Triple
T12191398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fallersleben |
E290470
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Stadt Wolfsburg |
E74139
|
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: Stadt Wolfsburg | Statement: [Fallersleben, partOf, Stadt Wolfsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadt Wolfsburg Context triple: [Fallersleben, partOf, Stadt Wolfsburg]
-
A.
Wolfsburg
chosen
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
B.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
-
C.
Herford
Herford is a historic town in northwestern Germany known for its medieval architecture and location in the region of North Rhine-Westphalia.
-
D.
Straußfurt
Straußfurt is a municipality in the German state of Thuringia, known for its rural setting and proximity to the Unstrut River.
-
E.
Braunschweig
Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c5340248190b79379423f3a3ca1 |
completed | April 10, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a8d24508190b0a60ca76d775b1e |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:50 p.m.