Triple
T3332846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steffl |
E70072
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Innere Stadt |
E69717
|
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: Innere Stadt | Statement: [Steffl, locatedIn, Innere Stadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Innere Stadt Context triple: [Steffl, locatedIn, Innere Stadt]
-
A.
Innere Stadt
chosen
Innere Stadt is the historic first district and city center of Vienna, Austria, known for its medieval street layout, grand boulevards, and concentration of major cultural and political landmarks.
-
B.
Innenstadt
Innenstadt is the central urban district of Frankfurt am Main, known as the city’s historic core and primary commercial area.
-
C.
Stadtmitte
Stadtmitte is the central urban district of the town of Bad Honnef in North Rhine-Westphalia, Germany.
-
D.
Stadtmitte
Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
-
E.
Fürther Innenstadt
Fürther Innenstadt is the central urban district of Fürth, Germany, known for its historic architecture, shopping streets, and role as the city’s cultural and commercial hub.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb194960081909333c855f06d8b03 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a867cac81909ddde955c1752ab8 |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.