6 line located in the central district of the city.
-
D.
Schulstraße
Schulstraße is a nearby street in the vicinity of Leopoldplatz in Berlin, Germany.
-
E.
Scharnweberstraße
Scharnweberstraße is a station on Berlin’s U6 U-Bahn line serving the Reinickendorf district in the north of the city.
- 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_69c0084869e881908d7859492183ca7b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c033e7403881908f5e3fe40183865a |
completed | March 22, 2026, 6:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a188e5c08190abbc283eff193761 |
completed | March 23, 2026, 2:12 a.m. |
| NEDg | Description generation | batch_69c0a210d9788190b3a40e8ec2f2c6b6 |
completed | March 23, 2026, 2:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0a2b1df0c8190b63d5ca984439b0b |
completed | March 23, 2026, 2:17 a.m. |
Created at: March 22, 2026, 3:53 p.m.