Triple
T12452196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knorr |
E297557
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Heilbronn, Germany |
E296707
|
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: Heilbronn, Germany | Statement: [Knorr, headquartersLocation, Heilbronn, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heilbronn, Germany Context triple: [Knorr, headquartersLocation, Heilbronn, Germany]
-
A.
Giessen, Germany
Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
-
B.
Neubiberg, Germany
Neubiberg, Germany is a municipality near Munich known as a hub for high-tech industry and research, including serving as the base for major semiconductor companies.
-
C.
Schröttinghausen, Germany
Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
-
D.
Heilbronn
chosen
Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
-
E.
Brühl, Germany
Brühl, Germany is a town in North Rhine-Westphalia known for its UNESCO-listed Augustusburg and Falkenlust palaces and its proximity to Cologne.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d9fa5f0819080ca9f6efa212c59 |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b9f4dd08190b1d62b03d68cc8a6 |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:56 p.m.