Triple
T16050726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U-Bahn line U9 |
E389344
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Amrumer Straße |
E1111953
|
NE FINISHED |
How this triple was built (2 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: Amrumer Straße | Statement: [U-Bahn line U9, hasStation, Amrumer Straße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amrumer Straße Context triple: [U-Bahn line U9, hasStation, Amrumer Straße]
-
A.
Amrumer Straße
chosen
Amrumer Straße is a Berlin U-Bahn station on the U9 line located in the Wedding district of the city.
-
B.
Siesmayerstraße
Siesmayerstraße is a street in Frankfurt am Main, Germany, known for bordering the historic Palmengarten botanical garden.
-
C.
Hedderichstraße
Hedderichstraße is a street in Frankfurt am Main, Germany, located in the Sachsenhausen district and connected to the city’s public transport network.
-
D.
Karmarschstraße
Karmarschstraße is a central shopping and traffic street in Hanover, Germany, running through the city center near Kröpcke square.
-
E.
Vorbergstraße
Vorbergstraße is a residential street located in the Akazienkiez neighborhood of Berlin’s Schöneberg district, known for its quiet, tree-lined character near the area’s lively cafés and shops.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18361c31481908b253e8b814ec9f6 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fb40dc08190b9d6a04f3c19f57d |
completed | May 11, 2026, 4:48 a.m. |
Created at: April 10, 2026, 4:56 a.m.